Abstract:
A system ( 400 ) for reducing non-acoustic noise includes a primary sensor ( 420 ), at least one secondary sensor ( 410 ), a filter ( 415 ), and a summation unit ( 425 ). The primary sensor ( 420 ) measures pressure and produces a primary pressure signal. The at least one secondary sensor ( 410 ) measures pressure and produce a secondary pressure signal. The filter ( 415 ) processes the secondary pressure signal to produce a filtered pressure signal. The summation unit ( 425 ) subtracts the filtered pressure signal from the primary pressure signal to reduce non-acoustic noise in the primary pressure signal.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     The instant application claims priority from provisional application No. 60/301,104, filed Jun. 26, 2001, and provisional application No. 60/306,624, filed Jul. 19, 2001, the disclosures of which are incorporated by reference herein in their entirety. 
     The instant application is related to co-pending application Ser. No. 10/170,865, entitled “Systems and Methods for Adaptive Wind Noise Rejection” and filed on Jun. 13, 2002, the disclosure of which is incorporated by reference herein. 
    
    
     FIELD OF THE INVENTION 
     The present invention relates generally to systems and methods for acoustic detection and, more particularly, to systems and methods for canceling noise in acoustic detection systems. 
     BACKGROUND OF THE INVENTION 
     A number of conventional systems detect, classify, and track air and ground bodies or targets. The sensing elements that permit these systems to perform these functions typically include arrays of microphones whose outputs are processed to reject coherent interfering acoustic noise sources (such as nearby machinery). Other sources of system noise include general acoustic background noise (e.g., leaf rustling) and wind noise. Both of these sources are uncorrelated between microphones. They can, however, be of sufficient magnitude to significantly impact system performance. 
     While uncorrelated noise is addressed by spatial array processing, there are limits to signal-to-noise improvements that can be achieved, usually on the order of 10*log N, where N is the number of microphones. Since ambient acoustic noise is scenario dependent, it can only be minimized by finding the quietest array location. At low wind speeds, system performance will be limited by ambient acoustic noise. However, at some wind speed, wind noise will become the dominant noise source—for typical scenarios at approximately 5 mph at low frequencies. The primary source of wind noise is the fluctuating, non-acoustic pressure due to the turbulent boundary layer induced by the presence of the sensor in the wind flow field. The impact of an increase in wind noise is a reduction in all aspects of system performance: detection range, probability of correct classification, and bearing estimation. For example, detection range can be reduced by a factor of two for each 3–6 dB increase in wind noise (depending on acoustic propagation conditions). 
     Therefore, there exists a need for systems and methods that can cancel wind noise so as to improve the performance of acoustic detection systems such as, for example, acoustic detection systems employed in vehicle mounted systems for which the effective wind speed includes the relative velocity of the vehicle when the vehicle is in motion. 
     SUMMARY OF THE INVENTION 
     Systems and methods consistent with the present invention address this and other needs by providing a multi-sensor windscreen assembly, and associated wind noise cancellation circuitry, to enable the detection of a desired acoustic signal while reducing wind noise. Multiple reference sensors, consistent with the present invention, may be distributed across a surface of a three dimensional body, such as a sphere, cylinder, or cone and may produce a response signal that corresponds to a net pressure acting on the three dimensional body. A primary sensor may further be located within the three dimensional body to sense acoustic pressure signals and non-acoustic pressure disturbances (e.g., wind noise). A finite impulse response (FIR) filter may adaptively filter the response signal from the multiple reference sensors to produce a filtered response. The filtered response may, in turn, be subtracted from a signal from the primary sensor to produce a signal that contains reduced non-acoustic disturbances. The filter may employ a least-means-square (LMS) algorithm for adjusting coefficients of the FIR filter to reduce the non-acoustic pressure disturbances. Systems and methods consistent with the present invention, thus, using an adaptive filtering algorithm, cancel wind noise from an acoustic signal so as to improve the performance of acoustic detection systems. 
