Abstract:
A plurality of sensors and a digital adaptive tuning filter bank are used in extracting a desired emitted signal embedded in a noisy environment. By monitoring noise statistics of the sensor signals, the digital adaptive tuning filter bank automatically adjusts its upper (to eliminate strong tonals) and lower (to eliminate background noise) thresholds to obtain a discovery frequency band. The filter bank is designed by examining the discovery band across the sensors and over a predefined period of time. The method described significantly reduces the possibilities of matching self-noise transients (unwanted signals) and thus minimizes the false alarm rate in emitted signal recognition.

Description:
BACKGROUND OF THE INVENTION 
   The detection and measurement of short, transient pulse modulated signals emitted from an acoustic signal source such as, for example, an active sonar source has been the subject of recent efforts in the array signal processing field. However, much of this effort has not addressed three important practical issues: (1) how low the signal to noise ratio is (less than −10 dB); (2) the existence of self-noise transient signals; and (3) the presence of strong tonals. Therefore, prior art signal models and detection methods have not performed satisfactorily when applied to detecting, for example, actual underwater acoustic signals. 
   In an actual underwater environment, machinery installed on surface ships and submarines, for example, inevitably generates a variety of harmonic resonance signals. These signals are called tonals. Tonals detected by an acoustic receiver can be much stronger than the emitted signal. Due to various underwater biological effects and flow induced resonances, self-noise transient signals can also interfere with the performance of the acoustic receiver. 
   Experimental data indicates that self-noise transient signals appear very similar to the emitted signal in the time domain. Some known characteristics of self-noise transient signals include: (1) their arrival time can be modelled using Poisson distribution; (2) their frequency is randomly distributed; and (3) their duration varies from a few milliseconds to a few hundred milliseconds. 
   Existence of self-noise transient signals is a major factor which contributes to the degradation of the false alarm rate performance of an underwater acoustic receiver. The duration of a pulse modulated emitted signal can be as short as a few milliseconds and as long as one second. Multipath delays also interfere with the emitted signal. Since it is desirable to provide a long range detection capability, the received emitted signal is often weak compared to environmental (noise) signals since the signal to noise ratio is low. In addition, the received emitted signal is corrupted by background “pink” noise as will be understood by those skilled in the art. 
   In order to detect the emitted signal and to measure its characteristics, it is necessary to enhance the aforenoted signal to noise ratio so a the reconstituted signal can be reliably recognized and measured. Since apriori knowledge of the emitted signal is not available, conventional match filter techniques will not aid in the enhancement of the signal to noise ratio. A number of developed detection algorithms have been proposed based on a statistical hypothesis test method. Unfortunately, the statistical models of self-noise transient signals are not known. Therefore, such methods are not useful. 
   The object of the present invention is to provide a method for detecting emitted signals which enhances the signal to noise ratio of the signals in an actual noisy environment, and which method is amenable to real time implementation. 
   SUMMARY OF THE INVENTION 
   This invention contemplates a method for detecting emitted acoustic signals including signal to noise ratio enhancement, wherein the emitted signals are distinguished from self-noise transient signals. Since the emitted signals are unsteady, the present invention features an adaptive filter technique having the capability to track the emitted signal and to enhance the signal to noise ratio, as is desired. An adaptive tuning filter bank tracks all possible signal sources and extracts the potential emitted signals. The output of the adaptive filter bank is identified for further emitted signal classification. The filter bank uses frequency domain information to track the emitted signals and is especially useful when the signal to noise ratio is very low which prohibits conventional adaptive filter techniques from being used for the desired purposes. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
       FIG. 1  is a block diagram/flow chart illustrating the method of the invention. 
       FIG. 2  is a flow chart particularly illustrating a tonal and noise suppression feature of the invention illustrated generally in  FIG. 1 . 
       FIG. 2A  is a magnitude histogram plot provided in accordance with the method of the invention. 
       FIG. 3  is a flow chart particularly illustrating a filter bank illustrated generally in  FIG. 1 . 
       FIGS. 4 ,  5  and  6  are diagrammatic representations illustrating digitized, filtered sensor signals which are processed by the method of the invention. 
       FIGS. 7 ,  8  and  9  are diagrammatic representations illustrating the magnitude spectra of the digitized signals illustrated in  FIGS. 4 ,  5  and  6  respectfully. 
       FIG. 10  is a diagrammatic representation of a total current histogram provided in accordance with the method of the invention. 
       FIGS. 11 ,  11 A;  12 ,  12 A; and  13 ,  13 A are diagrammatic representations of reconstituted signals provided by a filter bank shown generally in  FIG. 1  and particularly shown in  FIG. 3 . 
   

   DETAILED DESCRIPTION OF THE INVENTION 
   With reference to  FIG. 1 , a plurality of emitted acoustic signals, shown for purposes of illustration as three in number, are designated as S 1 , S 2  and S 3 . Signals S 1 , S 2  and S 3  are short, transient pulse modulated signals emitted from a signal source such as, for example, an active sonar source. Signals S 1 , S 2  and S 3  are sensed by sensors  2 ,  4  and  6 , respectively, which provide corresponding analog output acoustic signals. 
   The analog output acoustic signals from sensors  2 ,  4  and  6  are filtered by filters  8 ,  10  and  12 , respectively. Filters  8 ,  10  and  12  are anti-aliasing low pass filters and provide band selected/limited output signals which are digitized by analog to digital (A/D) converters  14 ,  16  and  18 , respectively. The digitized signals provided by A/D converters  14 ,  16  and  18  are graphically represented in  FIGS. 4 ,  5  and  6 , respectively. In this regard, reference is made to  FIG. 4  which illustrates the self-noise, transient portion of the digitized signals. The digitized signals are applied to a central processing unit  20  for processing according to the invention as will be next described. 
