Query: spatial  smoothing DOA

title: Improved spatial smoothing techniques for DOA estimation of coherent signals
abstract: In the context of coherent signal classification, spatial smoothing is necessary for the application of the eigen based direction of arrival (DOA) estimation methods. However, the currently known spatial smoothing algorithms not only reduce the effective aperture of the array, but also do not consider the cross correlations of the subarray outputs. An improved spatial smoothing algorithm which can fully utilize the correlations of the array outputs and produce a more stable estimate of the covariance matrix is presented. Simulation results are provided to verify the theoretical prediction. The superiority of this method over the conventional methods is obvious, especially when the SOSR (subarray to overall size ratio) is small.

title: Spatial difference smoothing for DOA estimation of coherent signals
abstract: A new method is proposed for direction of arrival estimation of coherent signals in the presence of correlated noise fields. The method comprises two steps: a first step for discriminating incoherent signals, and a second step for resolving correlated and coherent signals using spatial difference smoothing. It can eliminate spatially correlated noise and resolve more signals compared with the existing methods. The theoretical analysis and simulation results show that the proposed method is effective.

title: Spatial smoothing for direction of arrival estimation of coherent signals in the presence of unknown mutual coupling
abstract: There is rarely a method that can deal with the direction of arrival (DOA) estimation of coherent signals in the presence of unknown mutual coupling. Based on the special structure of mutual coupling matrix for uniform linear array (ULA) an improved spatial smoothing algorithm for DOA estimation of coherent signals is proposed. The DOAs can be accurately estimated without any calibration sources since the effects of mutual coupling can be eliminated by the inherent mechanism of the proposed algorithm. Simulation results demonstrate the effectiveness of the algorithm.

title: Computationally efficient DOA estimation in a multipath environment using covariance differencing and iterative spatial smoothing
abstract: High resolution signal processing using sensor arrays to estimate the directions of unknown correlated signals in multipath environments is associated with high computational complexity. This paper proposes a computationally efficient direction of arrival (DOA) estimation algorithm. The algorithm is based on two stages of detection: a first stage for discriminating uncorrelated signals and a second stage for resolving the directions of arrival of correlated signals using covariance differencing and iterative spatial smoothing. Simulation results show that the proposed algorithm operates at a much lower computational cost compared to standard methods.

title: Robust beamforming for coherent signals based on the spatial smoothing technique
abstract: Spatial smoothing provides an elegant solution for both beamforming and direction of arrival (DOA) estimation in a coherent signal environment. However, this technique is based on special array structures such as uniform linear arrays (ULAs) and its performance will degrade when the special structure is destroyed by all kinds of array errors in practice. In this paper, the robust beamforming problem for coherent signals is addressed and a forward only (FO) beamformer is proposed first based on worst case optimization by considering both the steering vector and the correlation matrix errors. By treating the steering vector error differently, two solutions are derived, which are then extended to the forward backward (FB) case with improved performance. By introducing a preprocessing matrix, real valued closed form solutions are then presented with the same performance as in the FB case, but with much lower computational complexity.

title: DOA estimation for coherent sources in beamspace using spatial smoothing
abstract: Beamspace MUSIC (multiple signal classification) algorithm is often used to solve the DOA (direction of arrival) estimation problem to take advantage of the benefits of beamspace operations, such as reduced computation complexity, reduced sensitivity to system errors, reduced resolution threshold, reduced bias in the estimate, and so on. However, this method will fail to work properly when the source signals are coherent or strongly correlated, which are often encountered in sonar or radar environment. This paper uses the forward backward spatial smoothing technique as the preprocessor of beamspace MUSIC for linear arrays, after which the coherence of the source signals can be removed in the covariance matrix. We show by computer simulation that the resolution of DOA estimation using beamspace MUSIC after spatial smoothing is better than using element space MUSIC after spatial smoothing. We also show that the beamspace MSUIC using spatial smoothing is more robust than element space MUSIC when there are system errors in the linear array.

title: On the explanation of spatial smoothing in MUSIC algorithm for coherent sources
abstract: This paper presents the necessity analysis and the sufficiency analysis of DOA estimation in coherent situation by means of three different aspects respectively, including the rank of covariance matrix, the correlation coefficient and the phase difference between coherent sources. The analyses explain how spatial smoothing decorrelates the coherent sources and how many times of spatial smoothing are needed to decorrelate completely. Simulation results illustrate the sensitivity of the MUSIC algorithm to the number of spatial smoothing.

title: Performance Analysis of Spatial Smoothing Schemes in the Context of Large Arrays
abstract: This paper addresses the statistical behavior of spatial smoothing subspace DoA estimation schemes using a sensor array in the case where the number of observations N is significantly smaller than the number of sensors M, and that the smoothing parameter L is such that M and NL are of the same order of magnitude. This context is modeled by an asymptotic regime in which NL and M both tend to at the same rate. As in recent works devoted to the study of (unsmoothed) subspace methods in the case where M and N are of the same order of magnitude, it is shown that it is still possible to derive improved DoA estimators termed as Generalized MUSIC with spatial smoothing (G MUSIC SS) The key ingredient of this work is a technical result showing that the largest singular values and corresponding singular vectors of low rank deterministic perturbation of certain Gaussian block Hankel large random matrices behave as if the entries of the latter random matrices were independent identically distributed. This allows to conclude that when the number of sources and their DoA do not scale with M,N,L, a situation modeling widely spaced DoA scenarios, then both traditional and Generalized spatial smoothing subspace methods provide consistent DoA estimators whose convergence speed is faster than 1/M. The case of DoA that are spaced of the order of a beamwidth, which models closely spaced sources, is also considered. It is shown that the convergence speed of G MUSIC SS estimates is unchanged, but that it is no longer the case for MUSIC SS ones.

title: On spatial smoothing for direction of arrival estimation of coherent signals in impulsive noise
abstract: This paper is mainly to deal with the problem of direction of arrival (DOA) estimations of multiple narrow band coherent sources impinging on the far field of a uniform linear array (ULA) under impulsive noise environments modeled as symmetric a stable distribution. By applying the spatial smoothing idea to the phased fractional lower order moments matrices, the PFLOM_SS matrix is first defined. The noise subspace can be estimated by the eigenvalue decomposition of PFLOM_SS matrix so as to estimate the DOAs of coherent sources in impulse noise. Computer simulation results verify the correctness and effectiveness of the proposed method for a wide range of highly impulsive environments.

title: DOD and DOA estimation using the spatial smoothing in MIMO radar with the EmV sensors
abstract: This paper deals with the directions of departure (DOD) and directions of arrival (DOA) estimation of coherent and noncoherent targets in bistatic MIMO radar with the electromagnetic vector (EmV) sensors. The high resolution eigenspace based methods such as, estimation of signal parameters via rotational invariance technique (ESPRIT) multiple signal classification, etc. fails to estimate DOD and DOA of fully or partially correlated targets. In order to employ these methods, a new pre processing method is developed based on the spatial smoothing in MIMO radar with the EmV sensors. Then, the directions are estimated using the ESPRIT algorithm. Monte Carlo simulations are performed to investigate the estimation accuracy and resolution capability of the proposed approach, and to compare with no pre processing and the existing method. The simulation result shows that, the proposed methodology improves the performance significantly.