A method for suppressing underwater self-interference

Abstract

The invention discloses an underwater self-interference suppression method, which is based on a fast bisection coordinate descent recursive least squares algorithm (FDCD_RLS) with a variable forgetting factor. The method comprises the following steps: initializing algorithm parameters, including a forgetting factor, a step size, a number of iterations, an initial error, and an initial value of an autocorrelation matrix; calculating an instantaneous error value at a current moment by updating an input signal vector; calculating the forgetting factor at a current moment according to the instantaneous error value, wherein an inverse tangent function relationship exists between the value of the forgetting factor and the square of the instantaneous error; updating a ring index sequence, and updating the autocorrelation matrix and a pseudo error vector through the ring index sequence; updating a weight increment and a pseudo error vector through a bisection coordinate descent method; updating an estimated self-interference channel parameter at a current moment according to the weight increment; and repeating the above steps until convergence, thereby finally obtaining an estimated value of the underwater self-interference channel.

Description

Technical Field

[0001] The present invention belongs to the field of underwater acoustic communication detection, and in particular to a digital domain self-interference suppression method in self-interference suppression of an underwater transceiver. Background Art

[0002] In recent years, with the continuous development of the field of underwater acoustics, the application of underwater acoustic technology in underwater communications, underwater detection, environmental monitoring and other fields has become increasingly widespread. Whether it is underwater full-duplex communication technology aimed at saving spectrum resources and improving communication efficiency, or integrated underwater communication and detection technology that reduces equipment size, reduces power consumption, and enhances stealth and security, both rely on integrated transceiver systems. However, during the operation of an integrated transceiver system, the strong power signal from the transmitter can directly interfere with the receiver, causing the desired signal at the far end to be overwhelmed by the strong self-interference signal, thereby hindering the reception of the desired signal at the far end. Therefore, self-interference suppression has become a key issue that needs to be addressed in integrated transceiver systems and has been widely researched and developed.

[0003] The underwater environment is complex and ever-changing. As underwater transceivers operate, they are affected by movement and changes in the water environment, causing significant changes in the self-interference underwater acoustic multipath channel. Therefore, real-time estimation of the self-interference channel parameters is necessary to accurately reconstruct and eliminate the self-interference signal from the transmitter to the receiver, thereby extracting the desired signal.

[0004] In the digital domain self-interference suppression method, an adaptive filtering algorithm is often used to achieve real-time estimation of the self-interference channel. The multipath delay channel model is highly compatible with the FIR transverse filter model, so the adaptive transverse filter can be used to simulate the actual underwater self-interference channel model. The transmitter signal and the receiver signal of the transceiver system are used as the reference signal and the expected signal of the adaptive filter respectively. The self-interference system parameters are estimated through iterative calculations. Real-time feedback is provided based on the error after cancellation, which can quickly correct the filter coefficients. This can make the estimated filter coefficients closer to the actual real-time changing self-interference channel, so as to achieve real-time reconstruction and cancellation of the self-interference signal, helping the signal at the receiving end to eliminate the interference signal from its own transmitter, and thus realize the extraction of the desired signal at the far end.

[0005] The adaptive channel estimation algorithms used in underwater transceivers mainly include the LMS algorithm and its improved algorithm, the RLS algorithm and its improved algorithm, the DCD_RLS algorithm and its improved algorithm, etc. Among them, the LMS algorithm and its improved algorithm perform iterative operations through gradient descent. The algorithm operation complexity is low, but the channel estimation accuracy is low, the convergence speed is slow, and the tracking performance is poor. The RLS algorithm introduces a forgetting factor and obtains the least squares solution by iteratively updating the inverse matrix of the covariance matrix. The channel estimation accuracy is high and the convergence speed is fast, but the amount of operation is large, and the operation complexity is O(N 2 ), especially when the order N is high, it consumes a lot of computing resources and computing time. The DCD_RLS algorithm no longer uses matrix operations, but instead obtains the least squares solution through bisection coordinate descent, which greatly reduces the amount of calculation, and the estimation accuracy is still high. However, during the iterative operation of the algorithm, the autocorrelation matrix still needs to be fully updated in each iteration, which consumes a lot of computing resources and takes a long time. The FDCD_RLS algorithm is an improved algorithm based on the DCD_RLS algorithm. In each iteration, it does not update the entire autocorrelation matrix in its entirety, but only updates one element in the matrix through a ring index, thereby further reducing the computational complexity and improving the computing speed.

