A method for suppressing strong interference in active sonar based on beam reconstruction

Abstract

The application discloses a kind of active sonar strong interference suppression methods based on beam reconstruction, it is related to acoustic signal processing target identification field.To solve the problem of active sonar target detection under strong interference environment, the application proposes a kind of adaptive filtering interference suppression method based on beam reconstruction.The method utilizes the correlation of interference signals in different azimuth beams, estimates the interference components of any beam in the interference range by the way of beam reconstruction, and carries out adaptive filtering on the beam data by taking the interference components as reference signal, to realize interference suppression.The application can effectively suppress the interference of any beam in the interference range, greatly improve the active target detection rate under interference background, and improve the anti-interference capability of sonar equipment.

Description

Technical Field

[0001] This invention relates to the field of acoustic signal processing and target recognition, and more specifically, to a method for suppressing strong interference in active sonar based on beam reconstruction. Background Technology

[0002] Active underwater target detection technology is currently one of the main methods for detecting long-range underwater targets. However, with the continuous development of marine engineering technology and the increasing military and civilian activities at sea, underwater targets are becoming increasingly concealed, the intensity of active targets is continuously decreasing, and the marine environment is becoming increasingly noisy, with the quantity and intensity of interference clutter rising sharply. This has significantly reduced the effectiveness of active target detection. Strong underwater interference sources have become one of the important factors affecting the performance of active sonar. Currently, strong interference sources mainly fall into the following categories:

[0003] First, there is the radiated noise generated by large surface vessels such as warships, merchant ships, and cruise ships. The propeller noise generated by the propulsion systems of large surface vessels is extremely high, and it generates a large-scale interference band during movement, raising the detection background and significantly reducing the detection range and identification confidence of weak underwater targets within the interference range.

[0004] Secondly, the signal suppression effect of active jamming equipment during underwater counter-attacks and counter-attacks. Currently, using jamming equipment to emit high-power jamming signals to suppress the target's location is a typical underwater counter-attack technique. Commonly used jamming methods include frequency jamming and noise jamming, both of which can significantly increase the background detection of the target's location, achieving the effect of "flooding" the target.

[0005] Thirdly, during multi-platform joint detection, there is the impact of co-frequency interference from other platforms' active detection. To reduce detection blind spots, multiple platforms may conduct active detection simultaneously under coordinated formation detection conditions. In this case, the active detection pulses emitted by other platforms will generate extremely strong co-frequency interference to this platform, causing a large detection blind spot in a wide spatiotemporal range and greatly affecting detection efficiency.

[0006] Current methods for active target detection under interference mainly include adaptive beamforming and spatial filtering. These methods mostly utilize the spatial orientation differences between the interference and the target, without considering the time-frequency characteristics of the interference itself. When the target is close to the interference, or even within the main lobe of the interference, the interference suppression effect is affected. If interference suppression is performed using the characteristic differences between the interference and the target, there is no need to consider their relative orientations. Even when the orientations of the interference and the target overlap, a high signal-to-noise ratio active target echo signal under main lobe interference can still be obtained. Summary of the Invention

[0007] To address the shortcomings of the prior art, this invention provides a method for suppressing strong interference in active sonar based on beam reconstruction. This invention can effectively suppress interference in any beam within the interference range, greatly improving the detection rate of active targets under interference conditions and enhancing the anti-interference capability of sonar equipment.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a method for suppressing strong interference in active sonar based on beam reconstruction, comprising the following steps:

[0009] S1: Taking a uniform linear array as an example, establish a received signal model;

[0010] S2: Perform conventional beamforming to generate the interfering beam signal and the target azimuth beam signal respectively; S3: Estimate the interference components at the location of the interference;

[0011] S4: Estimate the interference leakage components of the target's location;

[0012] S5: Perform time-domain conversion on the frequency domain signal to generate the time-domain beam signal of the target azimuth and the time-domain component of interference leakage;

[0013] S6: Using the estimated interference leakage component as a reference signal, adaptive filtering is performed on the target azimuth beam signal to achieve interference suppression;

[0014] S7: Using the transmitted signal as a reference, perform matching processing on the target echo signal and background noise after filtering out interference leakage components;

[0015] S8: Adjust the target's orientation, traverse the area affected by interference, and repeat S2 to S7.

[0016] By adopting the above technical solution, the correlation of interference signals in different azimuth beams is utilized to estimate the interference components of any beam within the interference range through beam reconstruction. Using this as a reference signal, the beam data is adaptively filtered to achieve interference suppression.

