Method and System for Enhancing Detection of Weak Echo Bright Spots of Underwater Targets

Abstract

This invention provides a method and system for enhancing and detecting weak echo bright spots of underwater targets, comprising: Step S1: A sonar system receives the time-domain waveform of a weak target echo signal; Step S2: The weak target echo signal is enhanced using a frequency-shift variable-scale random resonance method; Step S3: A weak target echo bright spot enhancement detection index is constructed, and the enhanced detection of weak echo bright spots of underwater targets is achieved based on the constructed index. This invention can effectively detect weak echo bright spots of underwater targets under low signal-to-noise ratio conditions, accurately locate the position of the target echo, and exhibits excellent anti-noise interference performance and good robustness.

Description

Technical Field

[0001] This invention relates to the field of echo signal detection for active sonar detection of underwater targets, specifically to a method and system for enhancing the detection of weak echo bright spots of underwater targets. Background Technology

[0002] Active sonar is currently the primary means of underwater target detection, widely used in underwater security defense and marine resource development. However, when active sonar detects underwater targets, the target's low intensity, significant two-way propagation loss, and the influence of complex marine environmental noise and reverberation interference often result in weak target echo signals with low signal-to-noise ratios, making them easily submerged by noise. This makes it difficult to detect underwater target echo highlights, severely limiting the detection performance of active sonar for underwater targets.

[0003] Traditional methods for detecting weak target echo bright spots are mostly suitable for target detection under high signal-to-noise ratio (SNR) conditions. For example, the classic matched filtering algorithm is prone to mismatch when the target echo signal is very weak and the SNR is low, resulting in numerous clutter bright spots and failing to effectively detect weak target echo bright spots hidden in background noise. Furthermore, for the problem of detecting weak target echoes under low SNR conditions, existing methods mostly require long signal accumulation times to obtain a certain time processing gain and improve the output SNR. However, signal accumulation methods require long periods of signal data acquisition, making them unsuitable when the signal accumulation time is short or when detecting high-speed moving targets.

[0004] To address the problem of detecting weak target echo bright spots under low signal-to-noise ratio (SNR) conditions, it is necessary to combine weak signal detection methods with methods to enhance the weak target echo signal. In weak signal detection, stochastic resonance theory can effectively enhance weak periodic signals under low SNR. However, at low SNR, relying solely on stochastic resonance-enhanced weak target echo signals for bright spot detection still suffers from significant background noise interference, limiting the detection capability of weak target echoes and making it difficult to directly locate the bright spots in the weak target echoes. Therefore, developing a method for enhancing and detecting weak target echo bright spots under low SNR conditions, constructing enhanced detection indices for weak target echo bright spots, and effectively suppressing background noise and clutter interference to accurately locate the target echo bright spots are of great significance for improving the active detection performance of underwater targets.

[0005] The shortcomings of existing technology are as follows:

[0006] 1) When the target echo signal is very weak and the signal-to-noise ratio is very low, the existing algorithm is prone to mismatch problems or requires long-term signal accumulation. The detection results contain many clutter bright spots and cannot effectively detect weak target echo bright spots hidden in the background noise.

[0007] 2) Existing weak target echo detection algorithms have insufficient noise suppression capabilities, the concentration of weak target bright spots is not obvious, and the detection effect is not intuitive. Summary of the Invention

[0008] To address the shortcomings of existing technologies, the purpose of this invention is to provide a method and system for enhancing the detection of weak echo bright spots of underwater targets.

[0009] A method for enhancing and detecting weak echo bright spots of underwater targets according to the present invention includes:

[0010] Step S1: The sonar system receives the time-domain waveform of the weak target echo signal;

[0011] Step S2: Enhance the weak target echo signal using the frequency shift and variable scale stochastic resonance method;

[0012] Step S3: Construct a weak target echo bright spot enhancement detection index, and realize the enhanced detection of weak echo bright spots of underwater targets based on the constructed weak target echo bright spot enhancement detection index.

