Differential characteristic-based passive target detection distance evaluation method under strong interference

Abstract

The invention relates to the technical field of sonar signal processing, in particular to a difference characteristic-based passive target detection distance evaluation method under strong interference, which specifically evaluates the influence of strong interference on a target, and introduces an interference level, an interference suppression gain and a characteristic detection threshold into a traditional sonar equation. The influence of strong interference on the passive sonar forecast distance is quantified. The method comprises the following steps: firstly, calculating a propagation loss curve according to hydrological conditions, and then calculating an interference level, an interference suppression gain, interference propagation loss, and various parameters such as a feature detection index and a feature detection threshold obtained by a multi-dimensional feature joint detection method according to set parameters of strong interference and a target; and optimizing a traditional passive sonar equation to obtain an optimized high-quality factor, and obtaining a corrected passive sonar forecast distance in combination with the propagation loss curve.

Description

Technical Field

[0001] This invention relates to the field of sonar signal processing technology, specifically to a method for evaluating the detection range of passive targets under strong interference based on differential characteristics. Background Art

[0002] In modern maritime tactical research, sonar studies are receiving increasing attention. Among the key performance indicators, sonar range is the most crucial. Knowing the range of one's own sonar in advance in a specific sea area can significantly enhance its effectiveness. However, in environments with strong interference, traditional sonar equations suffer from two main problems: 1) They only consider uniform isotropic noise, but strong interference alters the background noise, transforming it from uniform isotropic noise into directional, strong background noise; 2) They only consider energy detectors under constant false alarm rate (CFAR) conditions, neglecting the processing gain from characteristic detectors in environments with strong interference. These two issues lead to insufficient range prediction performance, or even failure, of traditional sonar equations in environments with strong interference. Summary of the Invention

[0003] The purpose of this invention is to provide a method for evaluating the detection range of passive targets under strong interference based on differential characteristics. This method proposes a method for evaluating the effective range of passive sonar under strong interference, which covers traditional elements such as sound source level and propagation loss as well as new elements such as interference level, interference suppression gain, and feature detection threshold, effectively improving the performance evaluation capability of sonar equipment in strong interference environments.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a method for evaluating the detection range of passive targets under strong interference based on differential characteristics, comprising the following steps:

[0005] S1, Strong interference spectral level SL at a specified frequency under strong interference environment. j The target spectral level (SL) and the relative distance between strong interference and the receiving array;

[0006] S2. Calculate the marine ambient noise level NL at a specified frequency, where NL decreases with frequency at 6dB / octave, and has an initial value of 64dB at 1kHz.

[0007] S3. Obtain the temperature, depth and salinity parameters of the sea area using a temperature, depth and salinity meter, calculate the sound velocity gradient, and generate a propagation loss curve at a specified frequency using a sound propagation model in combination with seabed medium parameters.

[0008] S4. Calculate the strong interference propagation loss TL based on the propagation loss curve and the strong interference distance. j ;

[0009] S5. Calculate the receiver directivity index DI. When the receiver array is undistorted or the model is mismatched, DI = 10lgM, where M is the number of array elements.

[0010] S6. Based on a multi-dimensional differential feature joint detection method, calculate the feature detection threshold DT. c Specifically, it includes:

[0011] (a) Extract the high and low frequency energy ratios, narrowband line spectra, and spatial energy distribution differences between the target and background noise;

[0012] (b) Construct the probability density distribution of the joint detection statistic to obtain the target mean μ. s and variance σ s The mean μ of the background noise, respectively n and variance σ n ;

[0013] (c) Generate ROC curves by using the detection probability and false alarm probability under different signal-to-noise ratios to determine the feature detection index. and calculate

[0014]

[0015] Where B is the processing bandwidth and T is the integration time;

[0016] S7. Calculate the interference suppression gain IG(θ) of the broadband detection algorithm, satisfying...

