exponential parameter estimation in the presence of known components and noise in the determination of the natural modes of an electromagnetic scatterer the measured time series will contain desired information noise and quite often known transient components introduced by the excitation source or measuring equipment this paper describes a linearly constrained total least squares lctls prony method for extracting the exponential model parameters from observed transient data for such problems the tls criterion yields better parameter estimates than ls moreover the incorporation of known signal information via constraints leads to even greater improvements in performance mathematical connections between lctls prony and a tls variation of time series deflation tsd are used to derive constraints for higher order excitation poles also we use tsd concepts to derive numerically superior data transformations for lctls simulation studies involving idealized test data and synthetic scattering response data of a perfectly conducting sphere demonstrate the advantages of the method

statistical analysis of tls based prony techniques abstract we present an analysis of parameter variance statistics for the tls prony method applied to damped exponential signals we derive the covariance matrix of the estimated parameters for this method the parameters include the magnitudes and angles of the poles and the magnitudes and angles of the amplitude coefficients we verify the theoretical results using monte carlo simulations studies we also compare the variance results to the corresponding cramer rao bounds for several cases

analysis on function duplicating capabilities of fuzzy controllers abstract fuzzy controllers have been successfully applied to many cases to which conventional control algorithms are difficult to be applied even though the representations and the processings of data and information in the fuzzy controller are quite different from other control algorithms the information processing operation that it carries out is basically a function f a rn rm from a bounded subset a of n dimensional euclidean space to a bounded subset f a of m dimensional euclidean space where n and m are the number of measured states and the number of control inputs of controlled system respectively under the assumptions of mamdani s direct reasoning method and c o g center of gravity defuzzification method the fuzzy controllers are proven to perform the mappings of any given functions with appropriately defined fuzzy sets the mapping capabilities of the fuzzy controllers are analyzed in detail for two cases f r1 r1 and g r2 r1 also it will be shown that the results can be extended to multiple dimensional cases

multiple emitter location and signal parameter estimation processing the signals received on an array of sensors for the location of the emitter is of great enough interest to have been treated under many special case assumptions the general problem considers sensors with arbitrary locations and arbitrary directional characteristics gain phase polarization in a noise interference environment of arbitrary covariance matrix this report is concerned first with the multiple emitter aspect of this problem and second with the generality of solution a description is given of the multiple signal classification music algorithm which provides asymptotically unbiased estimates of 1 number of incident wavefronts present 2 directions of arrival doa or emitter locations 3 strengths and cross correlations among the incident waveforms 4 noise interference strength examples and comparisons with methods based on maximum likelihood ml and maximum entropy me as well as conventional beamforming are included an example of its use as a multiple frequency estimator operating on time series is included

a unified approach to three eigendecomposition methods for frequency estimation the authors present a unified approach to three eigendecomposition based methods for frequency estimation in the presence of noise these are the tufts kumaresan tk method the minimum norm mn method and the total least squares tls method it is shown that 1 the mn method is a modified version of the tk method 2 the tls method is a generalization of the mn method 3 the tls solution vector can be expressed in matrix form and an alternative way of computing it is presented 4 the mn and the tls methods exhibit some improvement over the tk method

grouped coordinate ascent algorithms for penalized likelihood transmission image reconstruction presents a new class of algorithms for penalized likelihood reconstruction of attenuation maps from low count transmission scans we derive the algorithms by applying to the transmission log likelihood a version of the convexity technique developed by de pierro for emission tomography the new class includes the single coordinate ascent sca algorithm and lange s convex algorithm for transmission tomography as special cases the new grouped coordinate ascent gca algorithms in the class overcome several limitations associated with previous algorithms 1 fewer exponentiations are required than in the transmission maximum likelihood expectation maximization ml em algorithm or in the sca algorithm 2 the algorithms intrinsically accommodate nonnegativity constraints unlike many gradient based methods 3 the algorithms are easily parallelizable unlike the sca algorithm and perhaps line search algorithms we show that the gca algorithms converge faster than the sca algorithm even on conventional workstations an example from a low count positron emission tomography pet transmission scan illustrates the method

detection and localization of multiple sources in noise with unknown covariance the authors present a novel technique for the detection and localization of multiple sources in the presence of noise with unknown and arbitrary covariance the technique is applicable to coherent and noncoherent signals and to arbitrary array geometry and is based on rissanen s minimum description length mdl principle 1989 for model selection its computational load is comparable to that of analogous techniques for white noise simulation results demonstrating the performance of this technique are included

a fast recursive total least squares algorithm for adaptive fir filtering this paper proposes a new fast recursive total least squares n rtls algorithm to recursively compute the tls solution for adaptive finite impulse response fir filtering the n rtls algorithm is based on the minimization of the constrained rayleigh quotient c rq in which the last entry of the parameter vector is constrained to the negative one as analysis results on the convergence of the proposed algorithm we study the properties of the stationary points of the c rq the high computational efficiency of the new algorithm depends on the efficient computation of the fast gain vector fgv and the adaptation of the c rq since the last entry of the parameter vector in the c rq has been fixed as the negative one a minimum point of the c rq is searched only along the input data vector and a more efficient n rtls algorithm is obtained by using the fgv as compared with davila s rtls algorithms the n rtls algorithm saves the 6m number of multiplies divides and square roots mads the global convergence of the new algorithm is studied by lasalle s invariance principle the performances of the relevant algorithms are compared via simulations and the long term numerical stability of the n rtls algorithm is verified

fast recursive low rank linear prediction frequency estimation algorithms a class of fast recursive low rank linear prediction algorithms for the tracking of time varying frequencies of multiple nonstationary sinusoids in noise is introduced realizations with o nn and o nn sup 2 arithmetic operations per time step are described where n is the model order and n is the number of independent sinusoids the key step towards an operations count that depends only linearly on the model order is fast eigensubspace tracking and the property that the coefficients of a high order n prediction filter itself constitute a perfectly or almost perfectly predictable sequence that can be annihilated using a low order 2n prediction error filter that carries the desired signal frequency information in its roots in this concept root tracking is limited to a low order filter polynomial even if the overmodeling factor n n is much larger than 1 for optimal noise suppression extraneous roots are not computed explicitly detailed simulation results confirm the tracking capabilities of the new algorithms

detection of coherent signals using weighted subspace smoothing a new approach to detecting the number of coherent signals incident upon a uniformly spaced linear array is presented the approach combines a modified spatial smoothing scheme and a modified mdl criterion the modified spatial smoothing scheme referred to as weighted subspace smoothing is actually a generalization of the post smoothing approach proposed by krim and proakis in 1994 it is shown that the noise eigenvalues obtained with weighted subspace smoothing are more accurate than those obtained with the original spatial smoothing we thus attempt to improve the detection performance using these more accurate eigenvalues a novel modification of the minimum description length mdl criterion is proposed to accomplish this computer simulations are presented to study the performance improvement of this new approach