[0013] Preferably, step S1 involves: using an active sonar to transmit a detection signal of a certain frequency to the location of the underwater target, the underwater target generating a weak target echo, the sonar receiving array receiving the echo signal, and using a beamforming algorithm to obtain the time-domain waveform of the echo signal at the target's location.

[0014] Preferably, step S2 involves: performing down-conversion and scaling on the weak target echo signal to obtain a low-frequency signal, inputting the low-frequency signal into a stochastic resonance system for enhancement, and then performing scaling recovery and up-conversion on the weak echo signal output by the stochastic resonance system to obtain a stochastic resonance-enhanced weak target echo signal.

[0015] Preferably, step S2 employs:

[0016] The down-conversion process:

[0017] x c (t)=x(t)·υ c (t) (15)

[0018] Where, x c (t) represents the time-domain waveform of the output signal after down-conversion; x(t) represents the time-domain waveform of the weak input echo signal; υ c (t)=cos(2πf c t) represents the local oscillator signal of the mixer; f c The value represents the local oscillator frequency of the mixer, and t is the sampling time; after down-conversion, x... c (t) Perform low-pass filtering to retain the difference frequency signal portion;

[0019] The scaling transformation process involves time-domain extension of the signal waveform.

[0020] x R (t)=x c (t / R) (16)

[0021] Where, x R (t) represents the time-domain signal waveform after scaling, and R represents the scaling factor;

[0022] The stochastic resonance system:

[0023]

[0024] Where y(t) represents the time-domain waveform of the echo signal output by the random resonance; a and b are the parameters of the random resonance system;

[0025] The scale restoration process is the inverse of the scale transformation:

[0026] y R (t)=y(t·R) (17)

[0027] Among them, y R (t) represents the echo signal at the recovered scale, and R is the same scale transformation factor;

[0028] The upconversion is to restore the low-frequency echo signal to its original frequency.

[0029]

[0030] in, This represents a weak target echo signal enhanced by frequency-shifting, scale-variable random resonance. c (t) represents the same mixer; after completion, for Perform a high-pass filter to remove low-frequency components.

[0031] Preferably, step S3 employs a weak target echo bright spot enhancement detection index based on random resonance and spectral kurtosis, which is defined as:

[0032]

[0033] P x =|F{x(t)}| 2 (19)

[0034]

[0035]

[0036] Among them, P x and Let Fi and Fk represent the power spectra of the input and output weak echo signals, respectively; Fi represents the Fourier transform; SK represents the spectral kurtosis of the output weak echo signal power spectrum; Ni B This indicates the number of sampling points within the bandwidth B of the target echo frequency. β is the average power spectrum within bandwidth B; β is an exponential parameter that enhances the concentration of weak target echo bright spots, and the larger the value, the stronger the concentration.

[0037] An underwater target weak echo bright spot enhancement detection system according to the present invention includes:

[0038] Module M1: Time-domain waveform of weak target echo signals received by the sonar system;

[0039] Module M2: Employs a frequency-shifting, variable-scale stochastic resonance method to enhance weak target echo signals;

[0040] Module M3: Constructs a weak target echo bright spot enhancement detection index, and realizes enhanced detection of weak echo bright spots of underwater targets based on the constructed weak target echo bright spot enhancement detection index.

[0041] Preferably, module M1 employs the following method: using an active sonar to transmit a detection signal of a certain frequency to the location of the underwater target, the underwater target generates a weak target echo, the sonar receiving array receives the echo signal, and a beamforming algorithm is used to obtain the time-domain waveform of the echo signal at the target's location.

[0042] Preferably, module M2 employs the following methods: performing down-conversion and scale transformation on the weak target echo signal to obtain a low-frequency signal, inputting the low-frequency signal into a stochastic resonance system for enhancement, and then performing scale recovery and up-conversion on the weak echo signal output by the stochastic resonance system to obtain a stochastic resonance-enhanced weak target echo signal.

