Adaptive non-uniform power normalization least mean square error equalization method for underwater acoustic communication based on dynamic leakage factor

Abstract

The application discloses a self-adaptive non-uniform power normalization least mean square error equalization method for underwater acoustic communication based on a dynamic leakage factor, and belongs to the technical field of underwater acoustic communication; the method steps are as follows: a baseband sampling signal x is acquired; a weight value vector w0=0 is initialized, and an input signal vector u0 is a zero vector with a length L; the baseband sampling signal x is taken as an input signal, a training sequence is taken as an expected signal d, and adaptive dynamic updating of filter weight values is realized through real-time adjustment of a leakage factor, so that adaptive adjustment in a signal equalization process is completed; through introduction of a dynamic adjustment strategy of the leakage factor, joint optimization of weight attenuation and power normalization is adopted, so that equalization precision and algorithm convergence speed are effectively balanced, channel distortion is finally accurately compensated and equalized, and the communication quality and stability of the underwater acoustic communication system in a complex marine environment are improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of underwater acoustic communication, and particularly relates to an adaptive non-uniform power normalization least mean square error underwater acoustic communication equalization method based on a dynamic leakage factor. BACKGROUND

[0002] Equalization of underwater acoustic communication channels and adaptive beamforming can be attributed to the optimization problem of adaptive filtering. Through real-time correction and adjustment of signals by adaptive algorithms, an effective method has been developed to solve the problem of complex channel characteristics and noise suppression. Existing methods include the normalized least mean square (NLMS) algorithm and its improved versions, such as the proportional normalized least mean square (PNLMS) algorithm. Details of the proportional normalization-based algorithm can be found in the article "Improved Proportionate Constrained Normalized Least Mean Square for Adaptive Beamforming", published in Circuits, Systems, and Signal Processing, Volume 42, Issue 12, 2023, Pages 7651-7665. Detailed content of the normalized least mean square algorithm based on optimal design can be found in the article "Optimal Design of NLMS Algorithm with a Variable Scaler Against Impulsive Interference", published in Signal, Image and Video Processing, Volume 17, Issue 6, 2023, Pages 2705-2712. Although these documents propose several optimization methods, they still face the problem of performance degradation in non-stationary channels and strong interference environments.

[0003] Due to the highly time-varying nature and multipath effects of underwater acoustic channels, equalization and interference suppression become extremely difficult. In addition, the transmission characteristics of underwater acoustic channels can be influenced by many factors such as turbulence, temperature gradient, pressure change, etc. Traditional adaptive equalization methods often lose the ability to effectively estimate the channel in strong interference, nonlinear and rapidly changing underwater environments. Therefore, considering the unique characteristics and challenges of underwater acoustic communication channels, fine weight adjustment and parameter adaptive optimization of the equalization algorithm are needed to improve the robustness of the signal and the overall stability of the system. Literature [Wu F Y, Song Y C, 2023 (Wu F Y, Song Y C. Optimal design of NLMS algorithm with a variable scaler against impulsive interference [J]. Signal, Image and Video Processing, 2023, 17(6): 2705-2712.)] proposes an improved NLMS algorithm based on a variable scaling factor, which aims to improve the system's ability to cope with impulsive interference, but its convergence speed and stability are still lacking when facing high complexity multipath environments.

[0004] However, the weight update strategy in traditional NLMS and PNLMS algorithms cannot simultaneously meet the needs of channel equalization accuracy and adaptive convergence speed, which leads to the difficulty of balancing equalization effect and algorithm convergence speed. Especially in underwater acoustic communication, the change of channel characteristics is very rapid, and the adaptive ability of traditional algorithms to the environment is insufficient. To solve this problem, the present invention proposes an adaptive non-uniform power normalized least mean square error algorithm based on dynamic leakage factor, which uses dynamic adjustment of leakage factor to continuously adapt to channel changes, thereby improving the equalization performance in underwater multi-path effect and severe noise interference environment. In addition, by introducing a weight decay strategy and using a dynamic leakage factor to enhance the flexibility of weight update, better equalization effect and faster convergence speed are achieved, thereby significantly improving the robustness and signal processing performance of underwater acoustic communication systems. SUMMARY