pseudo randomly generated estimator banks a new tool for improving the threshold performance of direction finding a new powerful tool for improving the threshold performance of direction finding is considered the main idea of our approach is to reduce the number of outliers in the doa estimates using a previously proposed joint estimation strategy jes for this purpose multiple different doa estimators are calculated in a parallel manner for the same batch of data i e for a single data record employing these estimators simultaneously the jes improves the threshold performance because it removes outliers and exploits only successful estimators that are sorted out using a hypothesis testing procedure we consider an efficient modification of the jes with application to the pseudo randomly generated eigenstructure estimator banks based on secondand higher order statistics weighted music estimators based on the covariance and contracted quadricovariance matrices are chosen as appropriate underlying techniques for the second and fourth order estimator banks respectively computer simulations with uncorrelated sources verify the dramatic improvements of threshold performance as compared with the conventional second and fourth order music algorithms simulations also show that in the second order case the threshold performance of our technique is close to that of the wsf method and stochastic deterministic ml methods which are known today as the most powerful in the sense of estimation performance and at the same time as the most computationally expensive doa estimation techniques the computational cost of our algorithm is much lower than that of the wsf and ml techniques because no multidimensional optimization is required

on the zeros of the linear prediction error filter for deterministic signals the zeros of a linear prediction error filter polynomial for a class of deterministic signals that are a sum of samples of m exponentially damped undamped sinusoids is studied it is assumed that n samples are available for processing and that they are uncorrupted by noise it is shown that the exponent parameters of the m signals can be determined from m zeros called signal zeros of an lth degree prediction error filter polynomial l m if l lies in between m and n m n m 2 in a special case the rest of the l m zeros of the filter polynomial switch are called extraneous zeros are shown to be approximately uniformly distributed with in the unit circle regardless of the type of exponentials in the signal if the prediction filter coefficients are chosen to have minimum euclidean length the results obtained provide insight into the estimation problem with noisy data

an algorithm for pole zero system model order estimation in system identification estimates of the unknown system model orders are often required an algorithm for estimating model orders is described that looks at input output data covariance matrix eigenvectors when model orders are overestimated zeros appear in the noise subspace eigenvectors the number of zeros present can be used to estimate model orders

estimating the parameters of exponentially damped sinusoids and pole zero modeling in noise we have presented techniques 1 6 based on linear prediction lp and singular value decomposition svd for accurate estimation of closely spaced frequencies of sinusoidal signals in noise in this note we extend these techniques to estimate the parameters of exponentially damped sinusoidal signals in noise the estimation procedure presented here makes use of backward prediction in addition to svd first the method is applied to data consisting of one and two exponentially damped sinusoids the choice of one and two signal components facilitates the comparison of estimation error in pole damping factors and pole frequencies to the appropriate cramer rao cr bounds and to traditional methods of linear prediction second our method is applied to an example due to steiglitz 8 in which the data consists of noisy values of the impulse response samples composed of many exponentially damped sinusoids of a linear system having both poles and zeros the poles of the system are accurately determined by our method and the zeros are obtained subsequently using shanks method

on unique localization of multiple sources by passive sensor arrays necessary and sufficient conditions are derived for the unique localization of narrowband sources having the same known center frequency by passive sensor arrays the conditions specify the maximum number of sources that can be uniquely localized by a general array that satisfies some mild geometric constraints the conditions are expressed in terms of the number of sensors and the rank of the correlation matrix of the sources two different conditions are presented the first guarantees uniqueness for every batch of sampled data the second which is weaker guarantees uniqueness for almost every batch of sampled data with the exception of a set of batches of measure zero it is shown that a condition that is slightly weaker than the second one is also necessary

maximum likelihood array processing in spatially correlated noise fields using parameterized signals this paper deals with the problem of estimating signal parameters using an array of sensors this problem is of interest in a variety of applications such as radar and sonar source localization a vast number of estimation techniques have been proposed in the literature during the past two decades most of these can deliver consistent estimates only if the covariance matrix of the background noise is known in many applications the aforementioned assumption is unrealistic recently a number of contributions have addressed the problem of signal parameter estimation in unknown noise environments based on various assumptions on the noise herein a different approach is taken we assume instead that the signals are partially known the received signals are modeled as linear combinations of certain known basis functions the exact maximum likelihood ml estimator for the problem at hand is derived as well as computationally more attractive approximation the cramer rao lower bound crb on the estimation error variance is also derived and found to coincide with the crb assuming an arbitrary deterministic model and known noise covariance

formulation and solution of structured total least norm problems for parameter estimation the total least squares tls method is a generalization of the least squares ls method for solving overdetermined sets of linear equations ax spl ap b the tls method minimizes spl par e r spl par sub f where r b a e x so that b r spl isin range a e given a spl isin c sup m spl times n with m spl ges n and b spl isin c sup m spl times 1 the most common tls algorithm is based on the singular value decomposition svd of a b however the svd based methods may not be appropriate when the matrix a has a special structure since they do not preserve the structure previously a new problem formulation known as structured total least norm stln and the algorithm for computing the stln solution have been developed the stln method preserves the special structure of a or a b and can minimize the error in the discrete l sub p norm where p 1 2 or spl infin in this paper the stln problem formulation is generalized for computing the solution of stln problems with multiple right hand sides ax spl ap b it is shown that these problems can be converted to ordinary stln problems with one right hand side in addition the method is shown to converge to the optimal solution in certain model reduction problems furthermore the application of the stln method to various parameter estimation problems is studied in which the computed correction matrix applied to a or a b keeps the same toeplitz structure as the data matrix a of a b respectively in particular the l sub 2 norm stln method is compared with the ls and tls methods in deconvolution transfer function modeling and linear prediction problems

the constrained total least squares technique and its applications to harmonic superresolution the constrained total least squares ctls method is a natural extension of tls to the case when the noise components of the coefficients are algebraically related the ctls technique is developed and some of its applications to superresolution harmonic analysis are presented the ctls problem is reduced to an unconstrained minimization problem over a small set of variables a perturbation analysis of the ctls solution is derived and from it the root mean square error rmse of the ctls solution which is valid for small noise levels is obtained in closed form the complex version of the newton method is derived and applied to determine the ctls solution it is also shown that the ctls problem is equivalent to a constrained parameter maximum likelihood problem the ctls technique is applied to estimate the frequencies of sinusoids in white noise and the angle of arrival of narrowband wavefronts at a linear uniform array in both cases the ctls method shows superior or similar accuracy to other advanced techniques

