Underwater acoustic environment adaptive time delay estimation method

Abstract

The invention discloses a self-adaptive time delay estimation method for an underwater acoustic environment, which comprises the following steps of: 1, preprocessing a signal; 2, constructing a dynamic frequency domain weighting function; 3, adaptive parameter iterative optimization is carried out; 4, calculating a multi-channel cross-correlation function; 5, averaging a multi-channel cross-correlation function; and step 6, time delay estimation and output. Through environment adaptation, multi-channel cooperation and calculation efficiency optimization, high precision, high robustness and low delay of underwater sound time delay estimation are realized, the signal processing capability in a complex underwater scene is remarkably improved, and a key technical support is provided for military, civil and scientific research fields.

Description

Technical Field

[0001] This invention relates to the field of time delay estimation methods, specifically to an adaptive time delay estimation method for underwater acoustic environments. Background Technology

[0002] Underwater acoustic channels are characterized by significant multipath effects, strong time-varying characteristics, and complex noise interference. Time delay estimation, as one of the core technologies in underwater acoustic signal processing, directly affects target positioning accuracy and communication reliability. Traditional time delay estimation methods mainly include: 1. Cross-correlation method: The time delay is determined by calculating the peak value of the cross-correlation function between signals, but the computation is large and the noise resistance is weak.

[0003] 2. Cross-power spectrum method: The time delay is extracted using the phase information of the cross-power spectrum in the frequency domain, but spurious peaks are prone to appear under low signal-to-noise ratio or multipath interference.

[0004] 3. Generalized cross-correlation method: Improves the sharpness of correlation peaks through frequency domain weighted filtering, but requires pre-setting of the weighting function and is difficult to adapt to dynamic underwater acoustic environments.

[0005] However, the traditional time delay estimation methods for such underwater acoustic channels in the existing technology have the following problems: 1. Poor environmental adaptability: Traditional methods are sensitive to noise, multipath interference and channel time-varying characteristics, which leads to a decrease in estimation accuracy.

[0006] 2. High computational complexity: The cross-correlation method and the generalized cross-correlation method require a large number of convolution operations, which are not real-time.

[0007] 3. Fixed parameters: The weighting function or filter parameters need to be adjusted manually and cannot adapt to changes in the underwater acoustic environment.

[0008] Therefore, an adaptive time delay estimation method for underwater acoustic environments is needed to address the above problems. Summary of the Invention

[0009] The purpose of this invention is to provide an adaptive time delay estimation method for underwater acoustic environments to solve the problems mentioned in the background art.

[0010] To achieve the above objectives, the present invention provides the following technical solution: an adaptive time delay estimation method for underwater acoustic environments, comprising the following steps: Step 1: Signal preprocessing: processing the received underwater acoustic signal x ( n Bandpass filtering is performed, center frequency fc The value range is 2kHz≤ fc ≤50kHz, bandwidth B The value range is 0.5kHz ≤ B≤10kHz to suppress out-of-band noise; downsample the filtered signal, with a downsampling factor of [missing value]. D The range of values ​​for is 2≤ D ≤8, to reduce the amount of subsequent calculations; Step 2: Construction of dynamic frequency domain weighting function: Based on the signal preprocessed in Step 1, calculate its autopower spectrum. Gs ( yes Using the periodogram method, the formula is: ; ; where L is the signal length, and its value ranges from 1024≤L≤8192, in order to balance spectral resolution and computational efficiency; Step 3: Adaptive Parameter Iterative Optimization: Using the Least Mean Square Error (LMS) criterion, the weighting function parameters are dynamically adjusted via gradient descent. The iterative formula is as follows: ;

[0011] Step 4: Calculation of multi-channel cross-correlation function: For each channel of the array signal... xi ( n )and xj ( n Calculate the cross-correlation function The formula is:

[0012] Step 5: Averaging of multi-channel cross-correlation functions: Averaging the cross-correlation functions of each channel... The weighted average is calculated using the following formula: ; Step 6: Time Delay Estimation and Output: Finding the Average Cross-Correlation Function peak position Corresponding estimated delay ;in The sampling interval is used to estimate the time delay. Its confidence interval, which is determined by the ratio of the peak amplitude to the second-highest amplitude, and the ratio threshold. c The value range is 1.5≤ c ≤3.

