A strong and weak signal adaptive cancellation method and system based on complex structure

Abstract

This invention discloses a method and system for adaptive cancellation of strong and weak signals based on complex structures. The method includes: using a complex RLS adaptive filtering algorithm to track and filter strong interference signals in a complex observation signal to obtain a filtered output signal; during filtering, applying a new filter coefficient vector obtained iteratively from the current complex observation signal to the input complex observation signal after a delay of N periods, thereby decoupling the filter coefficient update from the signal filtering in time, allowing the filtering and filter coefficient update processes to be executed asynchronously; subtracting the filtered output signal from the desired signal to achieve adaptive cancellation of strong signals; and extracting weak signal features from the cancelled signal. This invention fully utilizes the complete information of complex signals in amplitude and phase, overcoming the limitations of traditional real-number processing methods in separating overlapping spectrum signals. It is particularly suitable for complex scenarios where strong and weak signals have similar spectra and large power differences, and has the advantages of high precision, low distortion, and strong real-time performance.

Description

Technical Field

[0001] This invention belongs to the field of signal processing and electronic reconnaissance technology, specifically relating to an adaptive cancellation method and system for strong and weak signals based on complex structures. Background Technology

[0002] In modern electronic reconnaissance and communication systems, the simultaneous existence of multiple signals in the same space and frequency band has become a common phenomenon. With the increasing scarcity of communication spectrum resources and the intensifying complexity of electromagnetic environments, the problem of mutual interference between signals is becoming increasingly prominent. Especially in scenarios where strong and weak signals coexist, weak signals often carry important target information, but because strong signals have the upper hand in energy, they easily mask the characteristics of weak signals, posing significant challenges to their detection and extraction. This problem is even more pronounced in the application of two-tone signals (DTMS), as DTMS signals typically have similar frequencies and time-frequency characteristics, making it difficult for traditional signal separation and detection methods to work effectively in such complex environments.

[0003] Existing technologies commonly employ interference suppression methods such as adaptive filtering, beamforming-based spatial filtering, and frequency domain processing-based signal separation. However, these methods all have certain limitations. First, traditional linear filtering methods struggle to achieve accurate separation when strong and weak signals exhibit significant spectral overlap, often resulting in residual interference. Second, most existing methods are based on real-valued signal models, failing to fully utilize the phase information and amplitude characteristics of complex signals, thus limiting their accuracy when processing two-tone or complex-modulated signals. Finally, some interference suppression methods introduce distortion while suppressing strong signals, weakening or even destroying the characteristics of weak signals, thereby affecting subsequent analysis and processing.

[0004] Patent application CN202210072376.8 discloses a weak signal extraction method based on coherence and filtering coefficients. Specifically, it includes: firstly, performing conventional denoising on seismic data, then calculating the coherence coefficient to separate the effective signal from the noise, and introducing orthogonal similarity for quality monitoring, but does not use an adaptive cancellation method to extract the weak signal.

[0005] Patent application CN201810474461.0 discloses a method and system for weak signal extraction, specifically including: performing FFT transform on the measured signal to obtain frequency component information, and then combining it with cross-correlation detection to recover the weak signal. This method avoids multiple iterations and reduces computational load by combining FFT and cross-correlation, and has higher efficiency and practicality compared to the method combining autocorrelation and cross-correlation. However, it does not address the complex environment where strong and weak signals coexist and have similar characteristics, and does not perform real-time elimination of strong signals. Summary of the Invention

[0006] In view of the above, in order to solve the technical problem that the detection and extraction of weak signals by strong signals is very difficult when there are signals with similar time-frequency domain characteristics in the same space, the present invention provides a method and system for adaptive cancellation of strong and weak signals based on complex structure. Specifically, the strong signal is first subjected to adaptive cancellation filtering, and after the strong signal is eliminated, the weak signal is subsequently detected and extracted, thereby realizing the adaptive cancellation and extraction of dual-tone signals. It is suitable for extracting weak target signals in dual-tone signals under strong interference background.