two dimensional prony modeling and parameter estimation a new method for estimating the two dimensional 2d exponential modes and amplitude coefficients in a prony model is presented this method involves two parts each utilizing a 1d singular value decomposition based technique and is capable of locating frequencies anywhere in the 2d frequency plane simulations are shown which demonstrate the performance of the algorithm

obstacle avoidance for a mobile robot a neuro fuzzy approach in this paper a neuro fuzzy approach is presented in order to guide a mobile robot this task could be carried out specifying a set of fuzzy rules taking into account the different situations found by the mobile robot this set of fuzzy rules could be optimised according to different criteria however the approach shown in this paper is able to extract a set of fuzzy rules set from a set of trajectories provided by a human these trajectories guide the mobile robot towards the target in different cases thus it has been possible to obtain the rules and membership functions automatically whereas other approaches need a previous definition of the rules and membership functions in order to verify that the obtained behaviour is satisfactory the neuro fuzzy approach has been implemented in two mobile robots

exact and approximate maximum likelihood parameter estimation for quasi synchronous cdma signals in order to construct the optimal decorrelator for a quasi synchronous qs code division multiple access receiver it is necessary to estimate the user delays however in a qs system it is shown that delay estimation is equivalent to estimating a parameter vector which is related linearly to the received signal an exact maximum likelihood ml solution using alternating maximization is first developed for joint estimation of the user delay vectors and amplitudes suboptimal recursive least squares rls and least mean square algorithms are then presented which approximate the true ml solution it is shown that the optimal decorrelator and demodulated symbols are also generated by the rls algorithm simulation and analytical results are presented for the bit error rate performance of the adaptive receiver and the average squared error of the estimated delay vectors is compared with the cramer rao bound

the cramer rao bound for pole and amplitude coefficient estimates of damped exponential signals in noise a complete cramer rao bound crb derivation is provided for the case in which signals consist of arbitrary exponential terms in noise expressions for the crbs of the parameters of a damped exponential model with one set of poles and multiple sets of amplitude coefficients are derived crbs for the poles and amplitude coefficients are derived in terms of rectangular and polar coordinate parameters for rectangular parameters it is shown that crbs for the real and imaginary parts of poles and amplitude coefficients are equal and uncorrelated in polar coordinates the angle and magnitude crbs are also uncorrelated furthermore the crbs of the pole angles and relative magnitudes are equal and are logarithmically symmetric about the unit circle

svd based information theoretic criteria for detection of the number of damped undamped sinusoids and their performance analysis following the information theoretic approach to model selection the authors develop criteria for detection of the number of damped undamped sinusoids these criteria are matched to the singular value decomposition svd based methods such as modified forward backward and forward backward linear prediction so well that the extra computations needed over and above those required for computing the svd are marginal next an analytical framework for analyzing the performance of these criteria is developed in the development of the analysis some approximations which become better for large signal to noise ratio are made simulations are used to verify the usefulness of the analysis and to compare the performance of the method with that of j j fuchs 1988

esprit based coherent source localization with forward and backward vectors novel methods are presented which estimate the direction of arrival doa of coherent signals by efficiently exploiting forward and backward vectors some correlation vectors or the eigenvector corresponding to the largest eigenvalue of the covariance matrix is used as the forward vector and the backward vector is then obtained by reversing the order of the complex conjugated elements of the forward one the use of both forward and backward vectors allows us to find a signal subspace of larger dimension than in the existing method which obtains that from a toeplitz matrix the proposed methods with an increased effective array aperture resolve more signals and provide better doa estimates than the toeplitz based method

perturbation theory for orthogonal projection methods with applications to least squares and total least squares the stabilized versions of the least squares ls and total least squares tls methods are two examples of orthogonal projection methods commonly used to solve the overdetermined system of linear equations ax b when a is nearly rank deficient in practice when this system represents the noisy version of an exact rank deficient zero residual problem tls usually yields a more accurate estimate of the exact solution however current perturbation theory does not justify the superiority of tls over ls in this paper we establish a model for orthogonal projection methods by reformulating the parameter estimation problem as an equivalent problem of nullspace determination when the method is based on the singular value decomposition of the matrix a b the model specializes to the well known tls method we derive new lower and upper perturbation bounds for orthogonal projection methods in terms of the subspace angle which shows how the perturbation of the approximate nullspace affects the accuracy of the solution in situations where tls is typically used such as in signal processing where the noise free compatible problem is exactly rank deficient our upper bounds suggest that the tls perturbation bound is usually smaller than the one for ls which means that tls is usually more robust than ls under perturbations of all the data also the bounds permit a comparison between the ls and tls solutions as well as for any two competing orthogonal projection methods we include numerical simulations to illustrate our conclusions

linear prediction approach for efficient frequency estimation of multiple real sinusoids algorithms and analyses based on the linear prediction property of sinusoidal signals two constrained weighted least squares frequency estimators for multiple real sinusoids embedded in white noise are proposed in order to achieve accurate frequency estimation the first algorithm uses a generalized unit norm constraint while the second method employs a monic constraint the weighting matrices in both methods are a function of the frequency parameters and are obtained in an iterative manner for the case of a single real tone with sufficiently large data samples both estimators provide nearly identical frequency estimates and their performance approaches crame spl acute r rao lower bound crlb for white gaussian noise before the threshold effect occurs algorithms for closed form single tone frequency estimation are also devised computer simulations are included to corroborate the theoretical development and to contrast the estimator performance with the crlb for different frequencies observation lengths and signal to noise ratio snr conditions

an efficient recursive total least squares algorithm for fir adaptive filtering an algorithm for recursively computing the total least squares tls solution to the adaptive filtering problem is described this algorithm requires o n multiplications per iteration to effectively track the n dimensional eigenvector associated with the minimum eigenvalue of an augmented sample covariance matrix it is shown that the recursive least squares rls algorithm generates biased adaptive filter coefficients when the filter input vector contains additive noise the tls solution on the other hand is seen to produce unbiased solutions examples of standard adaptive filtering applications that result in noise being added to the adaptive filter input vector are cited computer simulations comparing the relative performance of rls and recursive tls are described

