A time-frequency domain signal reconstruction method and system for countering high-power interference signals

Abstract

The application relates to the technical field of signal processing, in particular to a time-frequency domain signal reconstruction method and system for resisting high-power interference signals. The method is characterized in that: a radar echo signal is acquired, time-frequency transformation and threshold judgment are performed on the radar echo signal, time-frequency components corresponding to high-power interference are accurately identified and suppressed, and a time-frequency representation containing a vacancy area is formed; a reference time-frequency model of an original transmitted signal is constructed based on the vacancy area, and candidate completion components are obtained by sliding matching along a time dimension; a main-lobe side-lobe ratio is calculated in combination with a matched filter output, effective completion positions and amplitude parameters are determined according to a judgment criterion, time-frequency vacancy area adaptive reconstruction is realized, a high-quality target signal is finally output through inverse time-frequency transformation and matched filtering, effective suppression of high-power interference and accurate signal reconstruction are realized. The application realizes accurate suppression of high-power interference and high-fidelity recovery of a target signal, and significantly improves the detection reliability and signal-to-noise ratio of a radar system in a strong interference environment.

Description

Technical Field

[0001] This application relates to the field of signal processing technology, and in particular to a time-frequency domain signal reconstruction method and system for combating high-power interference signals. Background Technology

[0002] In related technologies, with the widespread application of radar systems in target detection, countermeasure reconnaissance, and complex electromagnetic environment perception, anti-jamming processing of radar echo signals has become an important technical means to ensure radar detection performance and reliability. Radar systems typically transmit frequency-modulated signals and receive target reflections to form echo signals. These echo signals are then processed through pulse compression, target detection, and parameter estimation to achieve target location and identification. However, in practical applications, radar echo signals are often affected by various high-power interference signals, especially coherent interference forms such as intermittent sampling and forwarding interference, frequency hopping interference, and spectral dispersion interference. These interferences can easily form anomalous components with significant amplitude and complex structures in the echo signals, thereby interfering with or even obscuring the true target signal.

[0003] However, existing radar anti-high-power jamming methods still face significant technical limitations in complex jamming scenarios. On the one hand, anti-jamming methods based on transmitted waveform or received filter optimization usually rely on prior information about the jamming signal, limiting their applicability and easily introducing high sidelobes under high-power jamming conditions, affecting target detection performance. On the other hand, while methods based on time-frequency domain jamming identification or piecewise pulse compression can utilize the prominent energy of the jamming signal in the time-frequency domain to achieve jamming suppression, when the jamming signal partially or completely overlaps with the real target signal in the time-frequency domain, direct jamming suppression often simultaneously removes the effective time-frequency components of the real target signal, leading to discontinuities and energy loss in the target signal. This, in turn, causes distortion of the matched filtering results, increased sidelobes, or a significant reduction in the target peak value, severely affecting the accuracy and stability of radar detection.

[0004] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0005] The main objective of this application is to propose a time-frequency domain signal reconstruction method and system for countering high-power interference signals, so as to achieve accurate suppression of high-power interference and high-fidelity recovery of target signals, and significantly improve the detection reliability and signal-to-noise ratio of radar systems in strong interference environments.

[0006] To achieve the above objectives, one aspect of this application proposes a time-frequency domain signal reconstruction method for combating high-power interference signals, the method comprising the following steps: Acquire radar echo signals; The radar echo signal is subjected to time-frequency transformation, and the time-frequency component corresponding to high-power interference is identified by a threshold decision method. The time-frequency components are suppressed to form a time-frequency representation that includes the missing regions; Based on the missing regions in the time-frequency representation, a reference time-frequency model of the original transmitted signal is constructed; Sliding matching is performed along the time dimension on the reference time-frequency model to determine candidate completion time-frequency components in the missing region; Based on the candidate completion time-frequency components, the main lobe-side lobe ratio is calculated based on the matched filter output, and the effective completion position and corresponding amplitude parameters are determined according to the preset decision criteria. Based on the determined effective completion position and amplitude parameters, the time-frequency missing region is reconstructed, and the reconstructed time-frequency domain representation is subjected to inverse time-frequency transformation and matched filtering to output the target signal after suppressing high-power interference.