[0013] In the second step of constructing the dynamic frequency domain weighting function, the formula is... When applied in shallow, multipath environments, a negative gradient compensation factor α is introduced to correct the weighting function as follows: Shallow sea is defined as water depth. h Satisfying 20m≤ h ≤200m, and the sound velocity profile has a negative gradient abrupt change layer; α The value range is 0.9≤ α ≤1.1, used to compensate for time delay estimation errors caused by sound ray bending.

[0014] In the fifth step of averaging the multi-channel cross-correlation function, when applied in a real-time processing system, the time T for a single delay estimation satisfies... T ≤10ms, achieved through the following measures: Fast Fourier Transform (FFT) is used to accelerate cross-power spectrum calculation, reducing the number of FFT points. Values The range of values ​​for the number of iterations K is limited to: To balance accuracy and speed.

[0015] In the third step of adaptive parameter iterative optimization; formula Among them To estimate the delay, To achieve the true time delay, it is expected that E is achieved by averaging through a sliding window with a window length of 50 ≤ M ≤ 200.

[0016] In the fifth step of averaging the multi-channel cross-correlation function, the formula... Channel weight coefficients in The calculation formula is as follows: ,in The signal-to-noise ratio (SNR) of the i-th and j-th channels is calculated by the ratio of signal energy to noise energy.

[0017] The formula in the second step of constructing the dynamic frequency domain weighting function In , which is a stabilizing factor.

[0018] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention achieves high accuracy, high robustness, and low latency in underwater acoustic time delay estimation through environmental adaptation, multi-channel collaboration, and computational efficiency optimization, significantly improving signal processing capabilities in complex underwater scenarios and providing key technical support for military, civilian, and scientific research fields.

[0019] 2. This invention dynamically constructs a weighting function (H(jω)) using the self-power spectrum (G_s(jω)) and introduces a stability factor (ε) to avoid numerical divergence, thus solving the problem of traditional methods being sensitive to noise.

[0020] 3. This invention utilizes the spatial diversity of array signals, through cross-correlation function averaging and signal-to-noise ratio weighting (… It improves estimation stability, and is especially suitable for complex sound field environments. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating the steps of an adaptive time delay estimation method for underwater acoustic environments according to the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Please see Figure 1 This invention provides a technical solution: an adaptive time delay estimation method for underwater acoustic environments, comprising the following steps: Step 1: Signal preprocessing: processing the received underwater acoustic signal x ( n Bandpass filtering is performed, center frequency fc The value range is 2kHz≤ fc ≤50kHz, bandwidth B The value range is 0.5kHz ≤ B ≤10kHz to suppress out-of-band noise; downsample the filtered signal, with a downsampling factor of [missing value]. D The range of values ​​for is 2≤ D ≤8, to reduce the amount of subsequent calculations; Step 2: Construction of dynamic frequency domain weighting function: Based on the signal preprocessed in Step 1, calculate its autopower spectrum. Gs ( yes Using the periodogram method, the formula is: ; ; where L is the signal length, and its value ranges from 1024≤L≤8192, in order to balance spectral resolution and computational efficiency; Step 3: Adaptive Parameter Iterative Optimization: Using the Least Mean Square Error (LMS) criterion, the weighting function parameters are dynamically adjusted via gradient descent. The iterative formula is as follows: ;

[0024] Step 4: Calculation of multi-channel cross-correlation function: For each channel of the array signal... xi ( n )and xj ( n Calculate the cross-correlation function The formula is: , It is the cross-correlation function of the signals in the i-th and j-th channels; Step 5: Averaging of multi-channel cross-correlation functions: Averaging the cross-correlation functions of each channel... The weighted average is calculated using the following formula: ; Step 6: Time Delay Estimation and Output: Finding the Average Cross-Correlation Function peak position Corresponding estimated delay ;in The sampling interval is used to estimate the time delay. Its confidence interval, which is determined by the ratio of the peak amplitude to the second-highest amplitude, and the ratio threshold. c The value range is 1.5≤ c ≤3.