     In accordance with the purpose of the invention as embodied and broadly described herein, a method for reducing non-acoustic noise includes measuring pressure at a primary sensor to produce a primary pressure signal; measuring pressure at least one secondary sensor to produce a secondary pressure signal; filtering the secondary pressure signal to produce a filtered pressure signal; and subtracting the filtered pressure signal from the primary pressure signal to reduce non-acoustic noise in the primary pressure signal. 
     In another implementation consistent with the present invention, a method of measuring fluid pressure includes measuring fluid pressure inside a windscreen to produce a measurement signal; inferring a net fluid pressure acting on the windscreen, the net fluid pressure comprising acoustic and non-acoustic pressure; estimating a component of the non-acoustic pressure that is correlated with the net fluid pressure; and eliminating the estimated component of non-acoustic pressure from the measurement signal. 
     In yet another implementation consistent with the present invention, a method for canceling disturbances from a sensor signal includes sensing disturbances at first and second sensors, the first sensor producing a first signal and the second sensor producing a second signal; adaptively filtering the first signal to produce a filtered signal; and subtracting the filtered signal from the second signal to cancel the disturbances from the second signal. 
     In a further implementation consistent with the present invention, a windscreen includes a three dimensional body comprising at least one surface; a first sensor located within the three dimensional body; and a plurality of second sensors distributed on the at least one surface of the body, the sensors configured to sense forces acting upon the body. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and, together with the description, explain the invention. In the drawings, 
         FIG. 1  illustrates an exemplary multi-sensor assembly consistent with the present invention; 
         FIG. 2  illustrates an exemplary multi-sensor assembly with a spherical windscreen and equatorially distributed sensors consistent with the present invention; 
         FIG. 3  illustrates another exemplary multi-sensor assembly consistent with the present invention; 
         FIG. 4  illustrates an exemplary noise cancellation system consistent with the present invention; 
         FIG. 5  illustrates an exemplary adaptive finite impulse response (FIR) filter consistent with the present invention; and 
         FIG. 6  is a flowchart that illustrates an exemplary process for wind noise cancellation consistent with the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     The following detailed description of the invention refers to the accompanying drawings. The same reference numbers in different drawings may identify the same or similar elements. Also, the following detailed description does not limit the invention. Instead, the scope of the invention is defined by the appended claims. 
     Systems and methods, consistent with the present invention, provide mechanisms that adaptively reduce noise in multiple signals received from a multi-sensor device. Multiple reference sensors, consistent with the present invention, may be distributed across a surface of a three dimensional body, such as a sphere, cylinder, or cone. A primary sensor may be located within the three dimensional body. Fluid pressures acting on the reference sensors may be combined to infer a net pressure acting on the three dimensional body, with the net pressure being correlated with the non-acoustic pressure acting over the entire three dimensional body. The net pressure acting on the three-dimensional windscreen is the source of the non-acoustic pressure acting on the primary sensor at a reduced level inside of the windscreen. The reference sensors may measure the acoustic signal, together with the non-acoustic wind pressure, and the reference sensor measurements may be passed through noise cancellation circuitry that estimates a component of the wind noise that is correlated with the primary sensor output. This correlated component may be subtracted from the primary sensor output to provide a reduced noise sensor output. The noise cancellation circuitry may include a finite impulse response (FIR) filter whose parameters are adaptively adjusted using a least-means-square (LMS) algorithm. 
     Exemplary Multi-Sensor Assembly 
       FIG. 1  illustrates an exemplary multi-sensor assembly  100  consistent with the present invention. Multi-sensor assembly  100  may include a windscreen  105  coupled to a support structure  110 . As illustrated, windscreen  105  may be configured as a three dimensional sphere. Windscreen  105  may, alternatively, be configured as a three dimensional cylinder, cone, or other shape (not shown). Windscreen  105  may further be constructed of a rigid or semi-rigid material. Windscreen  105  may also be constructed of a permeable or non-permeable material. For example, windscreen  105  may be constructed of foam and, thus, would be semi-rigid and permeable to fluids such as air or water. 