[0007] In some embodiments, the radar echo signal includes the echo signal of the real target and the high-power jamming signal.

[0008] In some embodiments, the step of performing time-frequency transformation on the radar echo signal and using a threshold decision method to identify the time-frequency components corresponding to high-power interference includes: The radar echo signal is subjected to a short-time Fourier transform to obtain the time-frequency amplitude matrix of the radar echo signal. An amplitude statistical distribution is constructed based on the time-frequency amplitude matrix, and a high-power decision threshold is determined according to the maximum inter-class variance method. Based on the high power decision threshold, the time-frequency amplitude matrix is ​​divided into thresholds, and time-frequency units exceeding the high power decision threshold are identified as time-frequency components corresponding to high power interference.

[0009] In some embodiments, the calculation formula for the maximum inter-class variance method is as follows: ; in, This represents the pixel value corresponding to any pixel in the time-frequency domain image. Indicates the decision threshold; This indicates that the pixel value is greater than or equal to the decision threshold. The set of pixels corresponds to the high-power foreground pixels in the time-frequency domain; This indicates that the pixel value is less than the decision threshold. The set of pixels corresponds to the background pixels in the time-frequency domain; Represents the set of foreground pixels The number of pixels contained in it; Represents the set of background pixels The number of pixels contained in the image; m represents the number of rows in the time-frequency image; n represents the number of columns in the time-frequency image; and These represent the proportion and weight of foreground and background pixels in the entire time-frequency domain image, respectively. and Representing the foreground pixel set and background pixel set The sum of all pixel values ​​in the array; and These represent the average pixel values ​​of the foreground pixel set and the background pixel set, respectively. The inter-class variance function is a measure of the difference between foreground and background. This indicates that when traversing all possible thresholds, the inter-class variance is minimized. The threshold index for obtaining the maximum value; This represents the optimal threshold determined based on the maximum inter-class variance criterion.

[0010] In some embodiments, the step of performing sliding matching along the time dimension on the reference time-frequency model to determine candidate completion time-frequency components in the missing region includes: The reference time-frequency model is slid-aligned along the time dimension, and the overlapping time-frequency components of the reference time-frequency model and the missing region are extracted at each sliding position. Perform matching response calculation on the overlapping time-frequency components to obtain the matching response value at the corresponding sliding position; A sliding matching response sequence is constructed based on the matching response value, and time-frequency components that meet the preset criteria are selected from the sliding matching response sequence as candidate time-frequency components for filling in the missing region.

[0011] In some embodiments, the step of calculating the main lobe-side lobe ratio based on the matched filter output according to the candidate completion time-frequency components, and determining the effective completion position and corresponding amplitude parameters according to a preset decision criterion, includes: The candidate completed time-frequency components are filled into the missing regions of the time-frequency representation to construct the corresponding completed time-frequency representation; Perform matched filtering on the completed time-frequency representation and calculate the corresponding main lobe-side lobe ratio; Based on the comparison result between the main lobe-side lobe ratio and the preset decision threshold, the completion position that meets the decision condition is determined as the effective completion position; Based on the peak value of the matched filter main lobe at the effective completion position, and combined with the linear relationship between the proportion of time-frequency components and the peak value of the matched filter, the corresponding amplitude parameter is determined.

[0012] In some embodiments, the formula for calculating the preset decision threshold is as follows:

[0013] in, Indicates the decision threshold parameter; Indicates the threshold scaling factor; Indicates the first The amplitude statistics corresponding to each time-frequency unit; i represents the time-frequency index variable; This indicates the index position corresponding to the maximum target amplitude. This represents the half-width parameter of the sliding window.