[0007] To achieve the above-mentioned objectives, an embodiment provides a method for adaptive cancellation of strong and weak signals based on complex structures, comprising the following steps: The complex RLS adaptive filtering algorithm is used to track and filter strong interference signals in the complex observation signal to obtain the filtered output signal. During filtering, the new filter coefficient vector obtained by iterating the current complex observation signal is applied to the input complex observation signal after N periods with a delay, so as to decouple the filter coefficient update from the signal filtering in time, so that the filtering and filter coefficient update processes are executed asynchronously. The strong signal is adaptively canceled by subtracting the filtered output signal from the desired signal, and the weak signal features are extracted from the canceled signal.

[0008] To achieve the above-mentioned objectives, the embodiments also provide a strong-weak signal adaptive cancellation system based on a complex structure, comprising: The filtering module is used to track and filter strong interference signals in complex observation signals using a complex RLS adaptive filtering algorithm to obtain a filtered output signal. During filtering, the new filter coefficient vector obtained by iterating the current complex observation signal is applied to the input complex observation signal after N cycles with a delay, so as to decouple the filter coefficient update from the signal filtering in time, so that the filtering and filter coefficient update processes are executed asynchronously. The weak signal extraction module is used to subtract the filtered output signal from the desired signal to achieve adaptive cancellation of strong signals, and to perform spectral analysis on the canceled signal to extract weak signal features.

[0009] To achieve the above-mentioned objectives, the embodiments also provide a computing device, including a memory and one or more processors, wherein the memory stores executable code, and when the one or more processors execute the executable code, it is used to implement the above-mentioned adaptive cancellation method for strong and weak signals based on complex number structures.

[0010] To achieve the above-mentioned objectives, the embodiments also provide a computer-readable storage medium storing a program thereon, which, when executed by a processor, implements the above-mentioned adaptive cancellation method for strong and weak signals based on complex number structures.

[0011] Compared with the prior art, the beneficial effects of the present invention include at least the following: This invention employs a complex RLS adaptive filtering algorithm for real-time tracking and estimation of strong interference signals. Its significant innovation lies in the time decoupling of weight coefficient updates and signal filtering. Specifically, the filtering weight coefficients obtained in the current iteration are delayed before being applied to subsequent input complex observation signals, allowing the filtering and update processes to be executed asynchronously. This greatly alleviates the computational load within a single clock cycle and significantly improves the system's applicability and reliability in high-speed real-time signal processing. Subsequently, efficient cancellation is achieved by subtracting the estimated strong interference components from the desired signal. The canceled signal is dominated by weak signals and noise, and weak signal features are extracted through spectral analysis. Finally, the cancellation performance and weak signal extraction quality are evaluated by comparing the time-domain waveforms and power spectra of the signals before and after cancellation. This invention fully utilizes the complete amplitude and phase information of complex signals, overcoming the limitations of traditional real-number processing methods in separating overlapping spectrum signals. It is particularly suitable for complex scenarios where strong and weak signals have similar spectra and large power differences, offering advantages such as high precision, low distortion, and strong real-time performance. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart of the adaptive cancellation method for strong and weak signals based on complex structures provided in the embodiment; Figure 2 The power spectral density of the received signal, the power spectral density of the target weak signal, and the power spectral density of the strong signal interference cancellation result are provided in the embodiment under the condition of strong self-noise interference SJR=-15dB. Figure 3 This is a time-domain diagram showing the cancellation results of the received signal and the weak and strong signal interference from the target signal under strong self-noise interference SJR=-15dB provided in the embodiment. Figure 4 The power spectral density of the received signal, the power spectral density of the target signal, and the power spectral density of the strong signal interference cancellation result are provided in the embodiment, where the strong signal interference and the target weak signal are independent of each other and the interference-to-signal ratio is SJR=-15dB. Figure 5 This is a time-domain diagram of the received signal, the target signal, and the interference cancellation result when the strong signal interference and the weak target signal are independent of each other and the interference-to-signal ratio is SJR=-15dB, as provided in the embodiment. Figure 6The power spectral density of the received signal, the power spectral density of the target signal, and the power spectral density of the strong signal interference cancellation result are provided in the embodiment, where the strong signal interference and the target weak signal are weakly correlated and the interference-to-signal ratio is SJR=-15dB. Figure 7 This is a time-domain diagram of the cancellation results of the received signal, the target signal, and the strong signal interference under the condition that the strong signal interference and the target weak signal are weakly correlated and the interference-to-signal ratio is SJR=-15dB, as provided in the embodiment. Figure 8 This is a schematic diagram of the strong and weak signal adaptive cancellation system based on a complex structure provided in the embodiment. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the scope of protection of this invention.