a neuro fuzzy system architecture for behavior based control of a mobile robot in unknown environments abstract a neuro fuzzy system architecture for behavior based control of a mobile robot in unknown environments is presented a neural network is used to understand environments its inputs are a heading angle between the robot and a specified target and range information acquired by an array of ultrasonic sensors the output from the neural network is a trained reference motion direction for robot navigation the methodology of the behavior based control approach proposed in this paper is 1 to analyze and to decompose a complex task based on stimulus response behavior and 2 to quantitatively formulate each type of behavior with a simple feature by fuzzy sets and fuzzy rules as well as to coordinate conflicts and competition among multiple types of behavior by fuzzy reasoning an advantage is that building fuzzy sets and rules for each simple featured type of behavior is much easier than for a complex task based upon a reference motion direction and distances between the robot and obstacles different types of behavior are fused by fuzzy logic to control the velocities of the two rear wheels of the robot simulation experiments show that the proposed neuro fuzzy system can improve navigation performance in complex and unknown environments in addition this architecture is suitable for multisensor fusion and integration

maximum likelihood estimation of multiple frequencies with constraints to guarantee unit circle roots an approximate maximum likelihood estimator mle of multiple exponentials converts the frequency estimation problem into a problem of estimating the coefficients of a z polynomial with roots at the desired frequencies theoretically the roots of the estimated polynomial should fall on the unit circle but mle as originally proposed does not guarantee unit circle roots this drawback sometimes causes merged frequency estimates especially at low snr if all the sufficient conditions for the z polynomial to have unit circle roots are incorporated the optimization problem becomes too nonlinear and it loses the desirable weighted quadratic structure of mle in the present paper the exact constraints are imposed on each of the first order factors corresponding to individual frequencies for ensuring unit circle roots the constraints are applied during optimization alternately for each frequency in the absence of any merged frequency estimates the rms values more closely approach the theoretical cramer rao cr bound at low snr levels

multiple source location the signal subspace approach the multiple source location problem is solved for a general array geometry using the principal of least squares modeling the incident wavefields may be a mixture of coherent and noncoherent sources that are broadband and or narrowband furthermore the wavefields may be plane waves and or spherical waves an effective algorithmic approach is developed for solving the multiple source location problem in the signal and covariance domains in each domain it is necessary to represent a set of vectors as a linear combination of array steering vectors using this property a generic descent algorithmic approach is developed for estimating the wavefield directions of arrival the success of such iterative approaches is critically dependent on the generation of good estimates to initialize the algorithm an effective initialization procedure is developed for this purpose direction of arrival estimates which arise from the proposed procedure are empirically found to be unbiased and low variance

singular value decomposition and improved frequency estimation using linear prediction linear prediction based lp methods for fitting multiple sinusoid signal models to observed data such as the forward backward fblp method of nuttall 5 and ulrych and clayton 6 are very ill conditioned the locations of estimated spectral peaks can be greatly affected by a small amount of noise because of the appearance of outliers lp estimation of frequencies can be greatly improved at low snr by singular value decomposition svd of the lp data matrix the improved performance at low snr is also better than that obtained by using the eigenvector corresponding to the minimum eigenvalue of the correlation matrix as is done in pisarenko s method and its variants

total least squares approach for frequency estimation using linear prediction the resolution of the estimated closely spaced frequencies of the multiple sinusoids degrades as the signal to noise ratio snr of the received signal becomes low this resolution can be improved by using the total least squares tls method in solving the linear prediction lp equation this approach makes use of the singular value decomposition svd of the augmented matrix for low rank approximation to reduce the noise effect from both the observation vector and the lp data matrix simultaneously comparison is made to the principle eigenvector pe method of tufts and kumaresan both on theoretical and experimental grounds the tls algorithm exhibits superior performance over the pe method where low rank approximation is applied to the data matrix only

matrix pencil method for estimating parameters of exponentially damped undamped sinusoids in noise a study of a matrix pencil method for estimating frequencies and damping factors of exponentially damped and or undamped sinusoids in noise is presented comparison of this method to a polynomial method svd prony method shows that the matrix pencil method and the polynomial method are two special cases of a matrix prediction approach and that the pencil method is more efficient in computation and less restrictive about signal probes it is found through perturbation analysis and simulation that for signals with unknown damping factors the pencil method is less sensitive to noise than the polynomial method an expression of the cramer rao bound for the exponential signals is presented

on the total least squares linear prediction method for frequency estimation the total least squares tls linear prediction lp method recently presented by rahman and yu 1987 and the equivalent improved pisarenko s ip method by kumaresan 1986 are reviewed and generalized by the whitening approach the resulting whitened tls lp method yields higher estimation accuracy than the tls lp this simulation was carried out in double precision fortran 77 on vax 8810 the imsl routines were used to perform eigendecompositions compute the polynomial roots and to generate the pseudo gaussian random numbers

joint estimation of time delays and directions of arrival of multiple reflections of a known signal we present an efficient algorithm for estimating the time delays and the directions of arrival doas of multiple reflections of a known signal the algorithm is based on an iterative scheme that transforms the multidimensional maximum likelihood criterion into two sets of simple one dimensional 1 d maximization problems simulation results illustrating the performance of the algorithm in comparison with the cramer rao bound are included

the equivalence of the total least squares and minimum norm methods signal processing it is shown that the minimum norm solution is equivalent to the total least squares solution it is noted that two versions of the total least squares solution exist one based on the signal subspace and another based on the noise subspace

detection of signals by information theoretic criteria a new approach is presented to the problem of detecting the number of signals in a multichannel time series based on the application of the information theoretic criteria for model selection introduced by akaike aic and by schwartz and rissanen mdl unlike the conventional hypothesis testing based approach the new approach does not requite any subjective threshold settings the number of signals is obtained merely by minimizing the aic or the mdl criteria simulation results that illustrate the performance of the new method for the detection of the number of signals received by a sensor array are presented

fuzzy logic controller design utilizing multiple contending software agents the concept of fuzzy logic control based on a distributed multi agent system is described in this paper it is shown that a system composed of a number of cooperating and contending intelligent agents can perform well in terms of sensing the measured input data and producing output control signals each agent s implementation consists of a number of fuzzy logic control rules although at each time step all the agents compete for the control of the system by proposing a possible action the main controller chooses among the selections based on a number of evaluation criteria it is through the simultaneous cooperation to overall control the system and competition to take control at a given time that the system yields convincing results the effectiveness of the novel methodology is illustrated through the control of a robotic system that keeps an inverted pendulum in balance while being perturbed by a human force in addition the proposed methodology is applied to the practical control of a service mobile manipulator interacting with a human it is shown that fuzzy logic control combined with intelligent multi agent architecture can yield effective and efficient intelligent controllers

a parameter estimation scheme for damped sinusoidal signals based on low rank hankel approximation most of the existing algorithms for parameter estimation of damped sinusoidal signals are based only on the low rank approximation of the prediction matrix and ignore the hankel property of the prediction matrix we propose a modified kumaresan tufts mkt algorithm exploiting both rank deficient and hankel properties of the prediction matrix computer simulation results demonstrate that compared with the original kumaresan tufts 1982 algorithm and the matrix pencil algorithm the mkt algorithm has a lower noise threshold and can estimate the parameters of signal with larger damping factors