[0043] Preferably, the module M2 adopts:

[0044] The down-conversion process:

[0045] x c (t)=x(t)·υ c (t) (22)

[0046] Where, x c (t) represents the time-domain waveform of the output signal after down-conversion; x(t) represents the time-domain waveform of the weak input echo signal; υ c (t)=cos(2πf c t) represents the local oscillator signal of the mixer; f c This represents the local oscillator frequency of the mixer, where t is the sampling time; after down-conversion, x... c(t) Perform low-pass filtering to retain the difference frequency signal portion;

[0047] The scaling transformation process involves time-domain extension of the signal waveform.

[0048] x R (t)=x c (t / R) (23)

[0049] Where, x R (t) represents the time-domain signal waveform after scaling, and R represents the scaling factor;

[0050] The stochastic resonance system:

[0051]

[0052] Where y(t) represents the time-domain waveform of the echo signal output by the random resonance; a and b are the parameters of the random resonance system;

[0053] The scale restoration process is the inverse of the scale transformation:

[0054] y R (t)=y(t·R) (24)

[0055] Among them, y R (t) represents the echo signal at the recovered scale, and R is the same scale transformation factor;

[0056] The upconversion is to restore the low-frequency echo signal to its original frequency.

[0057]

[0058] in, This represents a weak target echo signal enhanced by frequency-shifting, scale-variable random resonance. c (t) represents the same mixer; after completion, for Perform a high-pass filter to remove low-frequency components.

[0059] Preferably, module M3 employs a weak target echo bright spot enhancement detection index based on random resonance and spectral kurtosis, which is defined as:

[0060] SRSK=P x ·P x ·|SK| β (25)

[0061] P x =F{x(t)} 2 (26)

[0062]

[0063]

[0064] Among them, P x and Let Fi and Fk represent the power spectra of the input and output weak echo signals, respectively; Fi represents the Fourier transform; SK represents the spectral kurtosis of the output weak echo signal power spectrum; Ni B This represents the number of sampling points within the bandwidth B of the target echo frequency. β is the average power spectrum within bandwidth B; β is an exponential parameter that enhances the concentration of weak target echo bright spots, and the larger the value, the stronger the concentration.

[0065] Compared with the prior art, the present invention has the following beneficial effects:

[0066] 1. This invention can effectively detect weak echo bright spots of underwater targets under low signal-to-noise ratio, accurately locate the position of the target echo, and has excellent anti-noise interference performance and good robustness.

[0067] 2. The SRSK weak echo bright spot enhancement detection index proposed in this invention has the function of significantly enhancing weak target echo bright spots, improving the energy concentration of echo bright spots and suppressing clutter interference;

[0068] 3. It has a fast calculation speed and high efficiency, and the echo bright spot detection results are more intuitive and convenient. Attached Figure Description

[0069] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0070] Figure 1 Flowchart of a method for enhancing the detection of weak echo bright spots of underwater targets.

[0071] Figure 2 The power spectrum of the input echo signal is used to detect the target bright spot (simulation data).

[0072] Figure 3 The matched filter results for target bright spot detection (simulation data) are used to input the echo signal.

[0073] Figure 4 The power spectrum of the random resonance output echo signal is used to detect the target bright spot (simulation data).

[0074] Figure 5 The results of enhanced detection of bright spots in the SRSK index of the random resonance output echo signal (simulation data).

[0075] Figure 6The power spectrum of the target bright spot enhancement detection results of the echo signal input to the sea trial data (sea trial data).

[0076] Figure 7 Detection results of enhanced bright spots of the SRSK index target in the random resonance output echo signal of sea trial data (sea trial data). Detailed Implementation

[0077] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0078] Example 1

[0079] The present invention provides a method for enhancing the detection of weak echo bright spots of underwater targets, such as... Figure 1 As shown, it includes:

[0080] Step 1: (Signal acquisition and data preparation stage) The sonar system receives the time-domain waveform of the weak target echo signal.