[0015] The inventive concept of this invention is as follows: This embodiment provides a strong-weak signal adaptive cancellation scheme based on a complex structure. The RLS adaptive filtering algorithm in complex space protects the mathematical expression and perfect structure of complex signals encountered in the application field, achieving accurate tracking and filtering of strong signals, thus making it more suitable for practical system applications. The filtering revolves around "real-time iterative updating of filter weight coefficients - filtering the input observed signal," and fully utilizes the advantages of the complex structure and its unique timing design. Specifically, the RLS adaptive filtering algorithm applies the filter coefficients of the current sample iteration to the complex observed signal input after N cycles, allowing the system filtering and filter coefficient update processes to be completed without being completed in one clock cycle. This effectively reduces the computational pressure within a single cycle, meets the needs of high-speed real-time signal processing scenarios, and avoids signal distortion caused by computational delays, making it very suitable for high-speed real-time signal processing requirements.

[0016] like Figure 1 As shown in the embodiment, an adaptive cancellation method for strong and weak signals based on complex structures is provided. This method is suitable for extracting weak target signals from dual-tone signals under strong interference backgrounds and includes the following steps: S1, obtain the complex observation signal and initialize the parameters of the complex RLS adaptive filtering algorithm.

[0017] In this embodiment, complex observation signals can be directly acquired. To demonstrate the signal cancellation effect of this invention, complex observation signals are constructed to simulate a real complex electromagnetic environment. These complex observation signals include a target weak signal, a strong interference signal, and additive white Gaussian noise. Therefore, two types of interference components are specifically constructed: one is additive white Gaussian noise with an adjustable signal-to-noise ratio, used to simulate measurement noise at the receiver; the other is a reference interference channel signal, which is formed by superimposing strong noise on the target signal, performing spectrum shaping through a filter, and introducing delay and frequency shift processing to create a narrowband strong interference signal that overlaps with the spectrum of the target weak signal but has significantly higher power. Finally, the target weak signal, the strong interference signal combined with preset weights, and the additive white Gaussian noise are synthesized into the complex observation signal at the receiver using a superposition method. This serves as the input for subsequent adaptive filtering and interference cancellation processing.

[0018] Complex RLS filtering and real RLS filtering share a similar basic algorithmic framework, both based on the recursive least squares criterion, achieving adaptive parameter estimation by minimizing the weighted sum of squared errors. However, their essential difference lies in the signal space they define. Real RLS operates in the real domain, processing real-valued signals; while complex RLS operates in the complex domain, processing complex-valued signals. Since complex signals contain both amplitude and phase information, complex RLS can characterize a more complete set of signal properties, while real RLS can only handle amplitude variations and cannot describe phase relationships.

[0019] The initial coefficients of the complex RLS adaptive filter directly affect the convergence speed and steady-state performance of the filtering algorithm. Therefore, the key parameters of the complex RLS adaptive filtering algorithm are initialized, including the filter order, filter coefficient vector, forgetting factor, damping coefficient, and correlation inverse matrix, to lay the foundation for subsequent filtering processing and ensure that the filter can effectively capture the characteristics of strong signals.

[0020] S2 uses a complex RLS adaptive filtering algorithm to track and filter strong interference signals in the complex observation signal to obtain a filtered output signal.

[0021] In the embodiments, such as Figure 2 As shown, when using the complex RLS adaptive filtering algorithm to track and filter complex observation signals, the received complex observation signal is processed, and the filtered output signal is obtained by performing filtering calculations on the complex observation signal according to the current filtering coefficient vector. Specifically, this includes: (a) Calculate the gain vector of the complex observed signal based on the inverse covariance matrix and the forgetting factor. : in, n For periodic indexes, It is the inverse of the covariance matrix. For complex observation signals, The forgetting factor is indicated by the superscript H, which represents the conjugate transpose. ,in, for The identity matrix, This represents the filter order.