[0025] In the second step of constructing the dynamic frequency domain weighting function, the formula is... When applied in shallow, multipath environments, a negative gradient compensation factor α is introduced to correct the weighting function as follows: Shallow sea is defined as water depth. h Satisfying 20m≤ h ≤200m, and the sound velocity profile has a negative gradient abrupt change layer; α The value range is 0.9≤ α ≤1.1, used to compensate for time delay estimation errors caused by sound ray bending.

[0026] In the fifth step of averaging the multi-channel cross-correlation function, when applied in a real-time processing system, the time T for a single delay estimation satisfies... T ≤10ms, achieved through the following measures: Fast Fourier Transform (FFT) is used to accelerate cross-power spectrum calculation, reducing the number of FFT points. Values The range of values ​​for the number of iterations K is limited to: To balance accuracy and speed.

[0027] In the third step of adaptive parameter iterative optimization; formula Among them To estimate the delay, To achieve the true time delay, it is expected that E is achieved by averaging through a sliding window with a window length of 50 ≤ M ≤ 200.

[0028] In the fifth step of averaging the multi-channel cross-correlation function, the formula... Channel weight coefficients in The calculation formula is as follows: ,in The signal-to-noise ratio (SNR) of the i-th and j-th channels is calculated by the ratio of signal energy to noise energy.

[0029] The formula in the second step of constructing the dynamic frequency domain weighting function In , which is a stabilizing factor.

[0030] The following is a detailed data calculation process for the method of this application. Application scenario: a shallow sea area with a water depth of 80m, where the sound velocity profile has a negative gradient transition layer (sound velocity decreases with increasing depth), the target signal is a 15kHz LFM signal with a bandwidth of 4kHz and a signal-to-noise ratio (SNR) of 5dB.

[0031] Step 1: Signal preprocessing bandpass filtering: center frequency fc =15kHz, bandwidth B =4kHz; Downsampling: Original sampling rate fs =100kHz, downsampling factor D =4, sampling rate after downsampling fs =25kHz, sampling interval Ts =40 m s; Signal length: taken L =2048 points, corresponding to a duration of 81.92ms.

[0032] Step 2: Constructing the dynamic frequency domain weighting function: ,in The frequency range covers 13kHz to 17kHz; weighting function: stability factor After correction: ; Step 3; Adaptive parameter iterative optimization; LMS algorithm: step size factor μ=0.05, sliding window length M=100, error function: estimate the true delay by averaging the sliding window. It converged after 20 iterations; Steps four and five; multi-channel cross-correlation and averaging: array signal: 4-element uniform linear array, element spacing d=0.5m Cross-correlation function: ; Weighted average: Channel weight Calculated based on signal-to-noise ratio: ; Step 6; Peak Detection: Average Cross-Correlation Function R avg( m peak position =12; Delay estimation: Confidence interval: the ratio of peak amplitude to second highest peak γ = 2.1, confidence interval ± 0.1 ms.

[0033] This invention, through dynamic frequency domain weighting (second step) and adaptive parameter optimization (third step), suppresses noise interference and enhances signal characteristics, making peak detection of time delay estimation more accurate (sixth step), and is especially suitable for low signal-to-noise ratio environments.

[0034] The average processing of multi-channel cross-correlation functions (step 5) combined with signal-to-noise ratio weighting (SNR-based) further improves the estimation robustness and reduces errors caused by array channel inconsistencies.

