Target characteristic difference-based maximum detection distance evaluation method in strong interference environment

Abstract

The invention relates to the technical field of underwater acoustic signal processing, in particular to a target characteristic difference-based maximum detection distance evaluation method in a strong interference environment, and aims to solve the problem of failure of traditional sonar detection distance evaluation caused by strong interference by analyzing time-frequency or airspace difference characteristics of interference and a target and calculating an interference suppression gain. Simulation and lake test results show that the method can effectively eliminate the spatial influence of strong interference on the background level, improves the detection distance evaluation precision, and is suitable for an active sonar system in a complex interference environment.

Description

Technical Field

[0001] This invention relates to the field of underwater acoustic signal processing technology, specifically to a method for evaluating the maximum detection distance under strong interference conditions based on differences in target characteristics. Background Technology

[0002] With the deployment of various types of underwater acoustic countermeasures equipment and materials, underwater acoustic countermeasures technology has advanced rapidly in various countries, leading to a sharp decline in the detection capabilities of existing sonar systems. Strong-suppression underwater acoustic countermeasures equipment emits high-level interference signals to overwhelm weaker targets, causing a significant increase in background noise compared to ambient noise and creating a large detection blind zone in the vicinity of the equipment's location. In the presence of strong interference, the target echo is obscured by the strong interference signal, drastically reducing the effective range and detection range of the sonar system. Within the detection blind zone caused by strong interference, traditional methods for assessing the maximum detection range of active sonar systems become ineffective.

[0003] From the perspective of energy detection, traditional methods for estimating the maximum detection range of active targets first perform temporal matched filtering and spatial beamforming on the received data to obtain the target echo processing gain. According to the active sonar equations, the maximum detection range is highly susceptible to interference when estimating propagation loss by back-calculating within a given detection threshold. This is because within the detection blind zone caused by strong interference, the aforementioned traditional matched filtering and beamforming processes output the interference energy almost without distortion, resulting in a severely inaccurate estimation of the propagation loss corresponding to the maximum detection range. This is the main reason why traditional methods for estimating the maximum detection range of active targets fail. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to re-evaluate the maximum detection range of active targets under strong interference, reduce the impact of the active detection blind zone caused by strong interference on the evaluation of the maximum detection range, and solve the problem of failure of the evaluation of the maximum detection range of active targets under strong interference.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for evaluating the maximum detection distance under strong interference conditions based on target feature differences, comprising the following steps: S1: Determine the source level SL, receiver array directivity gain DI, and detection threshold DT based on the operating parameters of the active sonar system; the directivity gain DI is determined using the formula... The calculation is performed, where b(sinθ) is the directional pattern of the receiving array of the detection platform; S2: Estimate the interference energy from the received data and determine the interference level IL0; the calculation formula for the interference level IL0 is: Where ISL is the interference source level, TL(r) is the one-way propagation loss from the interference source to the detection platform, r is the one-way propagation distance, and fi and f s These are the interference bandwidth and the detection platform's operating bandwidth, respectively. S3: Correct the background interference level and calculate the background interference level IL(θ) at the interference azimuth; the formula for calculating the corrected background interference level IL(θ) is as follows: Where NL is the background noise level; S4: Based on the differences in spatial orientation and time-frequency distribution between the interference and the target, calculate the interference suppression gain IPG(θ); the formula for calculating IPG(θ) is: Among them, ISR in and ISR out These are the interference-to-signal ratios before and after interference suppression processing based on differential characteristics. and These are the variances of the interference and signal-noise components, respectively. in and() out These represent the results of the corresponding parameters before and after differential feature processing; S5: Correct the background interference level IL(θ) and background level BNL(θ) using the interference suppression gain IPG(θ); the corrected expression for the background interference level IL(θ) is: When the corrected interference level is lower than the background noise level, the corrected background level BNL(θ) is: ; S6: Based on the optimized active sonar equations Calculate the propagation loss TL(r) corresponding to the maximum detection range. max In the formula, DI i The array gain is the interference output azimuth, and TS is the target strength.

[0006] S7: Based on the propagation loss model By combining the frequency-varying characteristics of the absorption loss coefficient α, the maximum detection range r can be solved. max .

[0007] Preferably, the directional pattern b (sinθ) mentioned in step S1 is a uniform linear array directional pattern, and its expression is: In the formula, M is the number of array elements. β=2πdsinθ / λ θ is the direction of signal arrival, d is the element spacing, and λ is the signal wavelength.

[0008] Preferably, the interference suppression gain IPG(θ) in step S4 is achieved through spatial filtering or an adaptive interference suppression algorithm.

[0009] Preferably, the absorption loss coefficient α in step S7 is obtained by using an empirical formula. Calculate, where f is the signal frequency.