[0022] (b) The current complex observation signal is filtered based on the filter coefficient vector updated in the previous N periods to obtain the filtered output signal. : in, This is the transpose of the filter coefficient vector updated over the first N periods.

[0023] (c) Calculate the error based on the filtered output signal and the target signal, and update the filter coefficient vector based on the error and the gain vector using the recursive least squares criterion. : in, For target signal, For error, For error conjugate, and The first n The first cycle and the first A vector of filter coefficients for each period.

[0024] The RLS adaptive filtering algorithm used in this invention is an optimization model established in the complex signal space, and its objective function is the weighted sum of squared errors, i.e. .in, This represents the squared modulus of the complex error. In the complex domain, to ensure the differentiability of the objective function and the rigor of the optimization process, complex gradient theory (i.e., Wirtinger calculus) is required for derivation. Within this theoretical framework, the update direction of the weight vector is determined by the partial derivative of the objective function with respect to the conjugate of the weight vector. Therefore, in the final weight recursive formula, the error term must be in its complex conjugate form. Appear.

[0025] (d) Update the inverse covariance matrix based on the gain vector, forgetting factor, and complex observation signal. : in, and The first n The first cycle and the firstn The inverse covariance matrix with -1 period.

[0026] Steps (a)-(d) above represent the RLS adaptive filtering process for a single sampling point. Repeating this process for all sampling points yields the time-varying results. and .

[0027] S3 subtracts the filtered output signal from the desired signal to achieve adaptive cancellation of strong signals, and extracts weak signal features from the cancelled signal.

[0028] Signal cancellation is key to weak signal separation, and it utilizes the output of RLS adaptive filtering to eliminate strong signals and extract weak signals. In weak signal extraction based on RLS adaptive filtering, the filtered output is first used as an estimate of the strong signal, which is subtracted from the desired signal to achieve adaptive cancellation of the strong signal. The canceled signal is dominated by weak signals, thus improving its purity. Specifically, in each iteration, the receiver treats the filter output as an estimate of the strong interference component, subtracts it from the desired signal to obtain the residual signal, which is the effective output after interference cancellation. Theoretically, this preserves the target signal and background noise while eliminating the main interference. The calculation method is expressed as follows: .

[0029] The desired signal is the target signal approximated by the adaptive filter, while the observed signal is a mixture of strong and weak signals acquired at the receiver. In the interference cancellation structure, the desired signal is chosen as the observed signal, so that the adaptive filter aims to minimize the error between the filtered output and the observed signal. Since the filter's reference input signal is significantly correlated with the strong interference component but approximately uncorrelated with the weak target signal and noise, the RLS algorithm, in the least squares sense, will preferentially approximate the strong interference component in the observed signal. Therefore, the filter output can be considered as an adaptive estimate of the strong interference signal. By subtracting this estimate from the desired signal, the residual signal is obtained. The residual signal is the effective output after interference cancellation, mainly containing the weak target signal and background noise, thus achieving effective extraction of the weak signal.

[0030] By comparing the amplitude and waveform of the target signal before and after cancellation, we can observe whether the amplitude of the signal decreases after cancellation and whether it is mainly noise. Specifically, we can observe that the peak power spectrum of the strong signal is significantly reduced, the power spectrum outline of the weak signal is clearly visible, and the noise is obvious. We can extract the features of the weak signal by removing noise, and then observe the time domain and power spectrum of the extracted weak signal. We can also observe the signal cancellation effect under different SNR and SJR.

[0031] Specifically, in the MATLAB environment, the specific configurations of the signal parameters and simulation parameters are as follows: sampling rate GHz, self-noise interference SJR (-10dB, -30dB), Channel White Gaussian Noise SNR (-5dB, 15dB). The relevant parameters for the RLS algorithm are initialized as follows: Forgetting factor. Damping parameters The adaptive filter order N=8. Simultaneously, the correlation matrix is ​​initialized to store the signal and filter coefficients, specifically... Store the desired target signal. Store the filtered output signal. Store error signals. Changes in storage filter coefficients.