[0081] An active sonar emits a detection signal of a certain frequency toward the location of an underwater target. The underwater target generates a weak target echo, which is then received by the sonar receiving array (containing the weak target echo). A beamforming algorithm is used to obtain the time-domain waveform of the echo signal at the target's location, which serves as the input echo signal for this invention. However, directly detecting the target echo bright spot using the power spectrum calculated from this input echo signal's time-domain waveform or a matched filtering method is subject to significant background noise interference, making it impossible to effectively detect the location of the weak target echo.

[0082] Step 2: Use the frequency shift and scaled random resonance method to enhance the weak target echo signal.

[0083] Weak target echo signals often have frequencies above kilohertz, making direct stochastic resonance impossible. Therefore, the weak target echo signal needs to be mixed (down-converted) and scaled to convert it into a low-frequency signal, which is then input into the stochastic resonance system for enhancement. Then, the weak echo signal output from the stochastic resonance system undergoes scale recovery and mixing (up-converted) to obtain a stochastically enhanced weak target echo signal. The specific steps include:

[0084] The mixing (down-conversion) process is as follows:

[0085] x c (t)=x(t)·υ c (t) (29)

[0086] In the formula, x c (t) represents the time-domain waveform of the output signal after mixing (down-conversion); x(t) represents the time-domain waveform of the weak input echo signal; υ c (t)=cos(2πf c t) is the local oscillator signal of the mixer, where f c Let be the local oscillator frequency of the mixer, and t be the sampling time. After down-conversion, x... c (t) performs low-pass filtering to retain the difference frequency signal portion.

[0087] Scaling is the process of extending a signal waveform in the time domain, as follows:

[0088] x R (t)=x c (t / R) (30)

[0089] In the formula, x R (t) represents the time-domain signal waveform after scaling, and R is the scaling factor. Furthermore, this signal is input into a stochastic resonance system for enhancement. The expression for the stochastic resonance system is:

[0090]

[0091] In the formula, y(t) is the time-domain waveform of the echo signal output by random resonance, and a and b are system parameters.

[0092] Scale recovery is the inverse of the scale transformation, i.e.:

[0093] y R (t)=y(t·R) (32)

[0094] In the formula, y R (t) represents the echo signal at the recovered scale, and R is the same scale transformation factor.

[0095] Frequency mixing (upconversion) restores the low-frequency echo signal to its original frequency. The process is as follows:

[0096]

[0097] In the formula, For weak target echo signals enhanced by frequency-shifting and scale-variable random resonance, υ c (t) represents the same mixer. After completion, for... Perform a high-pass filter to remove low-frequency components.

[0098] Step 3: (Important step, innovation) Construct a weak target echo bright spot enhancement detection index.

[0099] Random resonance enhances weak target echo signals, increasing their energy intensity to some extent. However, relying solely on the power spectrum of the weak echo signal enhanced by the random resonance system for target bright spot detection still contains significant background noise interference, necessitating the development of effective weak target echo bright spot enhancement and detection metrics.

[0100] This invention proposes a weak target echo bright spot enhancement detection index based on random resonance and spectral kurtosis (SRSK index), which is defined as follows:

[0101]

[0102] in

[0103] P x =|F{x(t)}| 2 (35)

[0104]

[0105]

[0106] In the formula, P x and Let F{·} represent the power spectra of the input and output weak echo signals, respectively; let SK represent the spectral kurtosis of the output weak echo signal power spectrum; and let N represent the power spectrum of the output weak echo signal power spectrum. B The number of sampling points within the bandwidth B of the target echo frequency. β is the average power spectrum within bandwidth B; β is an exponential parameter, generally β≤4, which has the ability to enhance the concentration of weak target echo bright spots, and the larger the value, the stronger the concentration.