[0005] Technical problems to be solved:

[0006] In order to avoid the prior art, the present application provides a kind of equalization method of adaptive non-uniform power normalization least mean square error underwater acoustic communication based on dynamic leakage factor, which utilizes dynamic weight adjustment framework, combines with adaptive leakage factor adjustment mechanism, and realizes the robust equalization effect in complex underwater environment by constantly refining the update of filter weight value.The dynamic adjustment strategy of leakage factor is introduced, and the joint optimization of weight attenuation and power normalization is adopted, so that the equalization accuracy and algorithm convergence speed are effectively balanced, and finally the channel distortion is accurately compensated and equalized, to improve the communication quality and stability of underwater acoustic communication system in complex marine environment.

[0007] The technical scheme of the present application is: an equalization method of adaptive non-uniform power normalization least mean square error underwater acoustic communication based on dynamic leakage factor, the specific steps are as follows:

[0008] Obtain baseband sampling signal x;

[0009] Initialize weight vector w0=0, input signal vector u0 is zero vector of length L;And set leakage factor α;

[0010] Take baseband sampling signal x as input signal, training sequence as expected signal d, realize adaptive dynamic update of filter weight by real-time adjustment of leakage factor, to complete adaptive adjustment in signal equalization process.

[0011] The further technical scheme of the present application is: the acquisition method of the baseband sampling signal x is that the signal is obtained through underwater acoustic communication receiving end, the signal is multiplied after signal synchronization and resampling, and then processed by low-pass filter, so as to obtain baseband sampling signal x.

[0012] The further technical scheme of the present application is: the process of adaptive dynamic update of filter weight value is as follows:

[0013] Given input signal x[n], expected signal d[n] and learning rate μ, repeat the following n times iteration:

[0014] Calculate input vector u n =[x[n],x[n-1],…,x[n-L+1]] T , Wherein, L is filter length;

[0015] Calculate estimation error , Wherein, d[n] is expected signal, w n Is the current weight vector;

[0016] Calculate proportion factor , Wherein, q0 is initial proportion constant, δ is regularization factor, for preventing denominator zero;A is algorithm constraint term control factor;

[0017] Computing weight update And computing weight w n+1 =(1-alpha)w n +f, wherein alpha is the current leakage factor.

[0018] A further technical solution of the present application is that the number of iterations of the iterative computation is equal to the length N of the input signal.

[0019] A further technical solution of the present application is that the leakage factor alpha is set to 0.01.

[0020] A further technical solution of the present application is that the adaptive adjustment mechanism of the leakage factor balances the forgetting rate in the weight update process by introducing a dynamic leakage factor and an algorithm constraint term control factor to adapt to the rapid changes of the channel and effectively suppress the influence of the multipath effect.

[0021] An adaptive non-uniform power normalization least mean square error underwater acoustic communication equalization system based on a dynamic leakage factor, comprising an underwater acoustic communication receiving module, a signal processing module, and an adaptive filtering module;

[0022] The underwater acoustic signal is obtained through the underwater acoustic communication receiving module.

[0023] The signal frequency is expanded to the range of the carrier frequency through the signal processing module, and then processed by a low-pass filter to obtain a baseband sampling signal.

[0024] The adaptive filtering module is used for adaptively and dynamically updating the filter weight.

[0025] An electronic device, comprising at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the adaptive non-uniform power normalization least mean square error underwater acoustic communication equalization method based on a dynamic leakage factor.

[0026] A computer readable storage medium, which stores computer instructions for enabling a processor to implement the adaptive non-uniform power normalization least mean square error underwater acoustic communication equalization method based on a dynamic leakage factor when executed by the processor.