[0032] Under the above initialization parameters, simulation was performed using steps 2 and 3 above, and the simulation results are as follows. Figures 3-7 As shown, from the time-domain waveform of the signal, the complex observed signal before cancellation... In the waveform, strong signal energy dominates, and weak signal characteristics are completely masked, resulting in obvious strong interference fluctuations; while the residual signal after cancellation... The amplitude decreased significantly, the fluctuation characteristics of the strong signal essentially disappeared, and the waveform was dominated by stable background noise. Weak time-domain characteristics of the weak signal could also be observed, proving the significant effect of strong signal cancellation. At the power spectrum analysis level, before cancellation, the power spectrum peak of the strong signal was extremely high, completely covering the power spectrum region of the weak signal, making the weak signal's spectrum unidentifiable. After cancellation, the power spectrum peak corresponding to the strong signal decreased significantly, and the power spectrum outline of the weak signal became clearly visible. Its characteristic frequency perfectly matched the preset target weak signal frequency, achieving effective extraction of the weak signal's spectrum.

[0033] Based on the simulation parameters and experimental steps above, the final cancellation performance table is obtained, as shown in Table 1. The channel noise is above 0dB, and the strong signal interference cancellation can reach up to 23dB. Table 1 like Figure 8As shown, the embodiment also provides a strong and weak signal adaptive cancellation system 80 based on a complex structure, including a filtering module 81 and a weak signal extraction module 82. The filtering module 81 is used to track and filter strong interference signals in the complex observation signal using a complex RLS adaptive filtering algorithm to obtain a filtered output signal. During filtering, the new filtering coefficient vector obtained by iterating the current complex observation signal is delayed and applied to the input complex observation signal after N periods to decouple the filtering coefficient update from the signal filtering in time, so that the filtering and filtering coefficient update processes are executed asynchronously. The weak signal extraction module 82 is used to subtract the filtered output signal from the desired signal to achieve adaptive cancellation of strong signals, and to perform spectral analysis on the canceled signal to extract weak signal features.

[0034] It should be noted that the adaptive cancellation system for strong and weak signals based on complex structures provided in the above embodiments should be illustrated using the above-described division of functional modules. The functions can be assigned to different functional modules as needed, i.e., the internal structure of the terminal or server can be divided into different functional modules to complete all or part of the functions described above. Furthermore, the adaptive cancellation system for strong and weak signals based on complex structures provided in the above embodiments and the method embodiments for adaptive cancellation of strong and weak signals based on complex structures belong to the same concept. For details of their implementation, please refer to the method embodiments for adaptive cancellation of strong and weak signals based on complex structures, which will not be repeated here.

[0035] Based on the same inventive concept, the embodiment also provides a computing device, including a memory and one or more processors. The memory stores executable code, and when the one or more processors execute the executable code, it is used to implement the above-mentioned adaptive cancellation method for strong and weak signals based on complex number structures, specifically including the following steps: S1, obtain the complex observation signal and initialize the parameters of the complex RLS adaptive filtering algorithm; S2, The complex RLS adaptive filtering algorithm is used to track and filter strong interference signals in the complex observation signal to obtain the filtered output signal; S3 subtracts the filtered output signal from the desired signal to achieve adaptive cancellation of strong signals, and extracts weak signal features from the cancelled signal.

[0036] The computing device provided in this embodiment, at the hardware level, includes not only a processor and memory, but also internal buses, network interfaces, memory, and other hardware required for business operations. The memory is non-volatile memory. The processor reads the corresponding computer program from the non-volatile memory into memory and then runs it to implement the adaptive cancellation method for strong and weak signals based on complex structures described in S1-S3 above. Of course, besides software implementation, this invention does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution entity of the following processing flow is not limited to individual logic units, but can also be hardware or logic devices.

[0037] Based on the same inventive concept, the embodiments also provide a computer-readable storage medium storing a program thereon, which, when executed by a processor, implements the above-described adaptive cancellation method for strong and weak signals based on complex structures, specifically including the following steps: S1, obtain the complex observation signal and initialize the parameters of the complex RLS adaptive filtering algorithm; S2, The complex RLS adaptive filtering algorithm is used to track and filter strong interference signals in the complex observation signal to obtain the filtered output signal; S3 subtracts the filtered output signal from the desired signal to achieve adaptive cancellation of strong signals, and extracts weak signal features from the cancelled signal.

[0038] In this embodiment, the computer-readable medium includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data.

