Abstract:
A method for detecting signals using an adaptive transducer arrangement, the arrangement including a transducer array having a plurality of transducers, a beamformer, and an energy detector, the method comprising: determining weights to be applied by the beamformer to signals emitted from each transducer in order to maximize a performance metric; applying the determined weights to the signals emitted from each transducer; measuring the energy received at the energy detector; comparing the measured energy with a predetermined value and based on said comparison determining whether or not one or more signals are present.

Description:
FIELD 
     The present disclosure relates to a method and an apparatus for detecting signals. In particular, it relates to a method of and apparatus for using a transducer array and an adaptive beamformer to detect signals. 
     BACKGROUND 
     In the field of signal detection, the key problem which must be solved, is how to distinguish between noise alone, and the presence of one or more signals together with noise. This is a well known problem and many methods and devices are known for solving it. Conventional beamformers have been used to provide a more sensitive solution to the signal detection problem. However, they are typically used in the traditional manner of forming a directional beam and looking for signals in a particular direction. Spatial diversity methods which use multiple transducers have also been previously proposed. Neither of these techniques provides optimum energy detection sensitivity. 
     SUMMARY 
     In a first aspect, the present disclosure provides a method for detecting signals using an adaptive transducer arrangement, the arrangement including a transducer array having a plurality of transducers, a beamformer, and an energy detector, the method comprising: determining weights to be applied by the beamformer to signals emitted from each transducer in order to maximise a performance metric; applying the determined weights to the signals emitted from each transducer; measuring the energy received at the energy detector; comparing the measured energy with a predetermined value and based on said comparison determining whether or not one or more signals are present. 
     In a second aspect, the present disclosure provides a signal detection apparatus, comprising: a transducer array having plurality of transducers; a beamformer for applying weights to signals emitted from each transducer; an energy detector for measuring the energy of the combined signals received from the beamformer; a processor; wherein the beamformer is further for determining the weights to apply to the signals emitted from the transducers in order to maximise a performance metric; the processor is for comparing the measured energy with a predetermined value and determining, based on said comparison, whether or not one or more signals are present. 
     Further features are provided in the appended claims and accompanying description. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present disclosure will now be described by way of example only and with reference to the accompanying drawings, in which: 
         FIG. 1  shows an antenna array arrangement according to an embodiment of the present disclosure; 
         FIG. 2  shows a method in accordance with an embodiment of the disclosure; 
         FIG. 3  shows a method of optimising a weight vector in accordance with a further embodiment of the disclosure; 
         FIG. 4  shows a method of signal detection in accordance with an embodiment of the disclosure; and 
         FIG. 5  shows an antenna array arrangement according to a further embodiment of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 1  shows an adaptive antenna array  100  in accordance with an embodiment of the disclosure. The adaptive antenna array  100  includes a plurality of antennas  101 . In this embodiment, the adaptive antenna array  100  includes antenna  101 - 1 , antenna  101 - 2  through to antenna  101 -N. Each antenna  101  is connected to an antenna output line  102 . In the present example, antenna  101 - 1  is coupled to output line  102 - 1 , antenna  101 - 2  is coupled to output line  102 - 2  and antenna  101 -N is coupled to output line  102 -N. 
     The adaptive antenna array  100  also includes a beamformer  103 . The beamformer  103  includes gain and phase adjusters  104 . In particular, the beamformer  103  includes gain and phase adjusters  104 - 1 ,  104 - 2  through to  104 -N. Each of the respective antenna output lines  102  is connected to a respective gain and phase adjuster  104 . The beamformer  103  is arranged to apply adaptive complex weights to the antenna signals using the gain and phase adjusters  104 . This will be described in more detail below. The beamformer  103  also includes gain and phase adjuster outputs  105 . In this example, the array includes gain and phase adjuster outputs  105 - 1 ,  105 - 2  through to  105 -N. Each of the respective gain and phase adjusters  104  is coupled to a respective gain and phase adjuster output  105 . 
     The beamformer  103  also includes the summing circuit  106 . The summing circuit  106  is arranged to sum the signals which are output from the gain and phase adjusters  104 . The adaptive antenna array  100  includes a summation circuit output  107  which is coupled to the summing circuit  106 . Finally the adaptive antenna array  100  includes an energy detector  108  which is coupled to the summation circuit output  107 . 
     Before describing the operation of the adaptive antenna array  100  in detail, a brief overview of the process will be described in connection with  FIG. 2 . The process includes three main steps. The first step is to determine the weights to apply to the gain and phase adjusters  104  (S 200 ). Following this, the energy detector  108  measures the energy at its input (S 201 ). Finally, the measured energy is compared with a predetermined value to determine whether or not one or more signals is present (S 202 ). The process will now be described in more detail. 
     Prior to energy detection, the array  100  must be set up in order to maximise a performance metric at the input to the energy detector  108 . In order to achieve this, the weights applied by the gain and phase adjusters  104  must be established. This process will be described with reference to  FIG. 3 . 
     For the N element array shown in  FIG. 1 , the received signals, x(t)εC N×1 , are modelled as:
 