[0010] Preferably, the propagation loss TL(r) in S6 max )pass Sure.

[0011] Preferably, the interference bandwidth f in step S2 I Satisfy f I / f s ≥1.

[0012] Preferably, the method further includes a simulation verification step: by simulating the maximum detection distance variation trend under different interference source parameters, target azimuth and system parameters using a computer, generating an azimuth-related detection blind zone distribution map, and verifying the effectiveness of the corrected background level.

[0013] Preferably, when the method is applied in lake or sea trials, it specifically includes: extracting the interference azimuth and energy from measured data; correcting the background level in real time based on the interference suppression gain after processing the differential characteristics; outputting the target detection distance assessment result and comparing and verifying it with traditional methods.

[0014] Compared with existing technologies, the beneficial effects of this invention are: Addressing the problem of failure in evaluating the maximum detection range of active targets due to strong interference, this invention provides a method for evaluating the maximum detection range of active targets after suppressing strong interference based on differential characteristics. This method, based on the interference suppression gain resulting from processing the differential characteristics of interference and the target, and combined with the active sonar equations, designs an effective method and process for correcting the background noise level using the interference level and interference suppression gain under the influence of strong interference. After correcting the background noise level and eliminating the change in the background noise level by strong interference in spatial orientation, the orientation-related background level under the influence of strong interference can be correctly obtained, and the maximum detection range of active targets under strong interference can be reassessed. Simulation and lake test data analysis results show that the method proposed in this invention, after correcting the background noise level, can better re-evaluate the maximum detection range of active targets under strong interference conditions. It is a beneficial extension of the traditional method for evaluating the maximum detection range of active targets under strong interference conditions. Attached Figure Description

[0015] Figure 1 : A flowchart of the present invention; Figure 2 Assessment of the impact of strong interference on the maximum detection range of active targets; Figure 3 : a represents the change in active target detection range with target echo azimuth under strong interference; b represents the change in active target detection range with target echo signal-to-noise ratio under strong interference; Figure 4: a represents the change in active target detection range with the interference source level under strong interference; b represents the change in active target detection range with the interference distance under strong interference. Figure 5 : a represents the change in active target detection range under strong interference with the number of system array elements; b represents the change in active target detection range under strong interference with the time-bandwidth product of the transmitted signal; Figure 6 Verification results of active target lake test data under strong interference; Figure 7 Verification results of active target detection on a lake under strong interference based on differential characteristics. Detailed Implementation

[0016] 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.

[0017] (I) Implementation process See Figure 1 The present invention discloses a method for evaluating the maximum detection distance under strong interference conditions based on target feature differences, as shown in the flowchart below: S1. Determine the source level SL, array directivity gain DI, and detection threshold DT based on the operating parameters of the active sonar system.

[0018] The transmit source level (SL) of an active sonar system is determined by operating parameters, while the detection threshold (DT) is determined by the detection probability and false alarm probability of the sonar system during operation. The maximum detection range is evaluated under the same target intensity (TS). The receiver array directivity gain (DI) is array-dependent and satisfies [condition missing]. (1) Where b(sinθ) is the directional pattern of the receiving array of the detection platform. Taking the directional pattern of a uniform linear array as an example, b(sinθ) is given as... (2) Where M is the number of array elements, β=2πdsinθ / λ, θ is the signal arrival direction, d is the array element spacing, and λ is the signal wavelength.

[0019] S2. Estimate the interference energy from the received data and determine the interference level IL0.

[0020] When suppressive interference is present, let the interference source level be ISL, and assume the interference is omnidirectional. Then, when the interference signal reaches the detection platform, the interference level IL0 received by the detection platform is... (3) Where TL(r) is the one-way propagation loss from the interference source to the detection platform, and r is the one-way propagation distance. I and f S These are the interference bandwidth and the detection platform's operating bandwidth, respectively. Generally, the interference bandwidth should at least cover the detection platform's operating bandwidth, i.e., f. I / f s ≥1.

[0021] S3. Correct the background interference level and calculate the background interference level IL(θ) in the interference azimuth.

[0022] Under strong interference conditions, the interference and isotropic noise signals exhibit significant characteristic differences, with the interference energy concentrated in a specific spatial orientation. The gain correction for this difference is converted into a directional gain in the direction of interference arrival. The receiver interference level's impact on target detection is equivalent to directional background noise. Based on the incoherence of noise and interference, the receiver interference level and the background noise level at the detection platform are incoherently superimposed to form a new "background level." The corrected background interference level IL is... (4) Generally, there are .

[0023] S4. Calculate the background suppression gain IPG(θ) based on the difference characteristics.