[0017]

[0018] in, and These represent the beam energy at the target azimuth before and after suppression of the interference energy;

[0019] S8. Obtain the energy distribution map of the strong interference and the target's location in the background energy distribution map when strong interference exists. and ;

[0020] S9. Optimize the passive sonar equations to generate high-quality factors.

[0021]

[0022] Among them, BL j (θ) represents the beam domain interference and noise level, and its expression is:

[0023]

[0024] The target detection prediction distance is calculated by combining the propagation loss curve.

[0025] Preferably, the sound propagation model in S3 is the Kraken model, and the propagation loss curve is generated by jointly calculating the sound velocity gradient and the seabed medium parameters.

[0026] Preferably, the multidimensional differential feature joint detection method in S6 includes feature fusion and optimized threshold selection under the maximum a posteriori probability criterion.

[0027] Preferably, the calculation of the interference suppression gain in S7 employs a super-resolution algorithm. and The energy difference between conventional beamforming and super-resolution beamforming is determined respectively.

[0028] Preferably, the calculation of the quality factor FOM in S9 further includes the ambient noise level NL, the receiver directivity index DI, and the interference propagation loss TL. j The coupling correction term.

[0029] Preferably, the detection index d of the multidimensional differential feature joint detection method j The false alarm probability threshold and detection probability threshold are correlated with the ROC curve, and when the false alarm probability ≤ 0.1 and the detection probability ≥ 0.9, d j The value range is from 1.2 to 1.6.

[0030] Preferably, the calculation of the marine environmental noise level NL includes a towed platform self-noise correction term with a correction amount of 3dB.

[0031] Preferably, the calculation of the receiver directivity index DI includes an additional attenuation term caused by array distortion or model mismatch, and the attenuation amount is determined by actual measurement or simulation.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] This method specifically assesses the impact of strong interference on targets and analyzes the effects of signal processing methods with interference suppression capabilities on interference and target detection. It introduces interference level, interference suppression gain, and feature detection threshold into the traditional sonar equations to characterize the impact of interference on detection and the suppression effect of signal processing methods on interference. This optimizes the traditional sonar equations to meet the performance prediction requirements for range under strong interference. Specifically, the interference level characterizes the new noise background formed when strong interference propagates through the underwater acoustic channel and reaches the detection platform, combined with the ambient noise at the detection platform. The interference suppression gain characterizes the spatial suppression capability of the interference suppression method against strong interference. The feature detection threshold characterizes the detection performance of a feature detection method based on the combined multidimensional differences between strong interference and target features. Using these elements, the impact of strong interference on the traditional sonar equations can be effectively quantified, thereby effectively estimating the range of passive sonar targets under strong interference. Attached Figure Description

[0034] Figure 1 Flowchart of a method for assessing the effective range of passive sonar under strong interference;

[0035] Figure 2 A schematic diagram of the test route design to verify the improved prediction performance of passive sonar equations under strong interference;

[0036] Figure 3 (a) shows the sound velocity gradient during the experiment, obtained by hydrological environmental measurements using a temperature, depth, and salinity meter; (b) shows the propagation loss curve at 240 Hz, calculated using the Kraken model.

[0037] Figure 4 In the table, (a) represents the target signal amplitude, (b) represents the target signal source level, (c) represents the detection result of the conventional method, and (d) represents the detection result of the feature detection method.

[0038] Figure 5 The propagation loss curve of the target signal;

[0039] Figure 6 In the middle (a), the mean value μ of the jointly detected targets is calculated. s and variance σ s Mean μ of background noise n and variance σ n (a) is a schematic diagram; (b) is a multi-dimensional feature joint detection performance curve;

[0040] Figure 7 The simulation results show the interference suppression gain.

[0041] Figure 8 This is a background energy distribution map showing the energy at the location of the strong interference and the target when strong interference is present.

[0042] Figure 9 This represents the predicted range results of traditional sonar equations and passive sonar range assessment methods under strong interference. Detailed Implementation

[0043] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings, so that those skilled in the art can more clearly understand how to practice the present invention. Although the present 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.