 x ( t )= As ( t )+ n ( t )
 
where AεC N×K  is the array manifold matrix, s(t)εC K×1  is the received signals vector, and n(t)εC N×K  is the noise vector. As shown in  FIG. 1 , the received signals are the signals present at the outputs of the antennas  101 . The model accounts for K co-channel signals simultaneously arriving at the array  100 . Note that the antenna array  100  may have an arbitrary layout. There are no restrictions on the positions of the antennas  101 . Accordingly, the array manifold matrix does not need specifying in this model.
 
     Without loss of generality, the model represents the received signals at complex baseband. For the purposes of this embodiment, it is assumed that the band of interest has already been mixed down from the carrier frequency. It is also assumed that the baseband received signal has been pre-filtered by a low-pass filter to limit the average noise power. The noise vector models both externally generated noise, as well as noise generated within the system, such as thermal noise. The noise may be spatially correlated. 
     The signal at the output of the beamformer is given by:
 
 y ( t )= w   H   x ( t )
 
where w=[w 1 , w 2 , . . . , w N ] T εC N×1  is the complex weight vector containing all N adaptive weights. As shown in  FIG. 1 , y(t) is the signal at the output of the summation circuit  106 .
 
     As noted above, in order to detect one or more signals, a performance metric must be used. The proposed performance metric for the adaptive beamformer  103  prior to energy detection is defined as: the ratio of the total signals plus noise power to the total noise power at the beamformer  103  output (S 300 ). This is expressed as 
             λ   =         E   ⁢     {     |     y   ⁡     (   t   )       ⁢     |   2       }         E   ⁢     {     |       w   H     ⁢     n   ⁡     (   t   )         ⁢     |   2       }         =         w   H     ⁢     R   x     ⁢   w         w   H     ⁢     R   n     ⁢   w               
where the received signal covariance matrix is defined as R x =E{x(t)x(t) H } and R n =E{n(t)n(t) H } is the noise covariance matrix.
 
     Maximizing the performance metric (S 301 ) with respect to w 
                 ∂   λ       ∂     w   *         =             w   H     ⁢     R   n     ⁢     wR   x     ⁢   w     -       w   H     ⁢     R   x     ⁢     wR   n     ⁢   w           (       w   H     ⁢     R   n     ⁢   w     )     2       =   0           
which simplifies to
 
     
       
         
           
             
               
                 
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     Substituting for λ we obtain a generalized eigenvector equation
 