[0006] At the same time, in the RLS, DCD_RLS, and FDCD_RLS algorithms, the forgetting factor λ significantly affects the algorithm's estimation accuracy and convergence speed. λ typically ranges from 0 to 1 and represents the weight of the influence of past input data on the channel parameter estimate. The closer λ is to 1, the higher the estimation accuracy, but the slower the convergence speed. The smaller λ is, the lower the algorithm's estimation accuracy, but the faster the convergence speed, enabling better tracking of changing channels. With a fixed forgetting factor, estimation accuracy and convergence speed are often mutually exclusive. Therefore, in the application scenario of eliminating self-interference signals in underwater transceivers, it is urgent to propose an algorithm with high computational accuracy, fast convergence speed, and low computational complexity to achieve real-time estimation of complex and variable underwater self-interference channels and achieve real-time cancellation of self-interference signals. Summary of the Invention

[0007] The purpose of the present invention is to overcome the deficiencies in the prior art and provide an underwater self-interference suppression method based on a fast DCD_RLS algorithm with a variable forgetting factor (VFF_FDCD_RLS), so that the transceiver has both high steady-state accuracy and fast convergence tracking speed when performing self-interference signal suppression.

[0008] The purpose of the present invention is achieved through the following technical solutions:

[0009] An underwater self-interference suppression method based on the bipartite coordinate descent recursive least squares algorithm FDCD_RLS with variable forgetting factor includes:

[0010] Initialize the algorithm parameters, including the forgetting factor, step size, number of iterations, initial error, and initial value of the autocorrelation matrix;

[0011] By updating the input signal vector, the instantaneous error value at the current moment is calculated;

[0012] The forgetting factor at the current moment is calculated based on the instantaneous error value. There is an inverse tangent function relationship between the value of the forgetting factor and the square of the instantaneous error;

[0013] Performing a circular index sequence update, and updating the autocorrelation matrix and the pseudo error vector through the circular index sequence;

[0014] Update the weight increment and pseudo error vector by bisection coordinate descent method;

[0015] Update the estimated self-interference channel parameters at the current moment according to the weight increment;

[0016] Repeat the above steps until convergence, and finally obtain the estimated value of the underwater self-interference channel.

[0017] Furthermore, the initial value of the autocorrelation matrix is:

[0018]

[0019] is the preset initial value;

[0020] Input signal vector is a one-dimensional vector of order N

[0021]

[0022] Calculate the instantaneous error value at the current moment :

[0023]

[0024] Where d(n) is the receiving end signal. In the scenario of a transceiver integrated system, d(n) includes the self-interference signal from the transmitter, the far-end desired signal, and the environmental noise.

[0025] Furthermore, the calculation formula of the forgetting factor λ is as follows:

[0026]

[0027] The value of the forgetting factor λ is dynamically adjusted according to the square of the instantaneous error e at the previous moment, where is the middle value of the forgetting factor, A is the variable amplitude of the forgetting factor, parameter B controls the slope of the relationship curve between the forgetting factor and the instantaneous error e, that is, the rapidity of the change of the forgetting factor, and parameter E is the average value of the square of the instantaneous error; when e 2 When E, the forgetting factor λ= .

[0028] Furthermore, the ring index sequence is set to I(N), and its update method is:

[0029]

[0030] In each row i∈{1,2,…,N}, mod is the remainder function, and the value of mod(a,b) is the remainder after a is divided by b. When a=0 or a=b, its mod value is b;

[0031] Then the autocorrelation matrix is ​​calculated based on the ring index sequence. To update, only the elements in the corresponding column of the ring index sequence are updated in each row i. The update method is:

[0032]

[0033] Then the pseudo error vector renew:

[0034]

[0035] λ is the forgetting factor, is the instantaneous error value, is the input signal vector.

[0036] Furthermore, updating the weight increment and the pseudo error vector by the bisection coordinate descent method includes: iteratively calculating the step size h and the weight increment Δw, adjusting the estimated self-interference channel parameters at each moment, so as to gradually reduce the error of the pseudo error vector.