[0044] (I) Implementation process

[0045] This invention provides a method for evaluating the detection range of passive targets under strong interference based on differential characteristics, combined with... Figure 1 This method includes the following steps:

[0046] S1. Under strong interference environment, the strong interference spectrum level SL at a specified frequency is input. j The target spectral level SL and the distance between the strong interference and the receiving array.

[0047] S2. Calculate the marine ambient noise level NL at a specified frequency: The marine ambient noise level can be approximated as NL=64dB@1kHz, with attenuation every 6dB / octave.

[0048] S3. Calculate the sound velocity gradient by measuring parameters such as temperature, depth, and salinity of the sea area using a temperature, depth, and salinity meter, and then use the Kraken model to calculate the propagation loss curve at a specified frequency using the sound velocity gradient and parameters such as the selected seabed medium.

[0049] S4. Calculate the strong interference propagation loss TL using the propagation loss curve and the distance between the strong interference source and the receiving array. j .

[0050] S5. Calculate the receiver directivity index. : When there is no array distortion / model mismatch, ,in Indicates the number of array elements.

[0051] S6, Calculate the feature detection threshold DT c First, the probability density distribution of the optimal test metric for the joint detection method based on multi-dimensional difference features such as high- and low-frequency energy ratio characteristics, narrowband line spectrum characteristics, and spatial energy distribution difference characteristics under the maximum a posteriori probability criterion is calculated, with a mean of μ and a variance of σ. Then, the target mean μ of the joint detection is obtained. s and variance σ s The mean μ of the background noise, respectively n and variance σ n Furthermore, the joint detection performance of multidimensional features under different signal-to-noise ratios is calculated. Given different thresholds T, the feature detection index is calculated:

[0052]

[0053] The detection probability and false alarm probability curves under different signal-to-noise ratios can be obtained, i.e., the multi-dimensional feature joint detection performance curve (ROC curve); and then the feature detection threshold can be obtained.

[0054]

[0055] Where d j It is related to the detection probability and false alarm probability, where B is the processing bandwidth and T is the integration time.

[0056] S7. Calculate the interference suppression gain of the broadband detection algorithm used. :

[0057]

[0058] in and These represent the beam energy at the target's azimuth before interference suppression and the residual beam energy at the target's azimuth after interference suppression, respectively. For broadband detection using super-resolution algorithms, This can be approximated as the difference between the interference energy when using conventional beamforming and the interference energy at the target's azimuth. It can be approximated as the difference between the interference energy when the super-resolution algorithm is used and the interference energy at the target's location.

[0059] S8. Calculate the energy distribution at the location of the strong interference and the target in the background energy distribution map when strong interference is present. and .

[0060] S9. By adding parameters such as interference level, interference suppression gain, and feature detection threshold obtained in the above steps to the traditional sonar equation, the quality factor of the updated passive sonar equation is obtained. Combined with the propagation loss curve, the predicted passive target detection range based on this method is obtained: The quality factor of the optimized passive sonar equation is:

[0061]

[0062] in The beam domain interference and noise level are expressed as follows:

[0063] .

[0064] (II) Verifying the feasibility of this method based on "(I) Implementation process"

[0065] Figures 2 to 9 As shown, verification of this method requires the cooperation of the main ship, the target ship, and the jamming ship. The main ship sails from point A to point A' at a speed of 3 knots, during which the passive detection mode is activated; the jamming ship floats without power at point B, and the jamming equipment emits a strong suppressive jamming signal; the target ship floats without power at point C, and emits simulated target radiated noise signals of different source levels as instructed; the main ship uses an equally spaced towed array with an array spacing of 1.5m.