[0035] A negative gradient compensation factor (α) is introduced to correct the time delay deviation caused by the curvature of the sound ray, adapt to the negative gradient sound velocity profile in shallow sea (20m≤water depth≤200m), and solve the interference of multipath effect. Adaptive iterative optimization (step 3) adjusts the weighting function according to the real-time signal characteristics to adapt to the non-stationarity of the underwater acoustic signal (such as time-varying noise and Doppler frequency shift).

[0036] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0037] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An adaptive time delay estimation method for underwater acoustic environments, comprising the following steps, characterized in that: Step 1: Signal preprocessing: processing the received underwater acoustic signal x ( n Bandpass filtering is performed, center frequency fc The value range is 2kHz≤ fc ≤50kHz, bandwidth B The value range is 0.5kHz ≤ B ≤10kHz to suppress out-of-band noise; The filtered signal is downsampled, and the downsampling factor is... D The range of values ​​for is 2≤ D ≤8, to reduce the amount of subsequent calculations; Step 2: Construction of dynamic frequency domain weighting function: Based on the signal preprocessed in Step 1, calculate its autopower spectrum. Gs ( j ω Using the periodogram method, the formula is: ; ; where L is the signal length, and its value ranges from 1024≤L≤8192, in order to balance spectral resolution and computational efficiency; Step 3: Adaptive Parameter Iterative Optimization: Using the Least Mean Square Error (LMS) criterion, the weighting function parameters are dynamically adjusted via gradient descent. The iterative formula is as follows: ; Step 4: Calculation of multi-channel cross-correlation function: For each channel of the array signal... xi ( n )and xj ( n Calculate the cross-correlation function The formula is: Step 5: Averaging of multi-channel cross-correlation functions: Averaging the cross-correlation functions of each channel... The weighted average is calculated using the following formula: ; Step 6: Time Delay Estimation and Output: Finding the Average Cross-Correlation Function peak position Corresponding estimated delay ;in The sampling interval is used to estimate the time delay. Its confidence interval, which is determined by the ratio of the peak amplitude to the second-highest amplitude, and the ratio threshold. γ The value range is 1.5≤ γ ≤3.

2. The adaptive time delay estimation method for underwater acoustic environment according to claim 1, characterized in that: In the second step of constructing the dynamic frequency domain weighting function, the formula is... When applied in shallow, multipath environments, a negative gradient compensation factor α is introduced to correct the weighting function as follows: Shallow sea is defined as water depth. h Satisfying 20m≤ h ≤200m, and the sound velocity profile has a negative gradient abrupt change layer; α The value range is 0.9≤ α ≤1.1, used to compensate for time delay estimation errors caused by sound ray bending.

3. The adaptive time delay estimation method for underwater acoustic environment according to claim 1, characterized in that: In the fifth step of averaging the multi-channel cross-correlation function, when applied in a real-time processing system, the time T for a single delay estimation satisfies... T ≤10ms, achieved through the following measures: Fast Fourier Transform (FFT) is used to accelerate cross-power spectrum calculation, reducing the number of FFT points. Values The range of values ​​for the number of iterations K is limited to: To balance accuracy and speed.

4. The adaptive time delay estimation method for underwater acoustic environment according to claim 1, characterized in that: In the third step of adaptive parameter iterative optimization; formula Among them To estimate the delay, To achieve the true time delay, it is expected that E is achieved by averaging through a sliding window with a window length of 50 ≤ M ≤ 200.

5. The adaptive time delay estimation method for underwater acoustic environment according to claim 1, characterized in that: In the fifth step of averaging the multi-channel cross-correlation function, the formula... Channel weight coefficients in The calculation formula is as follows: ,in The signal-to-noise ratio (SNR) of the i-th and j-th channels is calculated by the ratio of signal energy to noise energy.

6. The adaptive time delay estimation method for underwater acoustic environment according to claim 1, characterized in that: The formula in the second step of constructing the dynamic frequency domain weighting function In , which is a stabilizing factor.

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