algebraicity of holomorphic mappings between real algebraic sets in cn 0 introduction 1 holomorphic nondegenera y of real analytic manifolds 1 1 preliminaries on real submanifolds of c n 1 2 holomorphic nondegeneracy and its propagation 1 3 the levi number and essential finiteness 1 4 holomorphic nondegeneracy of real algebraic sets 2 the segre sets of a real analytic cr submanifold 2 1 complexification of m involution and projections 2 2 definition of the segre sets of m at p0 2 3 homogeneous submanifolds of cr dimension 1 2 4 homogeneous submanifolds of arbitrary cr dimension 2 5 proof of theorem 2 2 1 3 algebraic properties of holomorphic mappings between real algebraic sets 3 1 a generalization of theorems 1 and 4 3 2 propagation of algebralcity 3 3 proof of theorem 3 1 2 3 4 proof of theorem 3 1 8 3 5 an example 3 6 proofs of theorems 1 through 4

recursive updating the eigenvalue decomposition of a covariance matrix the author addresses the problem of computing the eigensystem of the modified hermitian matrix given the prior knowledge of the eigensystem of the original hermitian matrix specifically an additive rank k modification corresponding to adding and deleting blocks of data to and from the covariance matrix is considered an efficient and parallel algorithm which makes use of a generalized spectrum slicing theorem is derived for computing the eigenvalues the eigenvector can be computed explicitly in terms of the solution of a much reduced k k homogeneous hermitian system the overall computational complexity is shown to be improved by an order of magnitude from o n sup 3 to o n sup 2 k where n n is the size of the covariance matrix it is pointed out that these ideas can be applied to adaptive signal processing applications such as eigen based techniques for frequency or angle of arrival estimation and tracking specifically adaptive versions of the principal eigenvector method and the total least squares method are derived

detection and localization of multiple sources via the stochastic signals model a method for the detection of coherent and noncoherent signals based on the application of j rissanen s 1978 minimum description length principle for model selection to the stochastic signals model is presented the detection and localization are done simultaneously with the location estimator coinciding with the maximum likelihood estimator derived by j f bohme 1986 the proposed method outperforms the method of m wax and i ziskind 1989 especially in the threshold region simulation results demonstrating the improved performance are included

a fast maximum likelihood algorithm for frequency estimation of a sinusoid based on newton s method a fast algorithm to estimate the frequency of a sinusoid is presented here it is based on newton s method for finding the root of an equation and it is shown that under easily met conditions the root mean square error rmse of the estimator is practically equal to the cramer rao bound after only two iterations of newton s method for all signal to noise ratios snr s above threshold the estimator s probability density function is computed analytically and the rmse is calculated for one and two iterations of newton s method its computational load is shown to be significantly less than other conventional algorithms

some properties of defuzzification neural networks abstract fuzzy systems have gained more and more attention from researchers and practitioners of various fields in such systems the output represented by a fuzzy set sometimes needs to be transformed into a scalar value and this task is known as the defuzzification process several analytic methods have been proposed for this problem but lately the neural network approach has been used for this purpose when employed as defuzzifiers a neural network is called a defuzzification neural network in this paper some preliminary results on properties of such defuzzification networks will be reported

infinitesimal cr automorphisms of real hypersurfaces let m be an analytic real hypersurface through the origin in c n 1 and let hol m denote the space of vector fields x re z m tangent to m in a neighborhood of the origin where z is a holomorphic vector field defined in a neighborhood of the origin the hypersurface m is holomorphically nondegenerate at the origin if there is no holomorphic vector field tangent to m in a neighborhood of the origin the main result of the paper is that hol m is finite dimensional if and only if m is holomorphically nondegenerate at the origin

advanced fuzzy logic control of a model car in extreme situations abstract state of the art in fuzzy logic control applications is the simple calculus of fuzzy if then rules predominantly using min max operators for the implementation mainly precompiler based tools are used though this concept is sufficient for simple control applications or consumer goods it is often insufficient for complex control applications this work investigates which advanced methods render the application of fuzzy control in complex problems possible and how fuzzy controllers may be built more efficiently the results of the theoretical work is demonstrated by the real model of a high performance race car

estimating frequencies of exponentials in noise using joint diagonalization the problem of estimating frequencies of superimposed exponentials in white noise collected by a single sensor is considered we discuss a solution based on a modification of an analytic algorithm that was proposed for separation of constant modulus signals received by an antenna array by van der veen and paulraj see ibid vol vol 44 p 1136 55 1996 we show that with an appropriate implementation of certain constraints the algorithm produces excellent super resolution results an advantage of the algorithm is that it does not require multidimensional search and therefore it is free from problems associated with search algorithms like convergence to local extremum or proper initialization another advantage is its applicability in nonuniform sampling conditions where methods based on linear prediction cannot be applied we carry out a detailed performance analysis under the assumption of long data record and derive conditions for estimating all the frequencies the performance of the proposed algorithm compares favorably with the performance of the tufts kumaresan method 1982

a perturbation property of the tls lp method it is shown that the tls lp total least squares linear prediction method and the svd prony method are equivalent to the first order perturbation approximation in practice it means that the two methods yield the same estimation variances when the signal to noise ratio snr is above a threshold

performance analysis of spatial smoothing with interpolated arrays the interpolated spatial smoothing algorithm is a computationally efficient method for estimating the directions of arrival doas of signals some of which may be perfectly correlated it extends the spatial smoothing method to arbitrary array geometries a statistical performance analysis of the algorithm is presented closed form expressions for the covariance matrix of the doa estimation errors are derived using a perturbation analysis evaluating these expressions for specific cases and comparing them to the cramer rao lower bound for the doa estimates provides insight into the statistical efficiency of this algorithm the formulas for the error covariance are quite general and can be specialized to provide results for other doa estimation algorithms as well