   Thus, the digitized signals from A/D converters  14 ,  16  and  18  are windowed and transformed to a frequency domain by an overlapping fast Fourier transform (FFT) method as at  22 ,  24  and  26 , respectively. Windowing is required to limit bin spreading in the frequency domain and overlapping is required to avoid time domain aliasing for reconstituted emitted signals. Thus, frequency domain signals are provided at  22 ,  24  and  26 , and are designated as FB 1 , FB 2  and FB 3 , respectively. The frequency domain signals are graphically illustrated in  FIGS. 7 ,  8  and  9 . In this regard, reference is made to  FIG. 7  which illustrates the tonal and “pink” background noise characteristics of the windowed and transformed signals. 
   In order to determine the upper levels (to eliminate strong tonals) and the lower levels (to discriminate emitted signals S 1 , S 2  and S 3  from background noise) thresholds, frequency domain signals FB 1 , FB 2  and FB 3  are processed for tonal and noise suppression at  28 ,  30  and  32 , respectively. The tonal and noise suppression processing is more particularly illustrated in  FIG. 2 , wherein, for example, frequency domain signal FB 1  is shown as being processed for tonal and noise suppression at  28 . 
   Thus, the magnitude spectrum of signal FB 1  is converted into a magnitude histogram plot at  34  and as illustrated in  FIG. 2A . Since background noise exists for most frequency bins, the number of occurrences is concentrated in the lower portion of the histogram. It is known that background noise has a Gaussian distributed probability density function. The rules for the lower and upper level thresholds are derived at  35  ( FIG. 2 ) from the magnitude histogram shown in  FIG. 2A . The magnitude spectrum is filtered in the upper and lower thresholds resulting in a discovery band (1/0 bit pattern). A single sensor spectral histogram is generated at  37 . Tonal and noise suppression is likewise performed for all of the frequency domain signals. To take into account the coherence property and multi-path delay effects of emitted signals S 1 , S 2  and S 3 , the resulting discovery band of each of the respective sensors  2 ,  4  and  6  is integrated over both time and spatial (across sensors) domains, since the emitted signals have a strong correlation in both domains. 
   The tonal and noise suppressed outputs at  28 ,  30  and  32  ( FIG. 1 ) are summed at  36  for developing a current spectral histogram at  38 . The current spectral histogram at  38  is summed at  40  with a previous spectral histogram at  42  to provide a total spectral histogram at  44 . 
   A frequency domain window design is established at  46  with reference being made to  FIG. 10 , which illustrates the frequency domain window design. Thus, the frequency domain window can be designed according to the number of occurrences of certain frequency bins. The self-noise transient signals are not correlated among sensors in both the spacial and frequency domains. Therefore, in a high resolution spectrum such as herein encountered, the frequency bins of self-noise transient signals are likely to be ignored by the frequency domain window design. In this regard, and with reference to  FIG. 1 , the frequency domain window design shown in  FIG. 10  is processed by a filter bank  48  as are frequency domain signals FB 1 , FB 2  and FB 3 . Reference is made to  FIG. 3  which more particularly shows the processing effected by filter bank  48 . 
   Thus, signals FB 1 , FB 2  and FB 3  are multiplied at  50 ,  52  and  54 , respectively, by the frequency domain window. In other words, the time domain and filter bank outputs for all of the sensors  2 ,  4  and  6  can be obtained by multiplying the corresponding frequency domain signals by the desired frequency domain windows and then taking inverse fast Fourier transforms (IFFT) at  56 ,  58  and  60 , respectively. De-windowing is performed at  62 ,  64  and  66  and time domain overlapping is performed at  68 ,  70  and  72 , whereby the accuracy of reconstituted output signals at  68 ,  70  and  72  is maintained. 
   The reconstituted signal at  68  is illustrated in  FIG. 11  and in  FIG. 11A , which is an extension of  FIG. 11 ; the reconstituted signal at  70  is illustrated in  FIG. 12  and in  FIG. 12A , which is an extension of  FIG. 12 ; and the reconstituted signal at  72  is illustrated in  FIG. 13  and in  FIG. 13A , which is an extension of  FIG. 13 . The reconstituted signals are processed for time domain cross-correlation at  74 , shown in  FIG. 1 , and the cross-correlated reconstituted signal thereby provided is identified at  76  and measured by a measurement unit  78 . 
   It will be recognized that the advantages of the described method, which includes a digital adaptive tuning filter bank, include the ability to significantly increase the signal to noise ratio and to reduce the possibility of matching self-noise transient signals. This simplifies the design task for an emitted signal recognition unit and minimizes false alarm rates, as are likely to occur. 
   In summary, emitted signals S 1 , S 2  and S 3  are sensed by sensors  2 ,  4  and  6 , respectively, and are thereafter digitized as shown in  FIGS. 4 ,  5  and  6 . Their magnitude spectra are demonstrated in  FIGS. 7 ,  8  and  9 , respectively. In this regard, it is to be noted that the signal to noise ratio for all sensors is less than −10 dB. Two of the outputs of the digital adaptive tuning filter bank for all three sensors are shown in  FIGS. 11 ,  12  and  13  and in  FIGS. 11A ,  12 A and  13 A. It will be discerned that  FIGS. 11 ,  12  and  13  portray steady weak tonals and  FIGS. 11A ,  12 A and  13 A are the desired emitted signals. 
   Although the invention has been shown and described with only three emitted signals S 1 , S 2  and S 3 , any number of signals may be processed by the method of the invention as will now be understood by those skilled in the art. 
   With the above description of the invention in mind, reference is made to the claims appended hereto for a definition of the scope of the invention.