[0014] In some embodiments, the formula for calculating the amplitude parameter is as follows: ; in, This represents the complex amplitude of the reconstructed signal obtained after reconstructing the signal in the missing time-frequency domain. Indicates the normalized complex amplitude. Indicates phase; This represents the peak amplitude of the matched filter obtained in the pulse compression domain after filling in and reconstructing the missing time-frequency domain signal; This represents the peak value of the matched filter corresponding to the missing time-frequency domain; This represents the area of ​​the time-frequency component of the target signal in the reconstructed time-frequency domain; This represents the area of ​​the target signal time-frequency component that has not yet been disturbed and set to zero in the empty time-frequency domain; The area of ​​the complete time-frequency domain signal corresponding to the complete real target signal is represented by N; N represents the number of sampling points.

[0015] In some embodiments, the step of reconstructing the time-frequency gap region based on the determined effective completion position and amplitude parameters, performing inverse time-frequency transform and matched filtering on the reconstructed time-frequency domain representation, and outputting the target signal after suppressing high-power interference includes: Based on the effective completion position and the amplitude parameter, the amplitude of the corresponding candidate completion time-frequency component is assigned and filled into the missing area of ​​the time-frequency representation to form the reconstructed time-frequency domain representation; Perform an inverse short-time Fourier transform on the reconstructed time-frequency domain representation to obtain the corresponding reconstructed time-domain echo signal; The reconstructed time-domain echo signal is subjected to matched filtering to output the target signal after suppressing high-power interference.

[0016] To achieve the above objectives, another aspect of this application proposes a time-frequency domain signal reconstruction system for combating high-power interference signals, the system comprising: The acquisition module is used to acquire radar echo signals; An interference identification module is used to perform time-frequency transformation on the radar echo signal and use a threshold decision method to identify the time-frequency components corresponding to high-power interference. An interference suppression module is used to suppress the time-frequency components to form a time-frequency representation that includes the missing region; The time-frequency model construction module is used to construct a reference time-frequency model of the original transmitted signal based on the missing regions in the time-frequency representation; The candidate completion determination module is used to perform sliding matching along the time dimension on the reference time-frequency model to determine the candidate completion time-frequency components in the missing region; The decision parameter determination module is used to calculate the main lobe-side lobe ratio based on the matched filter output according to the candidate completion time-frequency components, and determine the effective completion position and corresponding amplitude parameters according to the preset decision criteria. The signal reconstruction output module is used to reconstruct the time-frequency gap region based on the determined effective completion position and amplitude parameters, and to perform inverse time-frequency transformation and matched filtering on the reconstructed time-frequency domain representation to output the target signal after suppressing high-power interference.

[0017] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0018] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0019] To achieve the above objectives, another aspect of this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0020] The embodiments of this application include at least the following beneficial effects: This application provides a time-frequency domain signal reconstruction method and system for combating high-power interference signals. This scheme achieves accurate identification and location of high-power interference time-frequency components by acquiring radar echo signals and combining time-frequency transformation with threshold decision. Based on this, targeted suppression processing is performed on the interference components, avoiding the large-area information loss caused by traditional overall filtering or frequency band shielding methods to the effective target signal, and reducing the degree of damage to the signal structure caused by interference from the source. Furthermore, by constructing a reference time-frequency model of the original transmitted signal and performing sliding matching along the time dimension, the structured completion of the missing region and the candidate time-frequency components are realized. Constraint search transforms the reconstruction process from "blind completion" to "model-guided completion," significantly improving the physical consistency and signal continuity of time-frequency reconstruction. Simultaneously, by combining matched filter output to calculate the main lobe-side lobe ratio and introducing decision criteria to screen effective completion positions and amplitude parameters, the problem of spurious completion and noise amplification is effectively suppressed, avoiding the introduction of false energy that interferes with target detection and parameter estimation. Finally, through time-frequency gap region reconstruction, inverse time-frequency transform, and matched filtering, high-power interference is efficiently suppressed and target signal is recovered with high fidelity. This significantly improves the radar system's target detection reliability, signal-to-noise ratio, time-frequency structure integrity, and the stability and accuracy of subsequent signal processing algorithms under strong interference environments. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating a time-frequency domain signal reconstruction method for combating high-power interference signals provided in an embodiment of this application. Figure 2 This is a schematic diagram of the time-domain waveform of the radar echo signal provided in an embodiment of this application; Figure 3 This is a schematic diagram of the matched filter output result of the radar echo signal provided in the embodiments of this application after interference suppression but without time-frequency domain reconstruction; Figure 4 This is a schematic diagram illustrating the time-frequency domain signal reconstruction process and its effect in the context of frequency hopping interference, as provided in the embodiments of this application. Figure 5 This is a schematic diagram illustrating the time-frequency domain signal reconstruction process and effect under the spectrum dispersion interference scenario provided in the embodiments of this application; Figure 6 This is a schematic diagram of a time-frequency domain signal reconstruction system for combating high-power interference signals, provided in an embodiment of this application. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0023] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0024] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0026] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained first. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.