 R   x   w=λR   n   w  
 
     So the optimum weight vector is equal to the generalized eigenvector associated with the maximum generalized eigenvalue of the matrix pencil (R x , R n ). 
     To calculate this optimum weight vector, estimates of both R x  and R n  are required. Matrix R x  is estimated by directly taking the expectation of the outer product of the received signals vector x(t) with itself over a suitable averaging period. R n  may be obtained by estimating R x  when it is known that no signals are present, because in the absence of any signals R n =R x . Alternatively, the expectation of the outer product of the received signals vector with itself can be calculated for unoccupied adjacent frequency channels, and then the elements of the covariance matrices thus generated can be interpolated into the frequency channel of interest to provide an estimate of R n . Finally, if direct measurement is not feasible, then R n  can be estimated by modelling the noise characteristics of the receiver equipment and the external noise environment. 
     Accordingly, for a given set of signal data, we are able to determine the optimum weight vector (S 302 ). 
     Following determination of the optimum weight vector, the weights are applied to the same set of signal data. Accordingly, the ratio of the total power of the received signals plus noise to the total noise power arriving at the energy detector is maximised. The process of energy detection with be described with reference to  FIG. 4 . 
     For an energy based signal detector, we need to choose between the following two hypotheses:
         H 0 : The signal at the beamformer output y(t) is noise alone.   H 1 : y(t) consists of one or more signals plus noise.       

     The test statistic for the energy detector, V, is equal to the square of the beamformer  103  output signal integrated over a finite time interval T: 
     
       
         
           
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     A practical energy detection threshold should be proportional to the noise power at the output of the beamformer  103 , which is given by:
 
γ= kw   H   R   n   w  
 
where k is a scalar which may be used to adjust the false alarm rate. Consequently, the detection test hypotheses can now be expressed as:
 
 H   0   :V≦γ 
 
 H   1   :V&gt;γ 
 
     In other words, if the measured energy is greater than the energy due to noise, then one or more signals may assumed to be present. The first step in the process is to measure V at the energy detector  108  (S 400 ). V is then compared against γ, which has been determined in advance (S 401 ). If V≦γ then it is determined that the beamformer output  103  is noise alone (S 402 ). If V&gt;γ then it is determined that the beamformer output  103  is noise plus one or more signals (S 403 ). 
     This adaptive array energy detector provides up to 10 log 10  N dB coherent gain and a maximum of N orders of diversity. Furthermore, if some of the noise is external to the detection system and is correlated between the antenna elements, then the beamformer  103  described above will act to cancel the correlated noise and further gains in detection sensitivity are possible. 
     Of course, this is only possible if a good estimate of R n  is available for the generalized eigenvector calculation. If only a poor estimate of R n  is available then a different approach using another performance metric (which is not dependent on R n ) can provide better results. In a second embodiment, the ratio of the total signals plus noise power at the beamformer  103  output to the norm of the weight vector is optimized: 
     
       
         
           
             
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     To maximize λ′ the following standard eigenvector equation needs to be solved:
 
 R   x   w=λ′w  
 
     This time the optimum weight vector is equal to the eigenvector associated with the maximum eigenvalue of R x . The test statistic and detection threshold are then calculated as for the first version of this adaptive array energy detector. This simplified version of the adaptive array energy detector provides up to 10 log 10  N dB coherent gain and a maximum of N orders of diversity, but does not act to cancel any spatially correlated noise which might be present. 
     A third embodiment of the present disclosure will now be described with reference to  FIG. 5 .  FIG. 5  shows an adaptive antenna array  500  in accordance with the third embodiment of the disclosure. The adaptive antenna array  500  includes a plurality of antennas  501 . The array includes N antennas, as in the first embodiment. The array  500  includes antenna  501 - 1  through to antenna  501 -N. In this embodiment, one of the antennas is designated as antenna  501 - m  which is used as a reference antenna. In  FIG. 5 , antenna  501 - m  is shown as a separate antenna, but it is in fact one of the N antennas of the array  500 . Each antenna  501  is connected to an antenna output line  502 . In the present example, antenna  501 - 1  is coupled to output line  502 - 1  and antenna  501 -N is coupled to output line  502 -N. Antenna  501 - m  is coupled to output line  502 - m.    
     The adaptive antenna array  500  also includes a beamformer  503 . The beamformer  503  includes gain and phase adjusters  504 . In particular, the beamformer includes gain and phase adjusters  504 - 1  through to  504 -N. In this embodiment, antenna  501 - m  is not coupled to the beamformer  503 , and hence the output line  502 - m  is not coupled to a gain and phase adjuster. Each of the respective antenna output lines  502  is connected to a respective gain and phase adjuster  504 , except for output line  502 - m . The beamformer  503  is arranged to apply adaptive weights to the antenna signals using the gain and phase adjusters  504 . This will be described in more detail below. The beamformer  503  also includes gain and phase adjuster outputs  505 . In this example, the array includes gain and phase adjuster outputs  505 - 1  through to  505 -N. Each of the respective gain and phase adjusters  504  is coupled to a respective gain and phase adjuster output  505 . In particular, gain and phase adjuster  504 - 1  is coupled to output  505 - 1  and gain and phase adjuster  505 -N is coupled to output  505 -N. 
     The output of antenna  501 - m  is fed to cross correlator  509 , via line  502 - m . In practise, the cross correlator may be implemented in software. As will be described in more detail below, the cross correlator cross-correlates the output of the antenna  501 - m  and the outputs of the other antennas in order to determine a weight vector to apply to the gain and phase adjusters  504 . 
     The beamformer  503  also includes the summing circuit  506 . The summing circuit is arranged to sum the signals which are output from the gain and phase adjusters  504 . The array  500  includes a summation circuit output  507  which is coupled to the summing circuit  506 . Finally the array  500  includes an energy detector  508  which is coupled to the summation circuit output  507 . 
     The first stage in the process is to determine the adaptive weight vector for a given set of data. Starting with the standard received signal model for an N element array, the array is partitioned so that d m (t) is the signal from the m th  array element and x   m   (t)εC (N-1)×1  is the signal vector excluding the m th  array element. Consequently, a subarray is formed:
 