[0024] By leveraging the spatial and temporal differences in location and time-frequency distribution characteristics of strong interference and targets, spatial filtering and adaptive interference suppression methods are used to suppress the interference output energy, reduce the interference background, and improve the detection range of active targets. Let IPG be the interference suppression gain obtained after processing the differential features. (5) Among them, ISR in and ISR out These are the interference-to-signal ratios before and after interference suppression processing based on differential characteristics. and These are the variances of the interference and signal-noise components, respectively. in and() out These represent the results of the corresponding parameters before and after differential feature processing. The interference suppression processing gain is obtained by suppressing interference at a specific azimuth, and therefore is related to the azimuth. related.

[0025] S5. Correct the background interference level IL based on the interference suppression gain after differential feature processing. ， (θ) and background level BNL(θ).

[0026] Since the interference suppression gain depends on the signal processing algorithm of the detection platform and directly affects the background noise level, it is a function of the direction θ. The corrected background noise level IL is... (6) After differential feature processing, when the interference level received by the detection platform is lower than the background noise level, the interference background level IL(θ) is still dominated by the background noise level, and the corrected background level BNL(θ) is... (7) S6. Calculate the maximum range uppropagation loss TL(r) of the detectable target after differential feature processing based on the active sonar equation. max ).

[0027] The traditional active sonar equations under suppression jamming can be optimized as follows: (8) Among them, DI i The array gain at the azimuth of the interference output can be obtained from formula (1). After differential feature processing, the propagation loss TL(r) at the maximum detection range is... max )satisfy (9) S7. Determine the maximum detection range r based on the propagation loss estimation results. max .

[0028] Propagation loss can be calculated based on spherical wave attenuation. (10) Where α is the absorption loss coefficient, which is related to the signal frequency. According to the empirical formula for the absorption loss coefficient... (11) Then the maximum detection distance r max It can be obtained by combining formulas (9) and (10) numerically.

[0029] (II) Simulation and Actual Data Test Results Based on "(I) Implementation Process", the effects of the present invention are presented through simulation and actual data analysis, such as... Figures 2-7 As shown.

[0030] Computer Simulation 1: Taking the detection of underwater targets by a linear array sonar as an example, the number of array elements is M=96. During the simulation, a suppressive jamming source with an interference level of 160dB is set up 20km away from the detection platform. First, under the condition of no strong interference, according to the active sonar equation, the maximum detection range is 34.4km. However, under the presence of strong interference, the actual maximum detection range is much less than 34.4km. This is because the jamming source forms a directional "interference background" in the detection area that is much higher than the background noise level. Under the influence of strong interference, the actual detection range of the active sonar is reduced to varying degrees within the detection area, with the most significant reduction in the direction of the interference main lobe. Overall, a detection blind zone is formed near the interference main lobe region, which masks the target signal. Far from the strong interference, the maximum detection range is close to that under the condition of no strong interference. After processing based on a difference of approximately 6dB, the detection range of active targets near the interference direction is improved, and the impact of strong interference on the assessment of the maximum detection range of active targets is reduced. The simulation results are consistent with the analysis of the impact of strong interference on the assessment of the maximum detection range.

[0031] Computer Simulation 2: Based on the active sonar equation (Formula (8)), the influence of each element on the predicted range under the suppression interference is simulated and analyzed from the aspects of interference, signal, environment and equipment. Figure 3 This section describes the change in detection range as a function of target echo parameters, including target azimuth and echo signal-to-noise ratio (SNR). Due to the directional nature of the jamming, a detection blind zone is formed in the vicinity of the jamming area. In other azimuths, the impact of the jamming on the detection range assessment weakens with increasing distance from the jamming sector. Regarding the change in target SNR, the maximum detection range increases with increasing target echo intensity, and the increasing trend is more pronounced the further away from the jamming azimuth. Figure 4 The maximum detection range varies with the parameters of the interference source. As the level of the interference source increases, the maximum effective range of the active sonar decreases rapidly. Due to the influence of the propagation loss curve, the rate of decrease varies in different directions. As for the change of interference distance, the interference effect weakens as the interference is further away from the detection platform, and the weakening rate is relatively slow. Figure 5 For the maximum detection range depending on the processing parameters, the performance of different sizes of detection arrays under suppression jamming is mainly related to the target's azimuth. When the target and jamming azimuths overlap, changing the array size will not increase the effective range. This is because the jamming, as one of the targets, has a 10log... 10 The gain of the M-array increases rapidly as the target distance from the interference increases, which is consistent with the inverse relationship between the array beamwidth and aperture. As for the changes in the transmitted signal parameters, the detection threshold decreases continuously and the effective range increases continuously as the product of the transmitted signal time and bandwidth increases.