[0027] Advantages

[0028] The beneficial effects of the present application are that the present application proposes an adaptive equalization method based on a dynamic leakage factor to optimize the channel equalization performance of a water acoustic communication system. The present application uses a dynamic leakage factor adjustment mechanism combined with a non-uniform power normalized least mean square error (Leaky IPNLMS) method to realize adaptive dynamic updating of filter weights by adjusting the leakage factor in real time, effectively improving the equalization accuracy of the system in a complex water acoustic channel, and still maintaining stable convergence characteristics under complex conditions such as channel multipath effect and high noise interference, so that the stability and robustness of signal transmission are improved. This method not only improves the anti-interference ability of the communication system, but also can more quickly and accurately adapt to the dynamic changes of the channel, so that the water acoustic communication system has better transmission performance and higher communication quality in different underwater environments. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 The operation time of LMS, VSS-NLMS, NLMS, and Leaky IPNLMS algorithms is compared.

[0030] Figure 2 The constellation diagrams after equalization of LMS, VSS-NLMS, NLMS, and Leaky IPNLMS algorithms are shown.

[0031] Figure 3 The bit error rate performance of LMS, VSS-NLMS, NLMS, and Leaky IPNLMS algorithms in different signal-to-noise ratio intervals is shown. DETAILED DESCRIPTION

[0032] The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present application, and cannot be understood as a limitation of the present application.

[0033] Based on the traditional algorithm in water acoustic communication, the change of channel characteristics is very rapid, and the traditional algorithm has insufficient adaptability to the environment, etc. The present application provides an adaptive non-uniform power normalized least mean square error water acoustic communication equalization method based on a dynamic leakage factor, and the specific steps are as follows:

[0034] Step 1: At the receiving end of the water acoustic communication, after signal synchronization and resampling, the processed received signal is multiplied by the carrier, and then processed by a low-pass filter to obtain a baseband sampling signal x, which is used as the input signal of the equalizer;

[0035] Step 2: set the leakage factor α to 0.01, initialize the weight vector w0=0, and the input signal vector u0 is a zero vector with a length of L;

[0036] Step 3: given the input signal x[n], the expected output d[n] and the learning rate μ, repeat the following n th iteration:

[0037] Calculate input vector u n =[x[n],x[n-1],…,x[n-L+1]] T Wherein, x[n] is the current input signal, L is the filter length;

[0038] Calculate the estimation error Wherein, d[n] is the desired signal, w n Is the current weight vector;

[0039] Calculate the scaling factor Wherein, q0 is the initial scaling constant, δ is the regularization factor, used to prevent the denominator from being zero; a is the algorithm constraint term control factor;

[0040] Calculate the weight update And calculate the weight w n+1 =(1-α)w n +f, wherein α is the current leakage factor;

[0041] The iteration number above is equal to the total length N of the input signal.

[0042] Wherein the adaptive leakage factor adjustment mechanism is, by introducing a dynamic leakage factor and a decay coefficient, the forgetting rate in the weight update process is balanced, to adapt to the rapid change of the channel, and effectively suppress the influence of multipath effect.

[0043] The adaptive non-uniform power normalization least mean square error underwater acoustic communication equalization system based on a dynamic leakage factor, comprising an underwater acoustic communication receiving module, a signal processing module, an adaptive filtering module; the underwater acoustic signal is obtained through the underwater acoustic communication receiving module; the signal frequency is expanded to the range of carrier frequency through the signal processing module, and then processed by a low-pass filter, so that the baseband sampling signal is obtained; the adaptive dynamic update of the filter weight is realized through the adaptive filtering module.

[0044] The electronic device comprises at least one processor and a memory in communication connection with the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor, so that the at least one processor can execute the adaptive non-uniform power normalization least mean square error underwater acoustic communication equalization method based on a dynamic leakage factor.

[0045] The computer readable storage medium stores computer instructions, and the computer instructions are used to enable the processor to execute the adaptive non-uniform power normalization least mean square error underwater acoustic communication equalization method based on a dynamic leakage factor when the processor executes.