convergence analysis of a deterministic discrete time system of oja s pca learning algorithm the convergence of oja s principal component analysis pca learning algorithms is a difficult topic for direct study and analysis traditionally the convergence of these algorithms is indirectly analyzed via certain deterministic continuous time dct systems such a method will require the learning rate to converge to zero which is not a reasonable requirement to impose in many practical applications recently deterministic discrete time ddt systems have been proposed instead to indirectly interpret the dynamics of the learning algorithms unlike dct systems ddt systems allow learning rates to be constant which can be a nonzero this paper will provide some important results relating to the convergence of a ddt system of oja s pca learning algorithm it has the following contributions 1 a number of invariant sets are obtained based on which we can show that any trajectory starting from a point in the invariant set will remain in the set forever thus the nondivergence of the trajectories is guaranteed 2 the convergence of the ddt system is analyzed rigorously it is proven in the paper that almost all trajectories of the system starting from points in an invariant set will converge exponentially to the unit eigenvector associated with the largest eigenvalue of the correlation matrix in addition exponential convergence rate are obtained providing useful guidelines for the selection of fast convergence learning rate 3 since the trajectories may diverge the careful choice of initial vectors is an important issue this paper suggests to use the domain of unit hyper sphere as initial vectors to guarantee convergence 4 simulation results will be furnished to illustrate the theoretical results achieved

improved methods for exponential parameter estimation in the presence of known poles and noise by incorporating prior knowledge of known signal poles corresponding to some of the closely spaced sinusoids dowling et al see ieee trans antennas propagat vol 42 no 5 p 590 599 1994 have proposed an lctls flp technique which improves the performance of tls flp we present two better estimators that incorporate the same prior knowledge but clearly outperform lctls flp in resolution and parameter accuracy

a detailed derivation of arrays free of higher rank ambiguities since linear dependence of steering vectors would lead to ambiguous direction of arrival doa estimates it is crucial to employ array with linearly independent steering vectors in doa estimation applications in fact it is well known that in order to determine uniquely the doas of spl nu uncorrelated signals one requires an array that is free of up to rank spl nu ambiguities i e an array whose every spl nu 1 steering vectors with distinct doas are linearly independent however the question as to the sensor arrangement that gives rise to an array that is free of up to rank spl nu ambiguities where spl nu spl isin 3 remains unanswered we construct a class of cross arrays that are free of up to rank spl nu ambiguities where spl nu spl isin 1 through this study we derive a theorem for characterizing inherent ambiguities associated with cross arrays we also provide counterexamples to the conjecture for characterizing higher rank ambiguity proposed by lo and marple see ibid vol 40 p 2641 50 1992 and that proposed by wang et al see icassp 9 vol iv p 509 12 1994

general smoothing techniques for estimating deterministic undamped sinusoidal frequencies from noisy data general high resolution smoothing techniques for estimating deterministic sinusoidal frequencies from short record noisy data are presented symmetric smoothings are proven to provide conjugate reciprocal polynomial roots also in the noisy case and thereby eliminate spurious frequency estimates in numerical simulations they provided the lowest threshold of any currently existing method

unitary puma algorithm for estimating the frequency of a complex sinusoid one dimensional 1 d and two dimensional 2 d frequency estimation for a single complex sinusoid in white gaussian noise is a classic signal processing problem with numerous applications it is revisited here through a new unitary principal singular vector utilization modal analysis puma approach which is realized in terms of real valued computations the 2 d unitary puma is first formulated as an iteratively weighted least squares optimization problem recognizing that only one iteration is sufficient when 2 d unitary puma is initialized using least squares a computationally attractive closed form solution is then obtained a variant of 2 d unitary puma is also developed for the 1 d case due to the real valued computations and closed form expression for the frequency estimate the unitary puma is more computationally efficient than a number of state of the art methods furthermore the asymptotic variances of 1 d and 2 d unitary puma estimators are theoretically derived and numerical results are included to demonstrate the effectiveness of the proposed methods

a modified tls prony method using data decimation the paper introduces a modified tls prony method that incorporates data decimation the use of data decimation results in the reduction in the computational complexity because one high order estimation is replaced by several low order estimations the authors present an analysis of pole variance statistics for this modified tls prony method this analysis provides a quantitative comparison of the parameter estimation accuracy as a function of decimation factors the authors show that by using decimation one can obtain comparable statistical performance results at a fraction of the computational cost when compared with the conventional tls prony algorithm

accuracy of singularity expansion method in time and frequency domains to characterize antennas in presence of noise in this paper the accuracy of the singularity expansion method sem used for antenna characterization is investigated a well known limitation of the sem is that pole extraction is very sensitive to noise a comparison between two main methods of pole extraction is presented the matrix pencil mp method and the cauchy s method are used to extract poles from the radiated fields of a dipole antenna and two bowtie antennas results are presented for simulated fields and the robustness to a white gaussian noise is also analyzed we show that the mp method allows working with lower snr than cauchy s method and is more accurate for field reconstruction

mse based regularization approach to direction estimation of coherent narrowband signals using linear prediction this paper addresses the problem of directions of arrival doas estimation of coherent narrowband signals impinging on a uniform linear array ula when the number of signals is unknown by using an overdetermined linear prediction lp model with a subarray scheme the doas of coherent signals can be estimated from the zeros of the corresponding prediction polynomial although the corrected least squares cls technique can be used to improve the accuracy of the lp parameters estimated from the noisy array data the inversion of the resulting matrix in the cls estimation is ill conditioned and then the cls estimation becomes unstable to combat this numerical instability we introduce multiple regularization parameters into the cls estimation and show that determining the number of coherent signals is closely related to the truncation of the eigenvalues an analytical expression of the mean square error mse of the estimated lp parameters is derived and it is clarified that the number of signals can be determined by comparing the optimal regularization parameters with the corresponding eigenvalues an iterative regularization algorithm is developed for estimating directions without any a priori knowledge where the number of coherent signals and the noise variance are estimated from the noise corrupted received data simultaneously

a newton based ziskind wax alternating projection algorithm applied to spectral estimation a newton based alternating projection ap algorithm for estimating the parameters of exponential signals in noise is presented its performance is compared with that of modified forward backward linear prediction backward linear prediction mfblp blp total least squares tls and iterative quadratic maximum likelihood iqml methods using computer simulations in the case of undamped sinusoids the ap algorithm yielded a lower threshold snr than the others in the case of damped sinusoids its threshold snr was the same as that of tls but was 1 db lower than that of mfblp and iqml

blind received signal strength difference based source localization with system parameter errors the problem of passive source localization has been extensively studied due to its many applications in signal processing and wireless communications signal strength based localization methods have the advantages of low cost and simple implementation in this paper blind source localization with unknown transmit power and unknown path loss exponent is considered based on the received signal strength difference rssd a nonlinear rssd based model is formulated for systems perturbed by noise solutions obtained using conventional least squares methods suffer from significant performance degradation as they only consider errors in the data vector thus an extended total least squares etls method is developed for blind localization which considers perturbations in the system parameters as well as the constraints imposed by the relationship between the observation matrix and data vector the nonlinear and nonconvex rssd based localization problem is then transformed to an etls problem with fewer constraints this is transformed to a convex semidefinite programming sdp problem using relaxation the corresponding etls sdp method is extended to the case with an unknown path loss exponent to jointly estimate the unknown source location and path loss exponent without resorting to transmit power estimation which is sensitive to errors the mean squared error of the proposed etls method is obtained and the corresponding cramer rao lower bound crlb is derived as a performance benchmark performance results are presented that show that the rssd based etls sdp method attains the crlb for a sufficiently large signal to noise ratio snr