[0066] Calculate the propagation loss TL of strong interference based on the propagation loss curve and the strong interference distance. j Appendix Figure 4 The results of this experiment show the signal source-level changes and the detection results of two algorithms, among which... Figure 4 In the figure, (a) represents the target signal amplitude. Figure 4 (b) in the diagram represents the target signal source level. Figure 4 (c) in the figure represents the detection result using conventional methods. Figure 4 (d) represents the detection result of the feature detection method. The high-resolution spatial spectrum estimation algorithm used in this example is the feature detection algorithm. It can be seen that time 1 is the moment when the target is just suppressed by strong interference under the conventional beamforming detection method. At this time, the signal source level is reduced by 10dB, and the feature detection method can still detect the target. Time 2 is the moment when the target can be detected by the feature detection method after the strong interference and the target cross. At this time, the signal source level is increased by 10dB. Time 3 is the moment when the feature detection method can no longer detect the target. At this time, the strong interference azimuth is 83.7°, and the target azimuth is 86.2°. Before time 1, the target spectral level at 240Hz is set to SL=125.1dB@240Hz. From time 1 to time 2, the target spectral level is set to SL=115.1dB@240Hz. The marine environmental noise level is NL=79.8dB@240Hz (including 3dB of self-noise from the towing vessel).

[0067] Receive Directivity Index (6dB loss due to array distortion / model mismatch); the interference spectral level remained consistently at SL. j =130dB@240Hz; the processing bandwidth was set to 140Hz, the center frequency to 240Hz, and the integration time to 8s; based on the GPS information of the test vessel, at time 1, the target vessel was X2=8.80km away from the towed vessel, and at time 3, the target vessel was Y2=8.78km away from the towed vessel, while the interfering vessel was 3.2km away from the towed vessel; from the attached... Figure 5 It can be seen that the propagation loss TL of the strong interference at time 3 is... j =58.06dB@240Hz;

[0068] Based on a multidimensional differential feature joint detection method, the feature detection threshold DT is calculated. c Appendix Figure 6 This is a schematic diagram of the feature detection index, where (a) is the mean μ of the jointly detected targets obtained by calculation. s and variance σ s The mean μ of the background noise, respectively n and variance σ n The schematic diagram shows that by specifying different thresholds T, the detection probability and false alarm probability curves under different signal-to-noise ratios can be obtained.

[0069] For the problem of predicting detection performance under strong interference, it can be based on Figure 6 The ROC curve shown in (b) yields a range of selectable detection indices under specified maximum false alarm probability and minimum detection probability conditions; when the false alarm probability is 0.1 and the detection probability is 0.9, the corresponding feature detection index d j =1.4; For the traditional sonar equation, the detection index d=12 under the same false alarm probability and detection probability can be obtained by looking up the table; thus, the feature detection threshold is obtained:

[0070] ,

[0071] Detection threshold of traditional sonar equations:

[0072] ;

[0073] Figure 7 In this context, CBF represents the conventional waveforming algorithm, TZ represents the feature detection algorithm, and the interference suppression gain IG(θ) of the broadband detection algorithm is calculated.

[0074] ;

[0075] Calculate the energy at the location of the strong interference and the target in the background energy distribution map when strong interference is present. and ;

[0076] The quality factors of the optimized passive sonar equations were obtained through calculation:

[0077]

[0078] The quality factors of the traditional passive sonar equations:

[0079]

[0080] Combining the propagation loss curve, the predicted range for passive target detection under strong interference is Y1 = 12.2 km, while the predicted range using the traditional passive sonar equation is X1 = 34.0 km. Therefore, the prediction error for the effective range of passive target detection under strong interference is reduced to...

[0081] .

[0082] The results of experimental data analysis show that, under the conditions of strong interference distance of 3.2km, spectral level of 130dB@240Hz, and azimuth difference between strong interference and target of 2.5°, the prediction error of the effective range of this method is reduced by 86.43% compared with the prediction error of traditional sonar equations, and can support the prediction of the effective range of passive sonar targets in strong interference environment.