[0027] Radar echo signal refers to the electromagnetic wave signal emitted by a radar system that is reflected back to the receiver after being reflected by the target. It usually includes the echo signal of the real target, environmental noise, and possibly superimposed high-power interference signals.

[0028] High-power interference signals refer to interference signals emitted by interference sources with power significantly higher than the echo of the real target. They can form abnormally high amplitude components in radar echo signals, including but not limited to intermittent sampling and forwarding interference, frequency hopping interference, frequency sweeping interference, and spectrum dispersion interference.

[0029] The Maximum Between-Class Variance Method (Otsu Method) is a statistical decision method that determines the optimal threshold by maximizing the between-class variance between the foreground and the background. In this embodiment, it is used to adaptively determine the high-power interference decision threshold from the time-frequency amplitude matrix.

[0030] A missing region (or time-frequency hole) refers to an energy-deficient region in the time-frequency domain after suppressing or zeroing the time-frequency components corresponding to high-power interference. This region may also contain the real target signal components that have been masked by the interference.

[0031] The main lobe-to-side lobe ratio (MSR) is the ratio between the peak value of the main lobe and the maximum value of the side lobe in the output of the matched filter. It is used to measure the target detection quality and the reliability of the reconstruction results. In the embodiments of this application, it is used as a key evaluation index for determining the effectiveness of the completion.

[0032] Combating frequency hopping interference refers to the process of identifying the high-power time-frequency components corresponding to the interference signal through time-frequency domain analysis, and then suppressing and reconstructing them to reduce the impact of frequency hopping interference on the continuity of the real target signal and the detection performance, taking advantage of the characteristic that the interference signal rapidly jumps between different frequency points in the time dimension.

[0033] Spectral dispersion interference refers to the effective recovery of the real target signal by constructing a reference time-frequency model and completing and reconstructing the interfered area, taking advantage of the characteristics of interference signal energy spreading and distributing over a wide frequency band and forming a continuous coverage area in the time-frequency domain.

[0034] This application provides a time-frequency domain signal reconstruction method and system for combating high-power interference signals. This scheme acquires radar echo signals and combines time-frequency transformation with threshold decision to accurately identify and locate the time-frequency components of high-power interference. Based on this, targeted suppression of the interference components is performed, avoiding the large-area information loss caused by traditional overall filtering or frequency band shielding methods on the effective target signal, thus reducing the degree of interference damage to the signal structure at the source. Furthermore, by constructing a reference time-frequency model of the original transmitted signal and performing sliding matching along the time dimension, structured completion of missing regions and constraint search of candidate time-frequency components are achieved, enabling... The reconstruction process has been transformed from "blind completion" to "model-guided completion," significantly improving the physical consistency and signal continuity of time-frequency reconstruction. Simultaneously, by combining the matched filter output to calculate the main lobe-side lobe ratio and introducing decision criteria to screen effective completion positions and amplitude parameters, the problems of spurious completion and noise amplification are effectively suppressed, avoiding the introduction of false energy that interferes with target detection and parameter estimation. Finally, through time-frequency gap region reconstruction, inverse time-frequency transform, and matched filtering, high-power interference is efficiently suppressed and target signal is recovered with high fidelity. This significantly improves the radar system's target detection reliability, signal-to-noise ratio, time-frequency structure integrity, and the stability and accuracy of subsequent signal processing algorithms under strong interference environments.

