Abstract:
A method and apparatus for detecting, processing and tracking sonar signals to provide bearing, range and depth information that locates an object in three-dimensions underwater space. A Twenty Six Nearest Neighbor Peak Picker (TSNNPP) is disclosed that improves the detection of signals in noisy background by differentiating bandwidth (BW) characteristics of signals from BW characteristics of noise.

Description:
STATEMENT OF GOVERNMENT INTEREST 
     The invention described herein may be manufactured and used by or for the Government of the United States of America for governmental purposes without the payment of any royalties thereon or therefor. 
    
    
     BACKGROUND OF THE INVENTION 
     (1) Field of the Invention 
     The present invention generally relates to sonar systems and more specifically to sonar systems particularly adapted for identifying the location of an underwater object. 
     (2) Description of the Prior Art 
     Conventional passive sonar systems detect acoustic signals emanating from an underwater object; that is, any device that moves through the water while emitting acoustic signals that sonar can detect. Torpedoes and submarines are examples of such underwater objects. 
     As modern, very quiet submarine platforms become operational in large numbers, new methods of detecting very low level signals from these quiet submarine platforms are desired, especially in the presence of high noise levels from surface shipping, wind, biologics, and other sources of ambient noise. Currently, post processing narrowband beamformed data from sonar arrays is performed by spectrally flattening, or whitening a selected beam&#39;s data by use of a noise spectral equalization (nse) algorithm and displaying the resulting signal-to-noise (SNR) as several shades of gray on a LOFARGRAM display. There are several disadvantages to this type of algorithm and display. First, the beamformed data and threshold are discarded and eliminated from the detection process. Second, the beamformed data from one beam are not compared to adjacent spatial beams to compare relative levels, and therefore, beamformed data are thresholded independently for all beams. Third, beamformed data are assumed to contain energy of interest only in very narrow frequency bands (&lt;0.1 Hz) or in very broadbands (&gt;25 Hz), and energy of intermediate bandwidths (BWs) are not addressed by current post processing algorithms. Modern submarine platforms of interest radiate energy in all bandwidths and an algorithm is desired to be developed for the detection of energy in all bandwidths. 
     SUMMARY OF THE INVENTION 
     Therefore, it is an object of the present invention to provide a system that detects and measures energy in all bandwidth emanating from an underwater object. 
     Another object of the present invention to improve detection of signals present in noisy backgrounds. 
     In addition, it is an object of the present invention to provide a system having improved detection of signals of interest emanating from submerged objects. 
     Accordingly, the current invention provides a method and apparatus for detecting, processing and tracking sonar signals to provide bearing, range and depth information that locates an object in three-dimensions underwater space. A Twenty Six Nearest Neighbor Peak Picker (TSNNPP) is disclosed that improves the detection of signals in noisy background by differentiating bandwidth (BW) characteristics of signals from BW characteristics of noise. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The appended claims particularly point out and distinctly claim the subject matter of this invention. The various objects, advantages and novel features of this invention will be more fully apparent from a reading of the following detailed description in conjunction with the accompanying drawings in which like reference numerals refer to like parts, and in which: 
     FIG. 1 is a block diagram of the prior art system incorporated into the present invention; 
     FIG. 2 illustrates the operation of an eight nearest neighbor peak picker (ENNPP) algorithm that is part of the background of the present invention; 
     FIG. 3 illustrates the operation of a twenty-six nearest neighbor peak picker (TSNNPP) routine of the present invention; and 
     FIG. 4 illustrates the response associated with the operation of the TSNNPP routine of FIG.  3 . 
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     The present invention is an improvement of the invention described in U.S. Pat. No. 5,481,505 which is herein incorporated by reference. The present invention incorporates a herein termed “Twenty Six Nearest Neighbor Peak Picker (TSNNPP)” technique. The TSNNPP is an extension of the “Eight Nearest Neighbor Peak Picker (ENNPP)” which is completely described in U.S. Pat. No. 5,481,505. For the sake of brevity, the details of the operation of the system of U.S. Pat. No. 5,481,505, although applicable to the present invention, are not repeated herein, but rather are referenced as needed. 