[0107] The SRSK index proposed in this invention firstly utilizes the product spectrum of the power spectra of the input and output weak echo signals. When both the input and output have high spectral peaks at the target echo frequency, it can significantly enhance the energy of the target echo highlights and suppress other noise energy. Secondly, the SRSK index uses the spectral kurtosis of the output power spectrum as the weight of the product spectrum, which can effectively measure the effect of random resonance in enhancing weak echo signals. When the power spectrum of the random resonance output echo signal has only one strong spectral peak, it indicates a good resonance effect and a significant enhancement of the weak target echo; in this case, the spectral kurtosis value is large. If the power spectrum contains many other interference peaks, the resonance effect is poor, and the spectral kurtosis value becomes smaller. Therefore, using the spectral kurtosis value as a weight further highlights the ability of random resonance to enhance weak echo signals, improve the concentration of weak target echo highlights, and suppress interference highlights. In summary, the proposed SRSK index has excellent noise suppression capabilities and energy concentration capabilities for weak target echo bright spots. It can accurately locate the position of weak target echoes, and the detection results are more intuitive and convenient, thus solving the problem of detecting weak echo bright spots of underwater targets under low signal-to-noise ratio conditions.

[0108] An underwater target weak echo bright spot enhancement detection system according to the present invention includes:

[0109] Module 1: (Signal Acquisition and Data Preparation Stage) The time-domain waveform of the weak target echo signal received by the sonar system.

[0110] An active sonar emits a detection signal of a certain frequency toward the location of an underwater target. The underwater target generates a weak target echo, which is then received by the sonar receiving array (containing the weak target echo). A beamforming algorithm is used to obtain the time-domain waveform of the echo signal at the target's location, which serves as the input echo signal for this invention. However, directly detecting the target echo bright spot using the power spectrum calculated from this input echo signal's time-domain waveform or a matched filtering method is subject to significant background noise interference, making it impossible to effectively detect the location of the weak target echo.

[0111] Module 2: Employing a frequency-shifting, variable-scale stochastic resonance method to enhance weak target echo signals.

[0112] Weak target echo signals often have frequencies above kilohertz, making direct stochastic resonance impossible. Therefore, the weak target echo signal needs to be mixed (down-converted) and scaled to convert it into a low-frequency signal, which is then input into the stochastic resonance system for enhancement. Then, the weak echo signal output from the stochastic resonance system undergoes scale recovery and mixing (up-converted) to obtain a stochastically enhanced weak target echo signal. The specific steps include:

[0113] The mixing (down-conversion) process is as follows:

[0114] xc (t)=x(t)·υ c (t) (38)

[0115] In the formula, x c (t) represents the time-domain waveform of the output signal after mixing (down-conversion); x(t) represents the time-domain waveform of the weak input echo signal; υ c (t)=cos(2πf c t) is the local oscillator signal of the mixer, where f c Let be the local oscillator frequency of the mixer, and t be the sampling time. After down-conversion, x... c (t) performs low-pass filtering to retain the difference frequency signal portion.

[0116] Scaling is the process of extending a signal waveform in the time domain, as follows:

[0117] x R (t)=x c (t / R) (39)

[0118] In the formula, x R (t) represents the time-domain signal waveform after scaling, and R is the scaling factor. Furthermore, this signal is input into a stochastic resonance system for enhancement. The expression for the stochastic resonance system is:

[0119]

[0120] In the formula, y(t) is the time-domain waveform of the echo signal output by random resonance, and a and b are system parameters.

[0121] Scale recovery is the inverse of the scale transformation, i.e.:

[0122] y R (t)=y(t·R)(41)

[0123] In the formula, y R (t) represents the echo signal at the recovered scale, and R is the same scale transformation factor.

[0124] Frequency mixing (upconversion) restores the low-frequency echo signal to its original frequency. The process is as follows:

[0125]

[0126] In the formula, For weak target echo signals enhanced by frequency-shifting and scale-variable random resonance, υ c (t) represents the same mixer. After completion, for... Perform a high-pass filter to remove low-frequency components.

[0127] Module 3: (Important Steps, Innovation Points) Constructing a detection index to enhance weak target echo highlights.

[0128] Random resonance enhances weak target echo signals, increasing their energy intensity to some extent. However, relying solely on the power spectrum of the weak echo signal enhanced by the random resonance system for target bright spot detection still contains significant background noise interference, necessitating the development of effective weak target echo bright spot enhancement and detection metrics.