[0037] Furthermore, according to the dynamic changes of the underwater self-interference channel, the forgetting factor can be reduced to accelerate convergence when the channel changes rapidly, and the forgetting factor can be increased to improve the estimation accuracy when the channel is stable, thereby ensuring efficient self-interference signal suppression capability.

[0038] The present invention also provides an application of an underwater self-interference suppression method, which is applied to an underwater transceiver integrated system. During underwater communication or detection, real-time self-interference signal suppression is performed through digital domain adaptive filtering to improve the reception quality of the far-end desired signal.

[0039] The present invention also provides a self-interference suppression system for an underwater transceiver, comprising:

[0040] The signal acquisition module is used to simultaneously acquire the transmitting end signal and the receiving end signal of the underwater transceiver; the signal acquisition module includes the transmitting end signal acquisition after the power amplifier or the signal acquisition after the power amplifier nonlinear compensation;

[0041] An adaptive filtering module, configured to estimate the self-interference channel of the collected signal using the underwater self-interference suppression method and generate a signal after cancellation;

[0042] The processing module is used to update the self-interference channel parameters in real time according to the dynamically adjusted forgetting factor to achieve signal cancellation.

[0043] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the underwater self-interference suppression method when executing the program.

[0044] The present invention also provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the underwater self-interference suppression method are implemented.

[0045] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0046] 1. Reduce computational complexity and improve computational speed: Traditional RLS and DCD_RLS algorithms require a global update of the entire autocorrelation matrix, which results in high computational complexity. Especially in the case of high-order filters, the computational overhead and time consumption are high. The present invention introduces a circular index mechanism to update only a single element in the autocorrelation matrix, thus avoiding the computational overhead of a global update of the entire matrix. This optimization effectively reduces the amount of computation, thereby significantly improving the computational speed and reducing the consumption of computing resources. In the application scenario of high-order filters, compared to traditional algorithms, the present invention can quickly adapt to dynamically changing underwater environments while ensuring high precision, thereby improving real-time processing capabilities.

[0047] 2. Adaptive performance optimization: The present invention introduces a variable forgetting factor mechanism to dynamically adjust the value of the forgetting factor according to the size of the instantaneous error.

[0048] When the channel changes rapidly, the algorithm can automatically reduce the forgetting factor, thereby accelerating convergence and quickly tracking the changing channel parameters; when the channel is stable, the forgetting factor increases, thereby improving the steady-state estimation accuracy and optimizing the suppression effect of self-interference signals.

[0049] This design achieves a dynamic balance between estimation accuracy and convergence speed, enabling the algorithm to provide excellent adaptive performance under various environmental conditions, especially adapting to the changing underwater environment, and solving the problem that the fixed forgetting factor algorithm cannot balance accuracy and convergence speed.

[0050] 3. Improved channel estimation accuracy: By optimizing the weight update strategy and dynamically adjusting the variable forgetting factor, the present invention can accurately estimate multipath channels in complex underwater environments.

[0051] This algorithm makes the channel estimation result closer to the actual channel parameters, providing a solid foundation for subsequent interference signal reconstruction and elimination. This advantage ensures the efficient removal of self-interference signals during the suppression process, improves the quality of the received signal, and effectively enhances the reliability and stability of the signal.

[0052] 4. Enhance environmental adaptability:

[0053] The underwater environment is usually highly dynamic, including changes in water flow, movement of target objects, and fluctuations in noise levels. These factors lead to rapid changes in channel parameters.

[0054] By dynamically adjusting the forgetting factor, the present invention enables the algorithm to respond quickly to drastic channel changes, maintaining efficient interference suppression. Even in stable environments, the algorithm maintains high estimation accuracy, enhancing its adaptability to complex and changing environments. Compared with traditional algorithms with fixed forgetting factors, the present invention provides more stable and accurate signal processing capabilities in more complex scenarios, meeting the requirements of underwater communication and detection systems.

[0055] 5. Improve system stability and real-time performance:

[0056] Compared to traditional DCD_RLS and FDCD_RLS algorithms, the VFF_FDCD_RLS algorithm of the present invention can rapidly estimate the self-interference channel even when the signal fluctuates significantly, and eliminate the self-interference signal with high real-time accuracy, avoiding the delays that may occur in traditional algorithms. The algorithm maintains stable performance under different operating conditions, improving the signal processing capabilities of the underwater transceiver system and reducing the system's computational burden.