[0017] The present invention is further configured such that S1 is specifically as follows:

[0018] For an N-element uniform linear array with element spacing d, assume there is one interference signal and one target signal in space, and the target is within the interference's influence range; where the strong interference originates from θ I The target is incident from azimuth θ0, and it is assumed that the interference / noise received by each array element is uncorrelated with the signal; then

[0019]

[0020] Where 1≤n≤N represents the nth array element, X(ω,n) is the frequency domain expression of the signal received by the nth array element, and ω represents the frequency; S0(ω) is the frequency domain expression of the target signal, S I (ω) is the frequency domain expression of the interference signal, S N (ω,n) represents the frequency domain expression of the noise signal received by the nth array element; and These are the time delay differences between the target signal and the interference signal arriving at the nth array element, respectively, and c is the speed of sound.

[0021] The present invention is further configured such that S2 is specifically as follows:

[0022] Interference beam signals and θ0 azimuth beam signals are generated respectively.

[0023]

[0024] Where Y0(ω) is the azimuth beam signal of θ0, and S' N (ω,n) represents the background noise at the θ0 direction;

[0025]

[0026] Where Y I (ω) represents the interference beam signal, S' N (ω,n) represents the background noise in the direction of interference.

[0027] The present invention is further configured such that S3 specifically is as follows:

[0028] Because within the interference's influence range, the interference suppresses the target signal, meaning the interference component is much larger than the signal and noise components; therefore, in non-null regions, there is generally...

[0029]

[0030] in, Due to NS I (ω) is much larger than the other components, therefore Y can be used I (ω) Estimate S I (ω).

[0031] The present invention is further configured such that S4 is specifically as follows:

[0032] Compare Y0(ω) and Y I Each interference component in (ω) then

[0033]

[0034] That is, using weight vectors For interference signal Y IBy weighting (ω), the interference leakage component at the θ0 azimuth can be estimated.

[0035] The present invention is further configured such that S5 is specifically as follows:

[0036] Generate the time-domain beamline signal at θ0 azimuth and the time-domain component of interference leakage, i.e.

[0037] B0(t)=FFT(Y0(ω))

[0038]

[0039] Where B0(t) and B I (t) represents the θ0 azimuth time-domain beam signal and the interference leakage component, respectively, where t represents time.

[0040] The present invention is further configured such that S6 is specifically as follows:

[0041] Here, the Least Mean Square Error (LMS) algorithm is used, employing a transverse filter structure. Let the filter weight coefficient vector be w(n) in the nth iteration, then the filter weight coefficients in the (n+1)th iteration can be expressed as...

[0042] w(n+1)=w(n)+μu(n)(d * (n)-u H (n)w(n))

[0043] Where μ is the step size, u(n) is the sampling of the θ0 azimuth beam data B0(t), u H (n) is the transpose of u(n), d * (n) represents the estimated interference leakage component B. I (t) is the conjugate of the sampled data; at this point, the error signal can be expressed as

[0044] e(n)=d(n)-w T (n)u(n)

[0045] Where e(n) is the error of the nth iteration, which includes the target echo signal and background noise after filtering out interference leakage components.

[0046] The present invention is further configured such that S7 is specifically as follows:

[0047] Perform matching processing on e(n)

[0048]

[0049] Where Z(τ) represents the θ0 azimuth matching envelope sampling with a time delay of τ. To sample the transmitted signal S L (n) performs time delay conjugation.

[0050] The present invention is further configured such that S8 is specifically as follows:

[0051] By obtaining the warning history composed of Z(τ) at various azimuths within the interference range, and performing target detection on the warning history, information about the target's azimuth and distance can be obtained. Since the interference has been suppressed at this point, the target echo detection performance is better than before interference suppression.

[0052] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects:

[0053] This invention utilizes the correlation of interference signals in beams of different orientations to estimate the interference components of any beam within the interference range through beam reconstruction. Using this estimation as a reference signal, adaptive filtering is applied to the beam data to achieve interference suppression. This method can effectively suppress interference in any beam within the interference range, significantly improving the detection rate of active targets under interference conditions and enhancing the anti-interference capability of sonar equipment. Attached Figure Description

[0054] Appendix Figure 1 This is a schematic diagram illustrating the principle of beam reconfiguration interference suppression.

[0055] Appendix Figure 2 This is a block diagram of the technical solution of the present invention;

[0056] Appendix Figure 3 These are lake test data on the strong interference suppression effect of active sonar obtained based on conventional methods;

[0057] Appendix Figure 4 The data is based on the strong interference suppression effect of active sonar obtained from beam reconstruction. Detailed Implementation

[0058] The following combination Figures 1-4 Detailed descriptions of specific embodiments of the present invention are provided to enable those skilled in the art to more clearly understand how to practice the invention. Although the invention has been described in conjunction with its preferred embodiments, these embodiments are merely illustrative and not intended to limit the scope of the invention.