[0129] This invention proposes a weak target echo bright spot enhancement detection index based on random resonance and spectral kurtosis (SRSK index), which is defined as follows:

[0130]

[0131] in

[0132] P x =F{x(t)} 2 (44)

[0133]

[0134]

[0135] In the formula, P x and Let F{·} represent the power spectra of the input and output weak echo signals, respectively; let SK represent the spectral kurtosis of the output weak echo signal power spectrum; and let N represent the power spectrum of the output weak echo signal power spectrum. B The number of sampling points within the bandwidth B of the target echo frequency. β is the average power spectrum within bandwidth B; β is an exponential parameter, generally β≤4, which has the ability to enhance the concentration of weak target echo bright spots, and the larger the value, the stronger the concentration.

[0136] The SRSK index proposed in this invention firstly utilizes the product spectrum of the power spectra of the input and output weak echo signals. When both the input and output have high spectral peaks at the target echo frequency, it can significantly enhance the energy of the target echo highlights and suppress other noise energy. Secondly, the SRSK index uses the spectral kurtosis of the output power spectrum as the weight of the product spectrum, which can effectively measure the effect of random resonance in enhancing weak echo signals. When the power spectrum of the random resonance output echo signal has only one strong spectral peak, it indicates a good resonance effect and a significant enhancement of the weak target echo; in this case, the spectral kurtosis value is large. If the power spectrum contains many other interference peaks, the resonance effect is poor, and the spectral kurtosis value becomes smaller. Therefore, using the spectral kurtosis value as a weight further highlights the ability of random resonance to enhance weak echo signals, improve the concentration of weak target echo highlights, and suppress interference highlights. In summary, the proposed SRSK index has excellent noise suppression capabilities and energy concentration capabilities for weak target echo bright spots. It can accurately locate the position of weak target echoes, and the detection results are more intuitive and convenient, thus solving the problem of detecting weak echo bright spots of underwater targets under low signal-to-noise ratio conditions.

[0137] Example 2

[0138] Example 2 is a preferred example of Example 1.

[0139] This invention is a method for enhancing the detection of weak echo bright spots of underwater targets. It is based on the weak target echo signal data received when active sonar detects underwater targets. The implementation method is described below with specific simulation examples and sea trial data.

[0140] Step 1: Data Preparation

[0141] The active sonar transmits a detection signal towards the target's azimuth. The sonar receiving array receives the echo signal for 8 seconds. A conventional beamforming algorithm is then used to obtain the echo signal from the target's azimuth, which is used as the input echo signal for subsequent detection algorithms. The active sonar is 3 km away from the underwater target, and the center frequency of the weak target echo is 5136 Hz. The target bright spot is detected directly using the power spectrum of the input echo signal. The detection results are as follows: Figure 2 As shown in the figure, under low signal-to-noise ratio conditions, this method suffers from complex background noise and cannot detect target echo bright spots. Using the traditional matched filtering method for target bright spot detection yields the following results: Figure 3 As shown, this method still suffers from significant background noise interference, and the detection of weak target bright spots is not obvious.

[0142] Step 2: Enhance the weak target echo signal using the frequency-shifting, variable-scale stochastic resonance method.

[0143] The time-domain waveform of the input echo signal from the previous step is input into a frequency-shifted, scale-variable stochastic resonance system. The local oscillator frequency of the mixer is set to 5050Hz. The input echo signal is then sequentially mixed (down-conversion), scale-transformed, stochastically resonated, scale-restored, and mixed (up-conversion), resulting in an enhanced weak target echo signal. The power spectrum of the echo signal output from the stochastic resonance system is used for underwater target bright spot detection. The detection results are as follows: Figure 4 As shown, after random resonance enhancement, there is a suspected target echo bright spot at 3km, but the detection results still contain a lot of background noise interference, and further construction of target echo bright spot enhancement detection index is needed.