[0046] The application discloses an adaptive non-uniform power normalization least mean square error equalization method based on a dynamic leakage factor, which is used for signal equalization processing in an underwater acoustic communication system. The method introduces a dynamically adjustable leakage factor in an adaptive filtering algorithm, gradually attenuates the leakage factor according to signal environment changes, realizes stable updating of weights and optimization of equalization effect, thereby effectively suppressing noise interference in a complex underwater acoustic environment and improving signal transmission quality. The application is particularly suitable for underwater acoustic communication environments under multipath interference and large noise background, and provides stronger anti-interference capability and reliability guarantee for the underwater acoustic communication system.

[0047] The above technical solutions are further described below in combination with the drawings:

[0048] Figure 1 The operation time of LMS, VSS-NLMS, NLMS and Leaky IPNLMS algorithms is compared. As shown in the box plot, the operation time of the Leaky IPNLMS algorithm is obviously longer than that of the other three algorithms. The LMS algorithm has the smallest calculation time, followed by the VSS-NLMS and NLMS. The operation time of the Leaky IPNLMS algorithm is widely distributed, and the change from the minimum to the maximum value is also large, which may be due to the introduction of a more complex dynamic leakage factor and weight updating mechanism in each iteration process, thereby increasing the calculation burden. Although the operation time of the Leaky IPNLMS algorithm is the longest, such a cost may be to achieve higher accuracy and robustness in equalization performance. For real-time processing and very sensitive to delay applications, the longer calculation time may be a disadvantage, but for scenarios that require higher equalization effect, such calculation cost may be worthwhile.

[0049] Figure 2 The constellation diagrams of LMS, VSS-NLMS, NLMS and Leaky IPNLMS algorithms after equalization are shown. It can be seen that the constellation diagram of the LMS algorithm is scattered and has a large error, indicating that the algorithm has obvious deficiencies in the process of equalizing the channel. The performance of the VSS-NLMS algorithm after equalization is improved compared with the LMS algorithm, but the points are still not concentrated enough, which shows that the equalization effect of the signal under high noise condition is not good. The constellation diagram of the NLMS algorithm shows slightly better performance, and the scattered points are relatively concentrated, but there is still a certain deviation. The constellation diagram of the Leaky IPNLMS algorithm shows a very concentrated distribution, and the scattered points are almost in the ideal position, indicating that the algorithm can effectively reduce channel distortion and improve the equalization accuracy of the signal. The excellent performance of the Leaky IPNLMS algorithm is attributed to its updating mechanism of the dynamic leakage factor, which can better adapt to the channel characteristics and improve the robustness of the system.

[0050] Figure 3 The bit error rate performance of different algorithms in different signal-to-noise ratio intervals is shown. From the figure, it can be seen that the bit error rate of the LMS algorithm almost completely falls in the highest bit error rate interval, which indicates that it performs poorly in different signal-to-noise ratio conditions. The VSS-NLMS algorithm has some improvement, but the proportion in the higher bit error rate interval is still large. The bit error rate performance of the NLMS algorithm is further optimized, and the coverage area of the low bit error rate interval is larger, showing that the resistance to noise has been improved. However, the Leaky IPNLMS algorithm occupies the highest proportion in the low bit error rate interval, showing a significant performance advantage. This means that the Leaky IPNLMS not only performs stably in high noise conditions, but also can maintain a low bit error rate in various signal-to-noise ratio environments, greatly improving the reliability of communication.