[0057] 6. Strong system integration:

[0058] The algorithm of this invention not only boasts high computational efficiency and strong adaptability, but also integrates well with existing underwater transceiver systems. Based on a design using a variable forgetting factor and bipartite coordinate descent, it adapts to diverse operating environments and channel conditions, providing efficient and reliable technical support for underwater communications, detection, monitoring, and other fields.

[0059] In summary, the technical solution of the present invention has significant advantages over the existing technology. It can effectively reduce the computational complexity, improve the algorithm convergence speed and accuracy, enhance the system's adaptability, and significantly improve the performance and real-time performance of the underwater self-interference suppression system. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 The following illustrates the implementation steps of the fast DCD_RLS algorithm (VFF_FDCD_RLS) based on the variable forgetting factor in this embodiment.

[0061] Figure 2 The selection rule of the forgetting factor is shown, that is, the functional curve of the relationship between the forgetting factor and the instantaneous error.

[0062] Figure 3 The figure shows the comparison of the self-interference channel estimation results of VFF_DCD_RLS and FDCD_RLS algorithms with fixed forgetting factor under simulation tests.

[0063] Figure 4 The following figure shows the application scenario of self-interference suppression of underwater transceiver. DETAILED DESCRIPTION

[0064] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0065] Example 1

[0066] This embodiment provides a self-interference signal suppression method for underwater self-interference suppression based on a fast DCD_RLS algorithm with a variable forgetting factor (VFF_FDCD_RLS). This algorithm (VFF_FDCD_RLS) can adjust the forgetting factor of the algorithm in real time based on the magnitude of the instantaneous error, thereby controlling the self-interference channel estimation accuracy and convergence speed during the algorithm's iterative operation. When the underwater self-interference channel changes, the algorithm can achieve rapid convergence by reducing the forgetting factor and track the real-time changes in the self-interference channel parameters. When the environment remains stable, the algorithm can improve channel estimation accuracy and self-interference signal suppression effectiveness by increasing the forgetting factor. Furthermore, this algorithm is an improvement on the FDCD_RLS algorithm. Instead of updating the entire autocorrelation matrix in each iteration, only one element in the matrix is ​​updated using a ring indexing method. The new autocorrelation matrix R is then used to obtain the least-squares solution using the bisection coordinate descent method. This further reduces the computational effort and computational complexity compared to the DCD_RLS algorithm.

[0067] For each time n, it is known that the objective functions of the traditional RLS algorithm and the DCD_RLS algorithm are

[0068]

[0069] Similarly, in the FDCD_RLS algorithm, the objective function is also the formula. Similar to the DCD_RLS algorithm, this embodiment introduces a pseudo error vector to iteratively update the change vector of the filter coefficient, where the pseudo error vector is

[0070]

[0071] It is known that in the process of solving the least squares solution,

[0072]

[0073] in is the gain vector, which can be substituted into the above formula to obtain

[0074]

[0075] According to the recursive formula of the autocorrelation matrix The recursive formula for the pseudo error vector can be obtained as

[0076]

[0077] Then, Δw is adjusted by the bisection coordinate descent method so that As much as possible, so as to obtain the optimal weight change Δw for updating the estimated self-interference channel parameter (weight) w at the current moment, and to obtain a new pseudo error vector .

[0078] The implementation process of the VFF_FDCD_RLS algorithm involved in this embodiment is similar to the basic principle of the DCD_RLS algorithm implementation process, but the update of the autocorrelation matrix R and the pseudo error vector in the bipartite coordinate descent process are different. The update is different and the implementation steps are as follows Figure 1 , as follows:

[0079] The first step is to initialize the algorithm parameters. Set the forgetting factor, step size, number of iterations, initial error, and the initial value of the autocorrelation matrix. The initial value of the autocorrelation matrix is:

[0080]

[0081] The default initial value.

[0082] The second step is to update the input signal vector , which is an N-order one-dimensional vector

[0083]

[0084] Calculate the instantaneous error value at the current moment ,

[0085]

[0086] Where d(n) is the receiving end signal. In the scenario of a transceiver integrated system, d(n) includes the self-interference signal from the transmitter, the far-end desired signal, and the environmental noise.

[0087] The third step is to calculate the forgetting factor value at the current moment based on the instantaneous error.