analysis of the spatial filtering approach to the decorrelation of coherent sources the recently proposed spatial filtering which is developed to effectively decorrelate coherent signals is analyzed it has been shown that any set of distinct preliminary estimates of directions of arrival doas can be used to obtain a full rank source covariance matrix in addition a particular signal enhancement approach is developed to minimize the effects of the sensor noise statistical analysis of the spatial filtering and its enhanced version are also studied using the monte carlo method

application of maximum likelihood estimation to radar imaging an efficient maximum likelihood ml estimator to obtain the scattering center locations of a target and the relative scattering level of these scattering centers from the scattered field data is described in the proposed method ml estimation is carried out in the image domain rather than in the frequency aspect domain a two dimensional 2 d inverse fourier transform is used to transfer the scattered field data from frequency aspect domain to the image domain down range cross range as expected the scattered field data in the image domain has some regions with high energy the samples in the high energy regions are used to obtain the initial guess for the ml estimator as well as for ml estimation the ml estimator in the image domain is applied to both simulated and experimental scattered fields of some targets

exact maximum likelihood parameter estimation of superimposed exponential signals in noise a unified framework for the exact maximum likelihood estimation of the parameters of superimposed exponential signals in noise encompassing both the time series and the array problems is presented an exact expression for the ml criterion is derived in terms of the linear prediction polynomial of the signal and an iterative algorithm for the maximization of this criterion is presented the algorithm is equally applicable in the case of signal coherence in the array problem simulation shows the estimator to be capable of providing more accurate frequency estimates than currently existing techniques the algorithm is similar to those independently derived by kumaresan et al in addition to its practical value the present formulation is used to interpret previous methods such as prony s pisarenko s and modifications thereof

estimation of parameters of exponentially damped sinusoids using fast maximum likelihood estimation with application to nmr spectroscopy data we present fast maximum likelihood fml estimation of parameters of multiple exponentially damped sinusoids the fml algorithm was motivated by the desire to analyze data that have many closely spaced components such as the nmr spectroscopy data of human blood plasma the computational efficiency of fml lies in reducing the multidimensional search involved in ml estimation into multiple 1 d searches this is achieved by using our knowledge of the shape of the compressed likelihood function clf in the parameter space the proposed fml algorithm is an iterative method that decomposes the original data into its constituent signal components and estimates the parameters of the individual components efficiently using our knowledge of the shape of the clf the other striking features of the proposed algorithm are that it provides procedures for initialization has a fast converging iteration stage and makes use of the information extracted in preliminary iterations to segment the data suitably to increase the effective signal to noise ratio snr the computational complexity and the performance of the proposed algorithm are compared with other existing methods such as those based on linear prediction kiss iqml alternating projections ap and expectation maximization em

efficient mixed spectrum estimation with applications to target feature extraction we present a decoupled parameter estimation dpe algorithm for estimating sinusoidal parameters from both 1 d and 2 d data sequences corrupted by autoregressive ar noise in the first step of the dpe algorithm we use a relaxation relax algorithm that requires simple fast fourier transforms ffts to obtain the estimates of the sinusoidal parameters we describe how the relax algorithm may be used to extract radar target features from both 1 d and 2 d data sequences in the second step of the dpe algorithm a linear least squares approach is used to estimate the ar noise parameters the dpe algorithm is both conceptually and computationally simple the algorithm not only provides excellent estimation performance under the model assumptions in which case the estimates obtained with the dpe algorithm are asymptotically statistically efficient but is also robust to mismodeling errors

direction of arrival estimation for constant modulus signals in many cases where direction finding is of interest the signals impinging on an antenna array are known to be phase modulated and hence to have a constant modulus cm this is a strong property by itself it is already sufficient for source separation and can be used to construct improved direction finding algorithms we first derive the relevant cramer rao bounds crbs for arbitrary array configurations and specialize to uniform linear arrays we then propose a simple suboptimal direction estimation algorithm in which the signals are separated using the cm property followed by direction finding on the decoupled signals compared with the esprit algorithm and the crb for arbitrary signals the algorithm shows good results

estimation of the directions of arrival of signals in unknown correlated noise i the map approach and its implementation the authors propose a method of direction of arrival doa estimation of signals in the presence of noise whose covariance matrix is unknown and arbitrary other than being positive definite they examine the projection of the data onto the noise subspace the conditional probability density function pdf of the projected data given the signal parameters and the unknown projected noise covariance matrix is first formed the a posteriori pdf of the signal parameters alone is then obtained by assigning a noninformative a priori pdf to the unknown noise covariance matrix and integrating out this quantity a simple criterion for the maximum a posteriori map estimate of the doas of the signals is established some properties of this criterion are discussed and an efficient numerical algorithm for the implementation of this criterion is developed the advantage of this method is that the noise covariance matrix does not have to be known nor must it be estimated

simple effective computation of principal eigenvectors and their eigenvalues and application to high resolution estimation of frequencies we present the results of an investigation of the prony lanczos p l method 14 38 and the power method 39 for simple computation of approximations to a few eigenvectors and eigenvalues of a hermitian matrix we are motivated by realization of high resolution signal processing in an integrated circuit the computational speeds of the above methods are analyzed they are completely dependent on the speed of a matrix vector product operation if only a few eigenvalues or eigenvectors are needed the suggested methods can substitute for methods of the linpack or eispack subroutine libraries the accuracies of the suggested methods are evaluated using matrices formed from simulated data consisting of two sinusoids plus gaussian noise comparisons are made to the corresponding eigenvalues and eigenvectors obtained using linpack as a reference also the accuracies of frequency estimates obtained from the eigenvectors are compared

convergence analysis of a deterministic discrete time system of feng s mca learning algorithm the convergence of minor component analysis mca algorithms is an important issue with bearing on the use of these methods in practical applications this correspondence studies the convergence of feng s mca learning algorithm via a corresponding deterministic discrete time ddt system some sufficient convergence conditions are obtained for feng s mca learning algorithm with constant learning rate simulations are carried out to illustrate the theory