[0059] like Figure 1 , Figure 2 As shown, an active sonar strong interference suppression method based on beam reconstruction includes the following steps:

[0060] Step 1: Taking a uniform linear array as an example, establish the received signal model. For an N-element uniform linear array with an element spacing of d, assume there is one interfering signal and one target signal in space, and the target is within the interference range. The strong interference originates from θ. I Assuming the target is incident from azimuth θ0, and that the interference / noise received by each array element is uncorrelated with the signal, then...

[0061]

[0062] Where 1≤n≤N represents the nth array element, X(ω,n) is the frequency domain expression of the signal received by the nth array element, and ω represents the frequency. S0(ω) is the frequency domain expression of the target signal, S I (ω) is the frequency domain expression of the interference signal, S N (ω,n) represents the frequency domain expression of the noise signal received by the nth array element. and These are the time delay differences between the target signal and the interference signal arriving at the nth array element, respectively, and c is the speed of sound.

[0063] Step 2: Perform conventional beamforming to generate both the interference beam signal and the θ0 azimuth beam signal.

[0064]

[0065] Where Y0(ω) is the azimuth beam signal of θ0, and S' N (ω,n) represents the background noise at the θ0 direction.

[0066]

[0067] Where Y I (ω) represents the interference beam signal, S” N (ω,n) represents the background noise in the direction of interference.

[0068] Step 3: Estimate the interference component at the location of the interference. Since the interference suppresses the target signal within its influence range, the interference component is much larger than the signal and noise components. Therefore, in non-null regions, there is generally...

[0069] Y I (ω)≈NS I (ω)>>max[S0(ω)W(ω,φ),S” N (ω,n)]

[0070] in, φ=cosθ I -cosθ0. Because NS I (ω) is much larger than the other components, therefore Y can be used I (ω) Estimate S I (ω).

[0071] Step 4: Estimate the interference leakage component at θ0 azimuth. Compare Y0(ω) and Y I Each interference component in (ω) then

[0072]

[0073] That is, using weight vectors For interference signal Y I By weighting (ω), the interference leakage component at the θ0 azimuth can be estimated.

[0074] Step 5: Perform time-domain transformation on the frequency-domain signal to generate the time-domain beamline signal at θ0 azimuth and the time-domain component of interference leakage. That is...

[0075] B0(t)=FFT(Y0(ω))

[0076]

[0077] Where B0(t) and B I (t) represents the θ0 azimuth time-domain beam signal and the interference leakage component, respectively, where t represents time.

[0078] Step 6: Using the estimated interference leakage component as a reference signal, adaptive filtering is performed on the θ0 azimuth beam signal to achieve interference suppression. The Least Mean Square Error (LMS) algorithm is used here, employing a transverse filter structure. Let w(n) be the filter weight coefficient vector in the nth iteration, then the filter weight coefficients in the (n+1)th iteration can be expressed as...

[0079] w(n+1)=w(n)+μu(n)(d * (n)-u H (n)w(n))

[0080] Where μ is the step size, u(n) is the sampling of the θ0 azimuth beam data B0(t), u H (n) is the transpose of u(n), d * (n) represents the estimated interference leakage component B. I (t) is the conjugate of the sampled data. The error signal can then be expressed as...

[0081] e(n)=d(n)-w T (n)u(n)

[0082] Where e(n) is the error of the nth iteration, which includes the target echo signal and background noise after filtering out interference leakage components.

[0083] Step 7: Perform matching processing on e(n) with the transmitted signal as a reference.

[0084]

[0085] Where Z(τ) represents the θ0 azimuth matching envelope sampling with a time delay of τ. To sample the transmitted signal S L (n) performs time delay conjugation.

[0086] Step 8: Adjust θ0, traverse the interference range, and repeat steps 2 to 7. Obtain the warning history composed of Z(τ) at each azimuth within the interference range. By performing target detection on the warning history, the azimuth and distance information of the target can be obtained. Since the interference has been suppressed at this point, the target echo detection performance is better than before interference suppression. See the results below. Figure 3 , Figure 4 The interference is located at 76°, and the target is located at 87°. Figure 3 For routine alert procedures, interference completely obscures the target, making it impossible to extract effective features. Figure 4 The results of the interference suppression technique based on beam reconstruction show that interference at the target azimuth has been suppressed and the signal-to-interference ratio has been significantly improved.