[0088]

[0089] The value of the forgetting factor λ is completely based on the instantaneous error e at the previous moment, and its value is related to the square of the instantaneous error e by an inverse tangent function, where is the middle value of the forgetting factor, A is the variable amplitude of the forgetting factor, parameter B controls the slope of the relationship curve between the forgetting factor and the instantaneous error e, that is, the rapidity of the change of the forgetting factor, and parameter E is the average value of the square of the instantaneous error, which also controls the left and right horizontal shift of the function curve. 2 When E, the forgetting factor λ is just The relationship between the forgetting factor and the instantaneous error e is as follows Figure 2 shown.

[0090] The fourth step is to update the ring index array, the autocorrelation matrix R, and the pseudo error vector. For each row i∈{1,2,…,N}, the position of the current row is determined by the ring index. The ring index sequence is set to I(N), and its update method is

[0091]

[0092] Where mod is the remainder function, and the value of mod(a,b) is the remainder after a is divided by b. If a=0 or a=b, its mod value is b.

[0093] Then the autocorrelation matrix is ​​updated according to the ring index sequence, and only the elements of the corresponding ring index sequence column are updated in each row. The update method is

[0094]

[0095] Then the pseudo error vector update is performed,

[0096]

[0097] The fifth step is to solve the weight increment Δw and the new pseudo error vector by the bisection coordinate descent method. The specific implementation method is to iteratively update the weight increment coefficient corresponding to the position with the largest absolute value in the pseudo error vector according to the preset number of iterations. The implementation steps are:

[0098] (1) Initialize the step size and weight increment Δw.

[0099] (2) Find the position of the current maximum residual and the position of its corresponding index sequence, set the position as p(p∈{1,2…N}), and calculate p':

[0100]

[0101] I(1) represents the column index currently used in the first row of the autocorrelation matrix R. The value of p' is used to find the update position index corresponding to the weight w(p) in the first row of the autocorrelation matrix R. Because R uses a circular storage, the correct position needs to be found through the circular index when updating.

[0102] (3) Calculate the update step size h. Calculate the step size h by using the bisection coordinate descent method so that it can meet the following conditions. If it does not meet the conditions, divide h by 2 until it meets the following formula or reaches the preset bisection upper limit.

[0103]

[0104] (4) Update the weight increment Δw and the new pseudo error vector .

[0105]

[0106] The sign function is the sign function, which represents the update of Δw. Then the pseudo error vector is updated iteratively. .here The update is done iteratively one by one according to the ring index.

[0107]

[0108] Here i∈{1,2,…,N}, j is the ring index coordinate when the position of the maximum residual error is p, and its change rule is to decrease from p to 0 and then continue to increase to Np, and the change rule of k is to increase from one to j to 0 and then remain unchanged. The above is the N-order pseudo error vector Update method under ring index. So far, the weight increment Δw and the new pseudo error vector All solutions have been found, and the bipartite coordinate descent process is complete.

[0109] The sixth step is to update the filter coefficients at this moment according to the weight increment Δw obtained by the bisection coordinate descent method, that is, to estimate the self-interference channel parameters. for

[0110]

[0111] At this point, the iteration ends, the input data vector continues to be updated, and the next iteration is entered to solve w so that it can be close to the actual self-interference channel.

[0112] Example 2

[0113] This embodiment provides a self-interference suppression system for an underwater transceiver, including:

[0114] The signal acquisition module is used to simultaneously acquire the transmitting end signal and the receiving end signal of the underwater transceiver; the signal acquisition module includes the transmitting end signal acquisition after the power amplifier or the signal acquisition after the power amplifier nonlinear compensation;

[0115] An adaptive filtering module, configured to estimate the self-interference channel of the collected signal using the underwater self-interference suppression method described in Example 1 and generate a signal after cancellation;

[0116] The processing module is used to update the self-interference channel parameters in real time according to the dynamically adjusted forgetting factor to achieve signal cancellation.

[0117] Application scenarios of underwater transceiver self-interference suppression system include Figure 4 In the application scenario of underwater self-interference suppression, the self-interference signal of the transceiver will cause strong interference to the receiving end when performing underwater communication or detection, and the self-interference channel parameters will change due to changes in the underwater environment. Therefore, the self-interference signal needs to be reconstructed and filtered out in the digital domain through adaptive filtering in the signal collected by the receiving end, thereby realizing the extraction of useful signals.