[0144] Step 3: Construct a weak target echo bright spot enhancement detection index (SRSK index)

[0145] Calculate the SRSK weak target echo bright spot enhancement detection index according to the formula:

[0146]

[0147] With the index parameter β = 1, the SRSK indicator highlight detection results are as follows: Figure 5 As shown, the proposed SRSK index effectively improves the energy concentration of weak target echo bright spots, significantly suppresses background noise interference, and achieves enhanced detection of weak target echo bright spots under low signal-to-noise ratio conditions, making the detection effect more intuitive and convenient.

[0148] To further illustrate the effectiveness of this invention in detecting weak echo bright spots of underwater targets in actual sea trials, the above steps were applied to real sea trial data, resulting in enhanced echo bright spot detection results in the sea trial data, such as... Figure 6 and Figure 7 As shown. In the sea trial data, the target echo frequency was 0.385 (normalized frequency), and the distance between the target and the active sonar was approximately 0.48 (normalized distance). (Comparison) Figure 6 and Figure 7 The test results show that directly using the power spectrum of the input echo signal after beamforming of the sea trial data (such as...) Figure 6 The previous method could not detect weak target echo bright spots, but the SRSK index proposed in this invention can effectively detect the location of the target echo bright spots (0.385, 0.48). Figure 7 The detection results were consistent with the actual location, further verifying the effectiveness and accuracy of the invention in marine engineering applications.

[0149] Those skilled in the art will understand that, in addition to implementing the system, apparatus, and their modules provided by this invention in purely computer-readable program code, the same program can be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system, apparatus, and their modules provided by this invention can be considered a hardware component, and the modules included therein for implementing various programs can also be considered structures within the hardware component; alternatively, modules for implementing various functions can be considered both software programs implementing the method and structures within the hardware component.

[0150] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A method for enhancing the detection of weak echo bright spots of underwater targets, characterized in that, include: Step S1: The sonar system receives the time-domain waveform of the weak target echo signal; Step S2: Enhance the weak target echo signal using the frequency shift and scale random resonance method; Step S3: Construct a weak target echo bright spot enhancement detection index, and realize the enhanced detection of weak echo bright spots of underwater targets based on the constructed weak target echo bright spot enhancement detection index; Step S3 employs a weak target echo bright spot enhancement detection index based on random resonance and spectral kurtosis, which is defined as: Among them, P x and These represent the power spectra of the input weak echo signal and the output weak echo signal, respectively. Indicates Fourier transform; SK represents the spectral kurtosis of the power spectrum of the output weak echo signal; N B This represents the number of sampling points within the bandwidth B of the target echo frequency. β is the average power spectrum within bandwidth B; β is an exponential parameter that enhances the concentration of weak target echo bright spots, and the larger the value, the stronger the concentration.

2. The underwater target weak echo bright spot enhancement detection method according to claim 1, characterized in that, Step S1 involves: using an active sonar to transmit a detection signal of a certain frequency to the location of the underwater target; the underwater target generates a weak target echo; the sonar receiving array receives the echo signal; and a beamforming algorithm is used to obtain the time-domain waveform of the echo signal at the target's location.

3. The underwater target weak echo bright spot enhancement detection method according to claim 1, characterized in that, Step S2 involves: performing down-conversion and scaling on the weak target echo signal to obtain a low-frequency signal, inputting the low-frequency signal into a stochastic resonance system for enhancement, and then performing scaling recovery and up-conversion on the weak echo signal output by the stochastic resonance system to obtain a stochastic resonance-enhanced weak target echo signal.