     In general, the TSNNPP technique of the present invention determines relative maxima in beam levels on a frequency-azimuth-bandwidth (FRAZBW) surface at the output of a beamformer. Detection is enhanced for passive sonar systems for picking, peaks on the four dimensional FRAZBW surface rather than the commonly used FRAZ surface, especially if the Fourier Integral Method (FIM) algorithm is used as the beamforming method. The present invention may be further described with reference to FIG.  1 . 
     The apparatus  10  shown in FIG. 1 includes a towed horizontal hydrophone array  12  that receives acoustic signals in the water for all potential sources including any underwater objects. OBJ 1  and OBJ 2  represent two objects that produce acoustic signals that radiate as multiple plane waves PW 1  and PW 2  respectively. Fast Fourier Transform (FFT) processors  14 , shown as individual processors FFT( 1 ) . . . FFT(M), process signals from corresponding ones of M spaced hydrophones in the array  12 . A conventional measured covariance matrix processor  16  receives the output signals from the FFT processors  14  and interacts with an inverse beamforming plane wave beamformer processor  18  for producing an estimated bearing to a possible object. 
     The remaining portions of the apparatus  10  utilize the estimated bearing signal from the inverse beamforming plane wave beamformer  18  and covariance matrix data supplied by the measured covariance matrix processor  16  to produce beam values for each of a plurality of incremental ranges and depths along the estimated bearing. A weighting processor  20  can provide appropriate weighting functions for the output of the measured covariance matrix processor  16 . 
     An inverse beamforming matched field processor  22  uses the output of the measured covariance matrix processor  16  in its original or weighted form and signals from a signal propagation model processor  24 . The processor  24  models the signal propagation path characteristics from each of a plurality of incremental locations located at incremental ranges and depths along the estimated bearing. The IBF matched field processor  22  then generates a correlation value for each such incremental location. A peak selection circuit  26  selects those incremental locations that exhibit a maximum with respect to adjacent incremental locations. The foregoing processors operate iteratively over time. 
     An “M of N” tracker circuit  28  comprises a processor that utilizes the succession signals from the peak selection circuit  16  during each iteration to eliminate false targets and enable a target classification circuit  30  to classify a possible object as a target. A target display  32  provides the track of the bearing and range to and depth of each target over time. 
     As previously mentioned, the TSNNPP technique of the present invention is an extension of the ENNPP technique more fully described in U.S. Pat. No. 5,481,505. The operation of the ENNPP is illustrated in FIG. 2, more fully described in U.S. Pat. No. 5,581,505, and results in the detection of all the peaks relative maxima in beamformed levels on the beamformed FRAZ surface for a given time epoch, also more fully described in U.S. Pat. No. 5,481,505. A peak or relative maxima, beam level can be described by the following parameters: level; frequency; azimuth angle; azimuthal width; elevation angle; elevation angle width; and time. 
     Beam level on the FRAZ surface as a function of time is input to the ENNPP and tracked by the Inverse Beamforming M of N tracker circuit  28  in a manner as more fully described in U.S. Pat. No. 5,481,505. In the current invention, bandwidth is added to the parameter list above describing a beam level peak, or relative maxima, in the practice of the present invention. Since sources of interest in detection are assumed to be point sources, azimuthal angle width and elevation angle width are not used in the peak picking process of the present invention. 
     The bandwidth of the peak beam level may be further described with reference to FIGS. 3 and 4, wherein FIG. 3 illustrates the correlation  34 , in the form of a block, between the bandwidth, azimuth and center frequency parameters, and FIG.  4  is a response curve  36  of the center frequency f c  of FIG. 3 having a peak  38 . 
     The bandwidth and center frequency, f c , of a peak beam level is determined, as shown in FIG. 3, by first performing a derivative test known in the art. The center frequency f c  being calculated by the bandwidth derivative test has a certain beam level that is examined to see if it qualifies as an ENNPP relative maxima as more fully described in U.S. Pat. No. 5,481,505. If there is no ENNPP peak, or relative maxima, at this center frequency, no bandwidth based peak calculation is made because the beam energy in this frequency bin will certainly not qualify as a peak among its twenty-six nearest neighbors or relative maxima. If the beam level in the center frequency bin being examined is a ENNPP peak as specified in U.S. Pat. No. 5,481,505, the bandwidth based peak calculation illustrated in FIG. 3 is performed. 