[0118] The specific implementation steps are:

[0119] First, when the underwater transceiver is simultaneously transmitting and receiving signals, it collects both the transmitting and receiving signals and transfers them to the digital domain for further processing. To avoid nonlinear distortion caused by the power amplifier after amplification, the signal acquisition module can collect the transmitting signal after the power amplifier transmits it, or use the digital signal before transmission to compensate for the nonlinearity of the power amplifier.

[0120] Furthermore, in the digital domain, the order of the digital filter is set by the channel length of the working environment, the number of iterations of the VFF_FDCD_RLS algorithm at a single moment is set by testing the sparsity of the channel, and the appropriate forgetting factor change function is determined by testing parameters such as the working environment noise level. Parameters, including the intermediate value of the forgetting factor , change range A, error square shift range value E, and forgetting factor change slope parameter B. At the same time, it is also necessary to set the algorithm initialization input vector, pseudo error vector And parameters such as autocorrelation matrix R and step size.

[0121] Furthermore, the collected transmitter signal and receiver signal are used as the reference signal x(n) and the expected signal d(n) of the adaptive filter, respectively, and input into the VFF_FDCD_RLS algorithm in the adaptive filtering module. At each moment n, the N-order input signal vector is updated at each moment. , taking x(n) and d(n) as the input signals of the adaptive filter, perform iterative operations at each moment.

[0122] In each iteration, the input signal vector x(n) is convolved with the latest estimated self-interference channel parameter w obtained at the previous moment, and then subtracted from the expected signal d(n) to obtain the instantaneous error value e at that moment. Then, the forgetting factor λ and the pseudo error vector at the current moment are updated according to the instantaneous error value. .

[0123] Further, a ring index operation is performed to update the ring index vector I(n), and then the autocorrelation matrix is ​​updated by the ring index vector, and the new autocorrelation matrix R and the error vector are obtained. As the input signal of the bipartite coordinate descent function, the ring index vector I(n) is used as the auxiliary signal to calculate the weight increment Δw of the estimated self-interference channel parameter (weight) w at the current moment and the new pseudo error vector , the calculation method is the binary coordinate descent method in the technical solution, which obtains the optimal step size by binary step size, and updates the weight increment Δw at the position with the largest absolute value in the pseudo error vector, and then updates the pseudo error vector again by ring index. Update them one by one.

[0124] Finally, the obtained weight increment Δw is used to correct the filter coefficient value at the current moment, that is, to estimate the self-interference channel parameter w, and update the new pseudo error vector , and then enters the next iteration loop.

[0125] After completing the above steps, the value w obtained at each moment is the estimated value of the underwater acoustic channel h at that moment. The accuracy of this estimate directly affects the effectiveness of self-interference signal cancellation. The resulting error signal e is the signal after digital cancellation at the receiving end. After adaptive cancellation, e only contains the far-end desired signal, the noise signal, and the residual self-interference signal. The vast majority of the self-interference signal in the received signal is eliminated, allowing further analysis of the canceled signal to extract relevant information.

[0126] Preferably, the embodiments of the present application also provide a specific implementation of an electronic device capable of implementing all steps of the underwater self-interference suppression method in the above embodiment, and the electronic device specifically includes the following contents:

[0127] Processor, memory, communications interface, and bus;

[0128] Among them, the processor, memory, and communication interface communicate with each other through the bus; the communication interface is used to realize information transmission between related devices such as server-side devices, metering devices, and user-side devices.

[0129] The processor is used to call the computer program in the memory, and when the processor executes the computer program, all the steps in the underwater self-interference suppression method in the above embodiment are implemented.

[0130] An embodiment of the present application also provides a computer-readable storage medium capable of implementing all steps of the underwater self-interference suppression method in the above-mentioned embodiment. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements all steps of the underwater self-interference suppression method in the above-mentioned embodiment.

[0131] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences between the other embodiments. In particular, the hardware + program embodiments are generally similar to the method embodiments, so their description is relatively simple. For relevant portions, refer to the description of the method embodiments.

[0132] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0133] Although this application provides method steps such as the embodiments or flowcharts, more or fewer steps may be included based on routine or non-inventive work. The order of steps listed in the embodiments is merely one of many possible execution sequences and does not represent the only execution sequence. When executed in an actual device or client product, the methods may be executed sequentially according to the embodiments or the accompanying drawings, or in parallel (for example, in a parallel processor or multi-threaded processing environment).