     As shown in  FIG. 1 , multiple reference sensors (reference sensor  1   115 - 1  through reference sensor N  115 -N) may be distributed on a surface of windscreen  105 . As further illustrated in  FIG. 2 , the multiple sensors may be distributed around an equator of spherical windscreen  105 . One skilled in the art will recognize, also, that other sensor distributions may be possible. For example, sensors may be distributed at icoshedral points (not shown) on the surface of spherical windscreen  105 . Distribution of the sensors across a surface of windscreen  105  can depend on the shape of the windscreen (e.g., spherical, cylindrical, conical) and the particular airflow anticipated upon the windscreen. Multi-sensor assembly  100  may additionally include a primary sensor  120  ( FIG. 1 ) positioned within the approximate center of windscreen  105 . 
     Each of the multiple reference sensors  115  may include any type of conventional transducer for measuring force or pressure. A piezoelectric transducer (e.g., a microphone) is one example of such a conventional transducer. In some embodiments of the invention, each of the multiple reference sensors  115  may measure acoustic and non-acoustic air pressure. 
       FIG. 3  illustrates another exemplary multi-sensor assembly  300  consistent with the present invention. Multi-sensor assembly  300  may include a windscreen  305  coupled to a support structure  310 . As illustrated, windscreen  305  may be configured as a three dimensional sphere. Windscreen  305  may be constructed of materials similar to those described above with respect to the exemplary multi-sensor assembly of  FIG. 1 . Multiple reference sensors (reference sensor  315 - 1  through reference sensor  315 -N) may be distributed on a surface of windscreen  305  so as to couple windscreen  305  to support structure  310 . Movement of windscreen  305  due to fluid pressure against a surface of the windscreen, thus, induces signals in one or more of reference sensors  315 - 1  through  315 -N as force from the fluid pressure is coupled from windscreen  305 , through the reference sensors, and onto support structure  310 . Multi-sensor assembly  300  may additionally include a primary sensor  320  positioned within the approximate center of windscreen  305 . 
     Exemplary Active Noise Cancellation System 
       FIG. 4  illustrates an exemplary system  400  in which systems and methods, consistent with the present invention, may be implemented for actively canceling wind noise sensed at a multi-sensor device, such as multi-sensor assembly  100  or  300 . System  400  may be implemented in either software or hardware and may include an adaptive FIR filter  415 , a summation unit  425  and a least-means-square (LMS) adaptive algorithm  430  which may be implemented in either software or hardware. Active noise cancellation system  400  may actively cancel disturbances (d)  405  that characterize acoustic and non-acoustic noise impinging on the outer surface of windscreen  105  or  305 . The disturbances (d)  405  act through the impulse response system S  410  to produce a net reference sensor response s(k). For example, impulse response system S  410  may form a coherent sum of all reference sensor (e.g., reference sensor  1   115 - 1  through reference sensor N  115 -N) responses. The net reference response s(k) is dominated by non-acoustic noise relative to acoustic noise. A primary sensor response t(k) results from disturbance (d)  405  acting through the impulse response system T  420 , which characterizes the action of primary sensor  120  or  320 . The action of windscreen  105  or  305  does not completely remove the non-acoustic wind noise from the primary sensor response t(k). 
     Adaptive finite impulse response (FIR) filter  415  may include a conventional digital FIR filter, and may filter the net reference sensor response s(k) received from reference sensors  115  or  315  to produce a filtered response y(k). The filtered response y(k) may be subtracted from the by primary sensor response t(k), at summation unit  425 , to produce a residual primary sensor response e(k). The residual primary sensor response e(k) represents the noise reduced output of system  400 . This noise-reduced output may be used in a conventional acoustic detection system (not shown) for detecting, classifying, and tracking objects or targets. 
     The net reference sensor response s(k) and the residual primary sensor response e(k) may be input to a conventional least-means-square (LMS) adaptive algorithm  430  for adaptively updating filter coefficients of filter  415 . The adaptive nature of filter  415  accommodates changing conditions, such as, for example, changing wind speed, temperature, or barometric pressure. The LMS algorithm for updating the filter coefficient vector W may be given by:
 