     The bandwidth based peak calculation is performed as follows: 
     For frequency bins less than the center frequency f c , the following bandwidth peak test will be performed until BW L   n  becomes zero or negative:                BW   L   n     =       [       BL        (     f   n     )       -     BL        (     f     n   -   1       )         ]         f   n     -     f     n   -   1                   (   1   )                                
     where 
     f n =center frequency of the nth frequency bin 
     BL=beam level 
     BW L   n =left half bandwidth 
     Likewise, for frequency bins greater than the center frequency, f c , the following derivative test will be performed until BW R   n  becomes zero or positive:                BW   R   n     =       [       BL        (     f     n   +   1       )       -     BL        (     f   n     )         ]         f     n   +   1       -     f   n                 (   2   )                                
     where BW R   n =right hand bandwidth 
     The total bandwidth (BW) associated with the ENNPP peak, or relative maxima, is defined as: 
     
       
           BW=BW   L   n   +BW   R   n   (3) 
       
     
     The peak&#39;s bandwidth as defined in Equation (3) is not always greater than the width associated with the beam levels equal to one half of the peak&#39;s beam level (commonly called the “3 dB down” or “half power” width). It is the bandwidth defined in Equation (3) that is the parameter associated with the peak, or relative maxima, level input to the M of N tracker circuit  28  more fully described in U.S. Pat. No. 5,481,505. 
     Any bandwidth less than bandwidth calculated from Equations (1), (2), and (3) will have less total energy, and any bandwidth greater than bandwidth will have less signal energy than ambient noise energy. For this reason, the peak level at center frequency, f c , azimuth θ o , and bandwidth is greater than the beam levels in all adjacent twenty six nearest neighbor frequency azimuth bandwidth (FRAZBW) cells. 
     Since most data processors are digital and the frequency spectra of a beam is generated by a Fast Fourier Transform (FFT) there is a minimum bandwidth equal to the frequency resolution of the FFT. Also, all bandwidths determined by Equations (1), (2), and (3) will be integral multiples of this minimum bandwidth shown as BW o  in FIG.  3 . For analogue processors, bandwidth can be any arbitrary value. 
     Finally, peaks found by the above algorithm are processed with the M of N tracker circuit  28 , more fully described in U.S. Pat. No. 5,481,505. The M of N tracker preferably has three additional settings to address the peak&#39;s bandwidth. First, there is a bandwidth range specifying the minimum and maximum bandwidth to be input to the M of N tracker circuit  28 . Second, there is “bandwidth fix” true or false which allows the bandwidth to vary by only a fixed (true) amount, or tolerance, with time about the bandwidth of the first peak in the track or not (false). The third M of N tracker circuit  28  parameter is the magnitude of the bandwidth tolerance. These parameters of bandwidth range, bandwidth fix, and magnitude of the bandwidth tolerance are described in detail in U.S. Pat. No. 5,481,505 for the parameters of frequency and azimuth. 
     The advantage of adding bandwidth as a parameter input into the M of N tracker circuit  28  is to improve detection of signals in noisy background by differentiating bandwidth characteristics of signals from bandwidth characteristics of noise. Signals of interest in this case are submarines operating submerged and producing signals of finite, but relatively stable bandwidth. Noise comes from various sources including surface shipping, wind, waves, marine life, seismic activity, and seismic profilers. The noise sources originating from the sea surface (shipping, wind, waves, and seismic profiling) will be highly unstable in levels and bandwidth due to multipath propagation from near surface source depths. The other noise sources originating below the surface are very minor in level and dominated by the near surface noise sources. This use of bandwidth in the practice of the present invention, therefore, improves detection of submerged signals of interest. 
     It will be understood that various changes in the details, steps and arrangements of parts, which have been herein described and illustrated in order to explain the nature of the invention, may be made to those skilled in the art within the principle and scope of the invention as expressed in the independent claims.