[0032] Actual Data Analysis 1: The effectiveness of this invention was compared with the results of actual lake test data processing. Strong interference was located at 76° azimuth, and the target was located at 87° azimuth. Figure 6 In conventional broadband surveillance, strong interference completely obscures the target, making it impossible to extract effective features, and the maximum detection range cannot be assessed. Figure 7 Based on the broadband warning processing results after differential feature processing, the target is clearly visible against a relatively flat background, allowing for good target detection at a range of 1020m. After differential feature processing and background noise level correction, it is evident that the maximum detection range of active targets can exceed 1000m under strong interference, thus achieving an assessment of the maximum detection range of active targets under this strong interference condition.

[0033] The above analysis results demonstrate the effectiveness of the proposed method for evaluating the maximum detection range of active targets under strong interference, based on differential characteristics. The method and process provided by this invention for correcting interference levels and background noise levels under strong interference can effectively eliminate the influence of strong interference on the background noise level in spatial orientation, and better determine the orientation-related background level under strong interference. After correcting the background noise level, it is possible to re-evaluate the maximum detection range of active targets in the presence of strong interference, representing a beneficial extension of traditional methods for evaluating the maximum detection range of active targets under strong interference conditions.

Claims

1. A method for evaluating the maximum detection range under strong interference conditions based on target feature differences, characterized in that, Includes the following steps: S1: Determine the source level SL, receiver array directivity gain DI, and detection threshold DT based on the operating parameters of the active sonar system; the directivity gain DI is determined using the formula... The calculation is performed, where b(sinθ) is the directional pattern of the receiving array of the detection platform; S2: Estimate the interference energy from the received data and determine the interference level IL0; the calculation formula for the interference level IL0 is: Where ISL is the interference source level, TL(r) is the one-way propagation loss from the interference source to the detection platform, r is the one-way propagation distance, and f I and f S These are the interference bandwidth and the detection platform's operating bandwidth, respectively. S3: Correct the background interference level and calculate the background interference level IL(θ) at the interference azimuth; the formula for calculating the corrected background interference level IL(θ) is as follows: Where NL is the background noise level; S4: Based on the differences in spatial orientation and time-frequency distribution between the interference and the target, calculate the interference suppression gain IPG(θ); the formula for calculating IPG(θ) is: Among them, ISR in and ISR out These are the interference-to-signal ratios before and after interference suppression processing based on differential characteristics. and These are the variances of the interference and signal-noise components, respectively. in and() out These represent the results of the corresponding parameters before and after differential feature processing; S5: Correct the background interference level IL(θ) and background level BNL(θ) using the interference suppression gain IPG(θ); the corrected expression for the background interference level IL(θ) is: When the corrected interference level is lower than the background noise level, the corrected background level BNL(θ) is: ； S6: Based on the optimized active sonar equations Calculate the propagation loss TL(r) corresponding to the maximum detection range. max In the formula, DI i The array gain is the output azimuth of the interference, and TS is the target strength. S7: Based on the propagation loss model By combining the frequency-varying characteristics of the absorption loss coefficient α, the maximum detection range r can be solved. max .

2. The method for evaluating the maximum detection distance under strong interference conditions based on target feature differences according to claim 1, characterized in that, The directional pattern b (sinθ) mentioned in step S1 is a uniform linear array directional pattern, and its expression is: In the formula, M is the number of array elements, β=2πdsinθ / λ, θ is the direction of signal arrival, d is the spacing between array elements, and λ is the signal wavelength.

3. The method for evaluating the maximum detection range under strong interference conditions based on target feature differences according to claim 1, characterized in that, The interference suppression gain IPG(θ) mentioned in step S4 is achieved through spatial filtering or an adaptive interference suppression algorithm.

4. The method for evaluating the maximum detection distance under strong interference conditions based on target feature differences according to claim 1, characterized in that, The absorption loss coefficient α mentioned in step S7 is obtained through an empirical formula. Calculate, where f is the signal frequency.

5. The method for evaluating the maximum detection distance under strong interference conditions based on target feature differences according to claim 1, characterized in that, The propagation loss TL(r) mentioned in S6 max )pass Sure.

6. The method for evaluating the maximum detection distance under strong interference conditions based on target feature differences according to claim 1, characterized in that, The interference bandwidth f mentioned in step S2 I Satisfy f I / f s ≥1.

7. The method for evaluating the maximum detection range under strong interference conditions based on target feature differences according to claim 1, characterized in that, The method also includes a simulation verification step: by simulating the maximum detection distance variation trend under different interference source parameters, target azimuth and system parameters, a azimuth-related detection blind zone distribution map is generated, and the effectiveness of the corrected background level is verified.

8. The method for evaluating the maximum detection range under strong interference conditions based on target feature differences according to claim 1, characterized in that, When the method is applied in lake or sea trials, it specifically includes: extracting the location and energy of interference from measured data; correcting the background level in real time based on the interference suppression gain after processing the differential characteristics; outputting the target detection range assessment result and comparing and verifying it with traditional methods.

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