 W ( k+ 1)= W ( k )+2 *mu*e ( k )* S ( k )  Eqn. (1)
 
where W(k) is a vector of filter coefficients at time step k;
 
     mu is an adaptation constant; 
     e(k) is the residual primary sensor response at time step k; and 
     S(k) is a vector of net reference sensor input samples at time step k. 
     For an adaptive FIR filter  415  of N filter coefficients, the vector quantities are:
 
 W ( k+ 1)=[ w   0   w   1   w   2    . . . w   N-1 ] T   Eqn. (2)
 
 S ( k )=[ s ( k ) s ( k− 1) . . .  s ( k−N+ 1)] T   Eqn. (3)
 
     The filter coefficients of vector W are adjusted by the LMS algorithm  430  so as to reduce the remaining non-acoustic noise in the primary sensor response t(k) that is correlated with the net reference sensor response s(k). To accomplish this, the LMS algorithm  430  correlates the residual primary sensor response e(k) with the net reference sensor response s(k). The correlated result is multiplied by the adaptation constant mu and then used to adjust the filter coefficients of adaptive filter  415 . The LMS algorithm can be iterated, with the objective being convergence to filter coefficients that minimize the average power in the residual primary sensor response e(k). As one skilled in the art will recognize, the choice of mu determines the rate of convergence for the LMS algorithm, and also determines how well the algorithm tracks the optimum solution (i.e., minimum mean-square error) under steady-state conditions. One skilled in the art may choose an appropriate value of mu to achieve a desired tradeoff between a rate of convergence for the LMS algorithm and minimization of mean-square error. 
       FIG. 5  illustrates exemplary components of adaptive FIR filter  415 . Filter  415  may produce a filtered response y(k) that may include the weighted sum of the current, and past, net reference sensor response s(k) inputs. Filter  415  may include multiple delay elements (Z −1 )  505  and a summation unit  510  for filtering the net reference sensor response s(k) according to filter coefficients {w 0 , w 1 , w 2  . . . , w N-1 } that are adaptively updated by LMS algorithm  430 . As shown, the net reference sensor response s(k) may be successively delayed by each delay element  505  of filter  415 . Before and after each delay element  505 , a filter coefficient w may be multiplied by the delayed net reference sensor response s(k). The weighted current, and past, net reference sensor inputs may then be summed by summation unit  510 . The filtered response y(k) from filter  415 , thus, may correspond to the following:
   y ( k )= w   0   s ( k )+ w   1   s ( k− 1)+ w   2   s ( k− 2)+ . . . + w   N   s ( k−N+ 1)  Eqn. (4) 
     
       
         
           
             
               
                 
                   
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     Exemplary Adaptive Wind Cancellation Process 
       FIG. 6  illustrates an exemplary process, consistent with the present invention, for canceling wind noise contained in signals received from multiple sensors, such as the sensors of multi-sensor assembly  100  or  300 . The exemplary process may begin, at time step k=0, with the filtering of the net reference sensor response s(k) using adaptive FIR filter  415 . Filter  415  may produce the filtered response y(k) [act  605 ] according to Eqn. (5) above. The filtered response y(k) may then be subtracted from the primary sensor response t(k) to produce the residual primary sensor response e(k) [act  610 ]:
   e ( k )= t ( k )− y ( k )  Eqn. (6) 
Summation unit  425  may, for example, be used to subtract the filtered response y(k) from the primary sensor response t(k) to generate the residual primary sensor response e(k). e(k), as described previously, represents the noise reduced output of system  400  and may be used in acoustic detection systems. The FIR filter  415  coefficients W may then be updated using LMS adaptive algorithm  430  [act  615 ]. For example, the LMS algorithm of Eqns. (1), (2) and (3) above may be used. At time step k=k+1, the process may return to act  605 .
 
     CONCLUSION 
     Systems and methods, consistent with the present invention, provide mechanisms that enable the detection of a desired acoustic signal incident at a multi-sensor windscreen assembly while reducing wind noise. The multi-sensor windscreen assembly may include multiple sensors distributed across a surface of a three dimensional windscreen, such as a sphere, cylinder, or cone, and may produce a response signal that corresponds to a net pressure acting on the three dimensional body. A primary sensor may further be located within the three dimensional body to sense acoustic pressure signals and non-acoustic pressure disturbances (e.g., wind noise). A finite impulse response (FIR) filter may adaptively filter the response signal from the multiple reference sensors to produce a filtered response. The filtered response may, in turn, be subtracted from a signal from the primary sensor to produce a signal that contains reduced non-acoustic disturbances. The filter may employ a least-means-square (LMS) algorithm for adjusting coefficients of the FIR filter to reduce non-acoustic pressure disturbances, thus, canceling wind noise from an acoustic signal so as to improve the performance of acoustic detection systems. 
     The foregoing description of exemplary embodiments of the present invention provides illustration and description, but is not intended to be exhaustive or to limit the invention to the precise form disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practice of the invention. For example, while certain components of the invention have been described as implemented in hardware and others in software, other configurations may be possible. Also, while series of acts have been described with regard to  FIG. 6 , the order of the acts may be altered in other implementations. No element, step, or instruction used in the description of the present application should be construed as critical or essential to the invention unless explicitly described as such. The scope of the invention is defined by the following claims and their equivalents.