[0039] The specific embodiments described above illustrate the technical solution and beneficial effects of the present invention in detail. It should be understood that the above description is only the most preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, additions, and equivalent substitutions made within the scope of the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for adaptive cancellation of strong and weak signals based on complex structures, characterized in that, Includes the following steps: The complex RLS adaptive filtering algorithm is used to track and filter strong interference signals in the complex observation signal to obtain the filtered output signal. During filtering, the new filter coefficient vector obtained by iterating the current complex observation signal is applied to the input complex observation signal after N periods with a delay, so as to decouple the filter coefficient update from the signal filtering in time, so that the filtering and filter coefficient update processes are executed asynchronously. The strong signal is adaptively canceled by subtracting the filtered output signal from the desired signal, and the weak signal features are extracted from the canceled signal.

2. The adaptive cancellation method for strong and weak signals based on complex structures according to claim 1, characterized in that, A complex RLS adaptive filtering algorithm is used to track and filter strong interference signals in complex observation signals to obtain a filtered output signal, including: Calculate the gain vector of complex observed signals based on the inverse covariance matrix and forgetting factor; The current complex observation signal is filtered based on the filter coefficient vector updated in the previous N periods to obtain the filtered output signal; The error is calculated based on the filtered output signal and the desired signal, and the filter coefficient vector is updated based on the error and the gain vector. The covariance inverse matrix is ​​updated based on the gain vector, forgetting factor, and complex observation signals.

3. The adaptive cancellation method for strong and weak signals based on complex structures according to claim 2, characterized in that, The gain vector of the complex observed signal is calculated based on the inverse covariance matrix and the forgetting factor, including: in, n For periodic indexes, For the gain vector, It is the inverse of the covariance matrix. For complex observation signals, The forgetting factor is represented by the superscript H, which indicates the conjugate transpose.

4. The adaptive cancellation method for strong and weak signals based on complex structures according to claim 2, characterized in that, The filtered output signal is obtained by filtering the current complex observation signal based on the filter coefficient vector updated in the previous N periods, including: in, n For periodic indexes, For filtering the output signal, For complex observation signals, This is the transpose of the filter coefficient vector updated over the first N periods.

5. The adaptive cancellation method for strong and weak signals based on complex structures according to claim 2, characterized in that, The error is calculated based on the filtered output signal and the desired signal, and the filter coefficient vector is updated based on the error and the gain vector, including: in, n For periodic indexes, For filtering the output signal, For the desired signal, For error, For error conjugate, For the gain vector, and The first n The first cycle and the first A vector of filter coefficients for each period.

6. The adaptive cancellation method for strong and weak signals based on complex structures according to claim 2, characterized in that, The covariance inverse matrix is ​​updated based on the gain vector, forgetting factor, and complex observation signals, including: in, n For periodic indexes, For the gain vector, Forgetting factor, For complex observation signals, the superscript H indicates the conjugate transpose. and The first n The first cycle and the first n The inverse covariance matrix with -1 period.

7. The adaptive cancellation method for strong and weak signals based on complex structures according to claim 1, characterized in that, Extracting weak signal features from the canceled signal includes: The canceled signal is mainly composed of weak and noisy signals. We extract the weak signal features through spectrum analysis.

8. A strong and weak signal adaptive cancellation system based on complex structure, characterized in that, include: The filtering module is used to track and filter strong interference signals in complex observation signals using a complex RLS adaptive filtering algorithm to obtain a filtered output signal. During filtering, the new filter coefficient vector obtained by iterating the current complex observation signal is applied to the input complex observation signal after N cycles with a delay, so as to decouple the filter coefficient update from the signal filtering in time, so that the filtering and filter coefficient update processes are executed asynchronously. The weak signal extraction module is used to subtract the filtered output signal from the desired signal to achieve adaptive cancellation of strong signals, and to perform spectral analysis on the canceled signal to extract weak signal features.

9. A computing device comprising a memory and one or more processors, wherein the memory stores executable code, characterized in that, When the one or more processors execute the executable code, they are used to implement the adaptive cancellation method for strong and weak signals based on complex structures as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores a program that, when executed by a processor, implements the adaptive cancellation method for strong and weak signals based on complex structures as described in any one of claims 1-7.

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