Claims

1. A method for evaluating the detection range of passive targets under strong interference based on differential characteristics, characterized in that, The following steps are involved: S1, Strong interference spectral level SL at a specified frequency under strong interference environment. j The target spectral level (SL) and the relative distance between strong interference and the receiving array; S2. Calculate the marine ambient noise level NL at a specified frequency, where NL decreases with frequency at 6dB / octave, and has an initial value of 64dB at 1kHz. S3. Obtain the temperature, depth and salinity parameters of the sea area using a temperature, depth and salinity meter, calculate the sound velocity gradient, and generate a propagation loss curve at a specified frequency using a sound propagation model in combination with seabed medium parameters. S4. Calculate the strong interference propagation loss TL based on the propagation loss curve and the strong interference distance. j ; S5. Calculate the receiver directivity index DI. When the receiver array is undistorted or the model is mismatched, DI = 10lgM, where M is the number of array elements. S6. Based on a multi-dimensional differential feature joint detection method, calculate the feature detection threshold DT. c ; S7. Calculate the interference suppression gain IG(θ) of the broadband detection algorithm, satisfying... in, and These represent the beam energy at the target azimuth before and after suppression of the interference energy; S8. Obtain the energy distribution map of the strong interference and the target's location in the background energy distribution map when strong interference exists. and ; S9. Optimize the passive sonar equations to generate high-quality factors. Among them BL j (θ) represents the beam domain interference and noise level, and its expression is: The target detection prediction distance is calculated by combining the propagation loss curve.

2. The passive target detection range evaluation method based on differential characteristics under strong interference according to claim 1, characterized in that, The sound propagation model in S3 is the Kraken model, and the propagation loss curve is generated by jointly calculating the sound velocity gradient and the parameters of the seabed medium.

3. The passive target detection range evaluation method based on differential characteristics under strong interference according to claim 1, characterized in that, The multidimensional differential feature joint detection method in S6 includes feature fusion and optimized threshold selection under the maximum a posteriori probability criterion.

4. The passive target detection range evaluation method based on differential characteristics under strong interference according to claim 1, characterized in that, The calculation of the interference suppression gain in S7 uses a super-resolution algorithm. and The energy difference between conventional beamforming and super-resolution beamforming is determined respectively.

5. The method for evaluating the detection range of passive targets under strong interference based on differential characteristics according to claim 1, characterized in that, The calculation of the factor of excellence (FOM) in S9 further includes the ambient noise level (NL), receiver directivity index (DI), and interference propagation loss (TL). j The coupling correction term.

6. The passive target detection range evaluation method based on differential characteristics under strong interference according to claim 1, characterized in that, In S6, the feature detection threshold DT is calculated. c Specifically, it includes: (a) Extract the high and low frequency energy ratios, narrowband line spectra, and spatial energy distribution differences between the target and background noise; (b) Construct the probability density distribution of the joint detection statistic to obtain the target mean μ. s and variance σ s The mean μ of the background noise, respectively n and variance σ n ; (c) Generate ROC curves by using the detection probability and false alarm probability under different signal-to-noise ratios to determine the feature detection index. and calculate Where B is the processing bandwidth and T is the integration time.

7. The passive target detection range evaluation method based on differential characteristics under strong interference according to claim 6, characterized in that, The detection index d of the multidimensional differential feature joint detection method j The false alarm probability threshold and detection probability threshold are correlated with the ROC curve, and when the false alarm probability ≤ 0.1 and the detection probability ≥ 0.9, d j The value range is from 1.2 to 1.

6.

8. The method for evaluating the detection range of passive targets under strong interference based on differential characteristics according to claim 1, characterized in that, The calculation of the marine environmental noise level NL includes a towed platform self-noise correction term, with a correction amount of 3dB.

9. The method for evaluating the detection range of passive targets under strong interference based on differential characteristics according to claim 1, characterized in that, The calculation of the receiver directivity index DI includes an additional attenuation term caused by array distortion or model mismatch, and the attenuation amount is determined by actual measurement or simulation.

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