detection of the number of coherent signals by the mdl principle an approach is presented to the problem of detecting the number of sources impinging on a passive sensor array that is based on j rissanen s 1983 minimum description length mdl principle the approach is applicable to any type of sources including the case of sources which are fully correlated referred to as the coherent signals case two slightly different detection criteria are derived both requiring the estimation of the locations of the sources the first is tailored to the detection problem per se whereas the second is tailored to the combined detection estimation problem consistency of the two criteria is proved and their performance is demonstrated by computer simulations

nonlinear filtering methods for harmonic retrieval and model order selection in gaussian and non gaussian noise this paper addresses the problem of high resolution parameter estimation harmonic retrieval and model order selection for superimposed sinusoids the harmonic retrieval problem is analyzed using a nonlinear parameter estimation approach estimation is performed using several nonlinear estimators with signals embedded in white and colored gaussian noise simulation results demonstrate that the nonlinear filters perform close to the cramer rao bound model order selection is performed in gaussian and non gaussian noise the problem is formulated using a multiple hypothesis testing approach with assumed known a priori probabilities for each hypothesis parameter estimation is performed using the extended kalman filter when the noise is gaussian the extended high order filter ehof is implemented in non gaussian noise simulation results demonstrate excellent performance in selecting the correct model order and estimating the signal parameters

maximum likelihood localization of multiple sources by alternating projection an algorithm referred to as apm for computing the maximum likelihood estimator of the locations of simple sources in passive sensor arrays is presented the algorithm is equally applicable to the case of coherent signals and to the case of a single snapshot the algorithm is iterative the maximum of the likelihood function is computed by successive approximations the convergence of the algorithm to the global maximum is demonstrated for a variety of scenarios the key to this global convergence is the initialization scheme

a new estimation method in modal analysis in this paper the modal analysis model which is made up of a linear combination of complex exponential functions is considered the problem of estimating the number of modes as well as the frequencies decays amplitudes and phases and their variability is afforded by means of the condensed distribution of the poles of pade approximants to the z transform of the data this provides a unified framework in which all of these problems can be solved in an almost automatic way it is experimentally proved that the results favorably compare with that provided by well established methods

high snr asymptotics for signal subspace methods in sinusoidal frequency estimation high snr limit second order properties of multiple signal classification music minimum norm mn and subspace rotation sur signal subspace methods for sinusoidal frequency estimation are discussed an alternative to large sample analysis of the methods is presented the two most important variants of these methods are considered in connection with the choice of the sample covariance matrix the simpler technique follows the principle of a linear prediction and the more complex one is based on the idea of a forward backward prediction explicit expressions for the high snr covariance elements of the estimation errors associated with all the methods are derived the expressions for the covariances are used to analyze and compare the statistical performances of music mn and sur estimation methods in both of the variants to discuss the problem of the optimal dimension of the data covariance matrix and to study the limit statistical efficiency of the methods performances of the large sample and high snr asymptotics derived using monte carlo simulations are presented

a self tuning fuzzy controller abstract the aim of a fuzzy controller is to compensate the dynamic characteristics of the controlled system it is the same purpose as for the fuzzy logic controller flc now the best design of the flc is required for achievement of this purpose but such a controller has not yet been completely designed because the fuzzy controller is constructed of control rules which are ambiguously described by conventional controller strategies and expert knowledge and because its structure theory is not well established yet that is in order to construct the best rules a highly skilled technique and a tuning technique using trial and error are required as a way to easily design a flc a method which improves a given standard flc is considered in this paper we propose a self tuning algorithm of the flc it has two functions in adjusting the scaling factors which are the parameters of the flc and in improving the control rules of flc by evaluating the control response at real time and the control results after operations we add these functions to the flc and design the self tuning fuzzy controller stfc this makes the flc a good controller

fuzzy drive control of an autonomous mobile robot abstract this paper deals with the fuzzy drive control of an autonomous mobile robot first a fuzzy model fuzzy expert system for the human thought process is designed by using the idea of fuzziness and secondly it is applied to the autonomous mobile robot the autonomous mobile robot is being investigated very actively in many research laboratories of companies and universities around the world for example there are the unmanned automated carrier in a factory the working robot in extreme situations and the house service robot 1 3 a human makes a proper decision and puts the decision into a proper action instantly under fuzzy and insufficient information the fuzzy drive control system architecture is a hierarchy that is constructed by a visual sensor subsystem a motor drive unit and a fuzzy drive expert system which manages these subsystems the visual sensor subsystem which perceives the outer environment such as the road the obstacles etc is comprised of a tv camera and an image processing unit the motor drive unit motor controller has the function of controlling the speed and the steering of the mobile robot by changing the revolution rate of motors mounted on the robot the fuzzy drive expert system which is the core of the fuzzy drive control system recognizes the environment with the image data and sends control commands to the subsystems by these commands from the drive expert system each subsystem motivates the self subsisting control function to achieve that purpose this expert system has an inference unit and rules such as recognition rules and control rules which were obtained experimentally as an application of the fuzzy drive control experiments of driving an autonomous mobile robot on a straight road and one with corners are performed from the experimental results it was found that the robot control system has the faculty of flexible circumstantial decision about equal to what a human is able to realize

reduced complexity maximum likelihood direction of arrival estimation based on spatial aliasing spatial aliasing is an undesirable phenomenon that prevents the unique determination of the direction of arrival doa of impinging signals in array signal processing however the characteristics of spatial aliasing that generates ambiguous doas can be also used to reduce the computational complexity in maximum likelihood ml doa estimation this paper proposes a structural method to dramatically reduce the computational complexity of the ml doa estimation using the spatial aliasing generated by a nested array structure with a doubly scaled aper ture an ml full grid search is computationally simplified by the highly compressed searching range and the small number of candidate values to be searched which are derived based on spatial aliasing performance analyses based on the theoretical bounds and computational complexity with computer simulations show that the proposed method requires an extremely reduced computational load compared to the conventional ml doa estimation with a uniform linear array ula while achieving the performance enhancement in the estimation accuracy due to the enlarged array aperture

linear prediction approach to direction estimation of cyclostationary signals in multipath environment we investigate the estimation of the directions of arrival doa of closely spaced narrowband cyclostationary signals in the presence of multipath propagation by exploiting the spatial and temporal properties of most communication signals we propose a new cyclic forward backward linear prediction fblp approach for coherent signals impinging on a uniform linear array ula in the proposed algorithm the evaluation of the cyclic array covariance matrix is avoided and the difficulty of choosing the optimal time lag parameter is alleviated as a result the proposed approach has two advantages 1 the computational load is relatively reduced and 2 the robustness of estimation is significantly improved the performance of the proposed approach is confirmed through numerical examples and it is shown that this approach is superior in resolving the closely spaced coherent signals with a small length of array data and at relatively low signal to noise ratio snr