[0051] From the analysis of the above three graphs, it can be concluded that the LMS algorithm has the shortest calculation time due to its simple structure, but performs poorly in terms of equalization performance and bit error rate, especially in conditions of large signal interference or complex channel characteristics. VSS-NLMS and NLMS have improved in terms of equalization effect and bit error rate, but still have deficiencies when facing more complex channels. While the Leaky IPNLMS algorithm has a relatively long calculation time, it has shown obvious advantages in the constellation diagram after signal equalization and bit error rate performance. Its dynamic leakage factor adjustment mechanism can adaptively adjust the weight according to the channel change, significantly improving the equalization accuracy and robustness of the system. For underwater acoustic communication scenarios that require high communication quality, the Leaky IPNLMS algorithm obviously has greater application potential and practical value. By using this method in complex underwater environments, the anti-noise ability and signal equalization accuracy of the system can be significantly improved, thereby ensuring the stability and efficiency of communication.

[0052] Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those skilled in the art can make changes, modifications, replacements and variations to the above embodiments without departing from the principles and purposes of the present application within the scope of the present application.

Claims

1. An adaptive non-uniform power normalization minimum mean square error equalization method for underwater acoustic communication based on dynamic leakage factor, characterized in that The specific steps are as follows: Acquiring baseband sample signals x ; Initialize the weight vector , the input signal vector to be a zero vector of length ; and set the leakage factor ; The baseband sampling signal x As an input signal, the training sequence is taken as an expected signal d The adaptive dynamic update of the filter weight is realized by adjusting the leakage factor in real time to complete the adaptive adjustment in the signal equalization process. The adaptive dynamic updating process of the filter weight is as follows: Given an input signal , a desired signal , and a learning rate , repeat the following for iterations: Computing an input vector wherein, is the filter length; Computing an estimation error wherein, is the desired signal, is the current weight vector; Computing a scaling factor wherein, is an initial scaling constant, is a regularization factor to prevent the denominator from being zero; is an algorithm constraint term control factor; Computing weight updates and computing weights where, is the current leakage factor.

2. The adaptive non-uniform power normalization minimum mean square error equalization method for underwater acoustic communication based on dynamic leakage factor according to claim 1, characterized in that: The baseband sampling signal x The acquisition method is that the signal is acquired by the underwater acoustic communication receiving end, the signal is multiplied with the carrier after signal synchronization and resampling, and then is processed by a low-pass filter, so as to obtain the baseband sampling signal x .

3. The adaptive non-uniform power normalized least mean square error equalization method for underwater acoustic communication based on dynamic leakage factor according to claim 2, characterized in that: The number of iterations is equal to the length of the input signal N .

4. The adaptive non-uniform power normalization least mean square error equalization method for underwater acoustic communication based on dynamic leakage factor according to claim 3, characterized in that: The leakage factor is set to .

5. The adaptive non-uniform power normalization least mean square error equalization method for underwater acoustic communication based on dynamic leakage factor according to claim 4, characterized in that: The adaptive adjustment mechanism of the leakage factor balances the forgetting rate in the weight updating process by introducing a dynamic leakage factor and an algorithm constraint term control factor to adapt to the rapid change of the channel and effectively suppress the influence of the multipath effect.

6. A dynamic-leakage-factor-based adaptive non-uniform power normalized least mean square error equalization system for underwater acoustic communication, for implementing the dynamic-leakage-factor-based adaptive non-uniform power normalized least mean square error equalization method for underwater acoustic communication according to any one of claims 1-5; characterized by: The underwater acoustic communication receiving module, the signal processing module, and the adaptive filtering module are included. An underwater acoustic signal is acquired through the underwater acoustic communication receiving module. The signal frequency is expanded to the range of the carrier frequency through the signal processing module, and then processed by a low-pass filter to obtain a baseband sampling signal. The adaptive dynamic updating of the filter weight is performed through the adaptive filtering module.

7. An electronic device, comprising: The computer readable storage medium stores computer instructions for enabling the processor to implement the adaptive non-uniform power normalization least mean square error underwater acoustic communication equalization method based on the dynamic leakage factor when executed by the processor.

8. A computer-readable storage medium, characterized in that: The computer readable storage medium stores computer instructions for enabling the processor to implement the adaptive non-uniform power normalization least mean square error underwater acoustic communication equalization method based on the dynamic leakage factor when executed by the processor.

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