[0087] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the invention by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the invention should be included within the scope of protection of the invention.

Claims

1. A method for suppressing strong interference in active sonar based on beam reconstruction, characterized in that, Includes the following steps: S1: Establish the received signal model; S2: Perform conventional beamforming to generate interference beam signals and target azimuth beam signals respectively; S3: Estimate the interference components at the location of the interference; S4: Estimate the interference leakage components of the target's location; S5: Perform time-domain conversion on the frequency domain signal to generate the time-domain beam signal of the target azimuth and the time-domain component of interference leakage; S6: Using the estimated interference leakage component as a reference signal, adaptive filtering is performed on the target azimuth beam signal to achieve interference suppression; S7: Using the transmitted signal as a reference, perform matching processing on the target echo signal and background noise after filtering out interference leakage components; S8: Adjust the target's orientation, traverse the area affected by interference, and repeat S2 to S7.

2. The active sonar strong interference suppression method based on beam reconstruction according to claim 1, characterized in that, S1 is specifically as follows: For an N-element uniform linear array with element spacing d, assume there is one interference signal and one target signal in space, and the target is within the interference's influence range; where the strong interference originates from... Azimuth incidence, target from Assuming azimuth incidence, and that the interference / noise received by each array element is uncorrelated with the signal; then... Where 1≤n≤N represents the nth array element, X(ω,n) is the frequency domain expression of the signal received by the nth array element, and ω represents the frequency; (ω) is the frequency domain expression of the target signal. (ω) is the frequency domain expression of the interference signal. (ω,n) represents the frequency domain expression of the noise signal received by the nth array element; and These are the time delay differences between the target signal and the interference signal arriving at the nth array element, respectively, and c is the speed of sound.

3. The active sonar strong interference suppression method based on beam reconstruction according to claim 2, characterized in that, S2 is specifically as follows: Generate interference beam signals and respectively Azimuth beam signal, in (ω) is Azimuth beam signal, (ω,n) is Background noise in the direction; in (ω) represents the interference beam signal. (ω,n) represents the background noise in the direction of interference.

4. The active sonar strong interference suppression method based on beam reconstruction according to claim 3, characterized in that, S3 is specifically as follows: Because within the interference's influence range, the interference suppresses the target signal, meaning the interference component is much larger than the signal and noise components; therefore, in non-null regions, there is generally... (ω)≈N (ω)>>max[ (ω)W(ω,φ), (ω,n)] in, φ=cos -cos ; Due to N (ω) is much larger than the other components, therefore it can be used (ω) estimation (ω).

5. The active sonar strong interference suppression method based on beam reconstruction according to claim 4, characterized in that, S4 is specifically as follows: Compare (ω) and Each interference component in (ω) then That is, using weight vectors Interference signals By weighting (ω), we can estimate The interference from the orientation leaked components.

6. The active sonar strong interference suppression method based on beam reconstruction according to claim 5, characterized in that, S5 is specifically as follows: generate The azimuth time-domain beam signal and interference leakage time-domain components, i.e. (t)=FFT( (ω)) in (t) and (t) respectively represent Azimuth time-domain beamforming signal and interference leakage components, where t represents time.

7. The active sonar strong interference suppression method based on beam reconstruction according to claim 6, characterized in that, S6 is specifically as follows: Here, the minimum mean square error algorithm is used, employing a transverse filter structure. Let w(n) be the filter weight coefficient vector in the nth iteration; then the filter weight coefficients in the (n+1)th iteration can be expressed as... w(n+1)=w(n)+μu(n)( (n)- (n)in(n)) Where μ is the step size, and u(n) is... Azimuth beam data Sampling of (t), (n) is the transpose of u(n). (n) represents the estimation of interference leakage components. (t) is the conjugate of the sampled data; at this point, the error signal can be expressed as e(n)=d(n)- (n)u(n) Where e(n) is the error of the nth iteration, which includes the target echo signal and background noise after filtering out interference leakage components.

8. The active sonar strong interference suppression method based on beam reconstruction according to claim 7, characterized in that, The specific details of S7 are as follows: Perform matching processing on e(n) Where Z(τ) represents a time delay of τ. Orientation matching envelope sampling To sample the transmitted signal (n) performs time delay conjugation.

9. The active sonar strong interference suppression method based on beam reconstruction according to claim 8, characterized in that, S8 is specifically as follows: By obtaining the warning history composed of Z(τ) at various azimuths within the interference range, and by performing target detection on the warning history, information about the azimuth and distance of the target can be obtained.

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