[0134] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0135] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0136] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0137] The present invention is not limited to the embodiments described above. The above description of the specific embodiments is intended to describe and illustrate the technical solutions of the present invention. The above specific embodiments are merely illustrative and not restrictive. Without departing from the scope of the present invention and the scope of protection of the claims, those skilled in the art may make various specific modifications based on the teachings of the present invention, all of which fall within the scope of protection of the present invention.

Claims

1. An underwater self-interference suppression method based on the fast bisection coordinate descent recursive least squares algorithm FDCD_RLS with variable forgetting factor, characterized in that: include: Initialize the algorithm parameters, including the forgetting factor, step size, number of iterations, initial error, and initial value of the autocorrelation matrix; By updating the input signal vector, the instantaneous error value at the current moment is calculated; The forgetting factor at the current moment is calculated based on the instantaneous error value. There is an inverse tangent function relationship between the value of the forgetting factor and the square of the instantaneous error; Performing a circular index sequence update, and updating the autocorrelation matrix and the pseudo error vector through the circular index sequence; The circular index sequence is set to I(N), and its update method is: ； In each row i∈{1,2,…,N}, mod is the remainder function, and the value of mod(a,b) is the remainder after a is divided by b. When a=0 or a=b, its mod value is b; Then the autocorrelation matrix is ​​calculated based on the ring index sequence. To update, only the elements in the corresponding column of the ring index sequence are updated in each row i. The update method is: ； Then the pseudo error vector renew: ； λ is the forgetting factor, is the instantaneous error value, is the input signal vector; Update the weight increment and pseudo error vector by bisection coordinate descent method; Update the estimated self-interference channel parameters at the current moment according to the weight increment; Repeat the above steps until convergence, and finally obtain the estimated value of the underwater self-interference channel.

2. The underwater self-interference suppression method according to claim 1, characterized in that: The initial values ​​of the autocorrelation matrix are: ； is the preset initial value; Input signal vector is a one-dimensional vector of order N ； Calculate the instantaneous error value at the current moment : ； Where n is the time, is the estimated self-interference channel parameter at the current moment, T is the vector transpose; d(n) is the receiving end signal. In the scenario of a transceiver integrated system, d(n) includes the self-interference signal from the transmitter, the far-end desired signal, and the ambient noise.

3. The underwater self-interference suppression method according to claim 1, characterized in that: The calculation formula of the forgetting factor λ is as follows: ； The value of the forgetting factor λ is dynamically adjusted according to the square of the instantaneous error e at the previous moment, where is the middle value of the forgetting factor, A is the variable amplitude of the forgetting factor, parameter B controls the slope of the relationship curve between the forgetting factor and the instantaneous error e, that is, the rapidity of the change of the forgetting factor, and parameter E is the average value of the square of the instantaneous error; when e 2 When E, the forgetting factor λ= .

4. The underwater self-interference suppression method according to claim 1, characterized in that: Updating the weight increment and the pseudo error vector by the bisection coordinate descent method includes: iteratively calculating the step size h and the weight increment Δw, adjusting the estimated self-interference channel parameters at each moment, and gradually reducing the error of the pseudo error vector.

5. The underwater self-interference suppression method according to claim 1, characterized in that: According to the dynamic changes of the underwater self-interference channel, the forgetting factor can be reduced to accelerate convergence when the channel changes rapidly, and the forgetting factor can be increased to improve the estimation accuracy when the channel is stable, ensuring efficient self-interference signal suppression capability.

6. An underwater transceiver self-interference suppression system, characterized in that: include: A signal acquisition module is used to simultaneously acquire the transmitting end signal and the receiving end signal of the underwater transceiver; The signal acquisition module includes the signal acquisition of the transmitter after the power amplifier or the signal acquisition after the power amplifier nonlinear compensation; An adaptive filtering module, configured to estimate the self-interference channel of the collected signal using the underwater self-interference suppression method according to any one of claims 1 to 5, and generate a signal after cancellation; The processing module is used to update the self-interference channel parameters in real time according to the dynamically adjusted forgetting factor to achieve signal cancellation.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the underwater self-interference suppression method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the underwater self-interference suppression method according to any one of claims 1 to 5 are implemented.

Images