4. The underwater target weak echo bright spot enhancement detection method according to claim 3, characterized in that, Step S2 employs the following: The down-conversion process: x c (t)=x(t)·υ c (t) (5) Where, x c (t) represents the time-domain waveform of the output signal after down-conversion; x(t) represents the time-domain waveform of the weak input echo signal; υ c (t)=cos(2πf c t) represents the local oscillator signal of the mixer; f c The value represents the local oscillator frequency of the mixer, and t is the sampling time; after down-conversion, x... c (t) Perform low-pass filtering to retain the difference frequency signal portion; The scaling transformation process involves time-domain extension of the signal waveform. x R (t)=x c (t / R)(6) Where, x R (t) represents the time-domain signal waveform after scaling, and R represents the scaling factor; The stochastic resonance system: Where y(t) represents the time-domain waveform of the echo signal output by the random resonance; a and b are the parameters of the random resonance system; The scale restoration process is the inverse of the scale transformation: y R (t)=y(t·R)(7) Among them, y R (t) represents the echo signal at the recovered scale, and R is the same scale transformation factor; The upconversion is to restore the low-frequency echo signal to its original frequency. in, This represents a weak target echo signal enhanced by frequency-shifting, scale-variable random resonance. c (t) represents the same mixer; after completion, for Perform a high-pass filter to remove low-frequency components.

5. A system for enhancing and detecting weak echo bright spots of underwater targets, characterized in that, include: Module M1: Time-domain waveform of weak target echo signals received by the sonar system; Module M2: Employs a frequency-shifting, variable-scale stochastic resonance method to enhance weak target echo signals; Module M3: Constructs a weak target echo bright spot enhancement detection index, and realizes enhanced detection of weak echo bright spots of underwater targets based on the constructed weak target echo bright spot enhancement detection index; The module M3 employs a weak target echo bright spot enhancement detection index based on random resonance and spectral kurtosis, which is defined as: Among them, P x and These represent the power spectra of the input weak echo signal and the output weak echo signal, respectively. Indicates Fourier transform; SK represents the spectral kurtosis of the power spectrum of the output weak echo signal; N B This represents the number of sampling points within the bandwidth B of the target echo frequency. β is the average power spectrum within bandwidth B; β is an exponential parameter that enhances the concentration of weak target echo bright spots, and the larger the value, the stronger the concentration.

6. The underwater target weak echo bright spot enhancement detection system according to claim 5, characterized in that, The module M1 employs the following method: it uses an active sonar to transmit a detection signal of a certain frequency to the location of the underwater target. The underwater target generates a weak target echo, the sonar receiving array receives the echo signal, and a beamforming algorithm is used to obtain the time-domain waveform of the echo signal at the target's location.

7. The underwater target weak echo bright spot enhancement detection system according to claim 5, characterized in that, The module M2 employs the following steps: it performs down-conversion and scale transformation on the weak target echo signal to obtain a low-frequency signal, inputs the low-frequency signal into a stochastic resonance system for enhancement, and then performs scale recovery and up-conversion on the weak echo signal output by the stochastic resonance system to obtain a stochastic resonance-enhanced weak target echo signal.

8. The underwater target weak echo bright spot enhancement detection system according to claim 7, characterized in that, The module M2 adopts: The down-conversion process: x c (t)=x(t)·υ c (t) (12) Where, x c (t) represents the time-domain waveform of the output signal after down-conversion; x(t) represents the time-domain waveform of the weak input echo signal; υ c (t)=cos(2πf c t) represents the local oscillator signal of the mixer; f c The value represents the local oscillator frequency of the mixer, and t is the sampling time; after down-conversion, x... c (t) Perform low-pass filtering to retain the difference frequency signal portion; The scaling transformation process involves time-domain extension of the signal waveform. x R (t)=x c (t / R) (13) Where, x R (t) represents the time-domain signal waveform after scaling, and R represents the scaling factor; The stochastic resonance system: Where y(t) represents the time-domain waveform of the echo signal output by the random resonance; a and b are the parameters of the random resonance system; The scale restoration process is the inverse of the scale transformation: y R (t)=y(t·R) (14) Among them, y R (t) represents the echo signal at the recovered scale, and R is the same scale transformation factor; The upconversion is to restore the low-frequency echo signal to its original frequency. in, This represents a weak target echo signal enhanced by frequency-shifting, scale-variable random resonance. c (t) represents the same mixer; after completion, for Perform a high-pass filter to remove low-frequency components.

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