 x     m   ( t )= A     m     s ( t )+ n     m   ( t )
 
where A   m    is the array manifold matrix with its m th  row removed, and n   m   (t) is the noise vector with its m th  element removed. Furthermore:
 
 d   m ( t )= s   T ( t ) a   m   +n   m ( t )
 
where a m  is the m th  column of A T  and n m (t) is the m th  element of the noise vector.
 
     We now consider d m (t) to be the reference signal and x   m   (t) to be our new observation vector. Forming the cross-correlation vector between these signals:
 
 r   x       m       d     m     =E{x     m   ( t ) d   m *( t )}
 
     The subarray beamformer weight vector is then set to: 
               w     m   _       =       r       x     m   _       ⁢     d   m           ||     r       x     m   _       ⁢     d   m         ||             
so that the signal at the output of the beamformer is:
 
 y   m ( t )= w     m     H   x     m   ( t )
 
     The test statistic which is thresholded to decide whether signal energy is present is then given by: 
               V   m     =         1   T     ⁢     ∫   t     t   +   T         |       y   m     ⁡     (   t   )       ⁢     |   2     ⁢           ⁢     ⅆ   t             
and the detection threshold is:
 
γ m   =kw     m     H   R   n       m       w     m   
 
where R n       m     =E{n   m   (t)n   m   (t) H } is the noise covariance matrix for the subarray excluding the m th  element.
 
     To understand the effect of this beamformer note that d m (t) contains a linear combination of all of the signals incident upon the receiving array. Consequently, by forming w   m    with d m (t) as a reference signal, this weight vector will attempt to coherently combine the spatial channels to maximize the power of the linear combination of received signals at the beamformer output. In effect, this is a spatial matched filter for the combined signals received by the array, and the performance metric which is implicitly optimized at the beamformer output is the total power of the received signal components which are present within the reference signal. The result is to increase the detection sensitivity by a maximum theoretical 10 log 10 (N−1) dB. Additionally, during small-scale fading conditions, up to N−1 orders of diversity are available. Although the theoretical performance of this adaptive array detector is slightly worse than for the previously described approach, it does have the benefit of simplicity of implementation. 
     While the above examples have been described in the context of radio signals and an adaptive antenna array, the disclosure is also applicable to acoustic or sonar applications using arrays of microphones or hydrophones. The term “transducer” is used to refer to an antenna, microphone, hydrophone, or any other suitable sensor. 
     Glossary of Mathematical Notation 
     A summary of the mathematical notation used in this specification is provided below:
     C The field of complex numbers   a(t) Scalar signal   a(t) Vector signal   A Matrix   E{•} Expectation   (•)* Complex conjugate   (•) T  Transpose   (•) H  Hermitian (complex conjugate) transpose