[0035] This application provides a time-frequency domain signal reconstruction method for combating high-power interference signals, relating to the field of signal processing technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or in-vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing a time-frequency domain signal reconstruction method for combating high-power interference signals, but is not limited to the above forms.

[0036] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0037] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.

[0038] Figure 1 This is an optional flowchart of a time-frequency domain signal reconstruction method for combating high-power interference signals provided in an embodiment of this application. Figure 1 The method may include, but is not limited to, steps S1 to S7: S1: Acquire radar echo signals; where the radar echo signals include real target echo signals and high-power interference signals.

[0039] In this embodiment, the radar periodically transmits a linear frequency modulated signal to the target area according to a preset pulse repetition period, and acquires the corresponding echo signal through the receiving antenna within each pulse repetition period. This echo signal is a composite signal formed by the transmitted signal propagating through space, being reflected back by the target, and superimposed with environmental noise and external interference signals. The radar receiver performs down-conversion, filtering, and analog-to-digital conversion on the echo signal to obtain a discrete digital echo signal sequence, which serves as input data for subsequent signal processing.

[0040] In practical applications, radar echo signals not only include the echo signals of real targets reflected from them, but may also contain high-power jamming signals emitted by jammers. These high-power jamming signals can take the form of intermittent sampling-forward jamming, frequency hopping jamming, frequency sweeping jamming, or spectral dispersion jamming. Their amplitude is typically significantly higher than that of the real target echo signals, and they may precede, delay, or completely cover the real target echo signals in time and location, thus creating false targets or obscuring the real targets within the echo signals.

[0041] Since radar cannot directly distinguish between real target echoes and high-power jamming signals during the reception phase, this step does not perform target-level discrimination processing on the echo signals. Instead, it fully preserves the time-domain information of the radar echo signals, providing the original data foundation for subsequent jamming identification and suppression based on time-frequency characteristics. This approach avoids introducing prior assumptions during signal acquisition, thereby improving the method's adaptability to different jamming types and complex scenarios.

[0042] refer to Figure 2 As shown, Figure 2 The horizontal axis represents the time delay in microseconds, and the vertical axis represents the amplitude of the echo signal. Figure 2 It reflects the time-domain distribution of the echo signal received by the radar within a single pulse repetition period. Figure 2 The radar echo signal is formed by superimposing the real target echo signal and the high-power interference signal. The background part consists of the real target echo signal and noise components, which have relatively low amplitude and relatively stable distribution.

[0043] As can be seen from the figure, signal components with significantly increased amplitude appear at multiple time points. The amplitude of these signal components is significantly higher than that of the background echo signal, exhibiting several pulse structures with abrupt amplitude increases. These signal components with abrupt amplitude increases correspond to high-power interference signals, especially the time-domain manifestation of intermittent sampling and forwarding interference, which is characterized by high power, short duration, and unpredictable location.

[0044] When high-power jamming signals partially or completely overlap with the echo signals of the real target in time, it is difficult to accurately distinguish between the real target echo and the jamming signal based solely on time-domain amplitude characteristics. This figure visually illustrates the masking effect of high-power jamming signals on radar echo signals, demonstrating the necessity of time-frequency domain analysis of radar echo signals. Furthermore, by employing time-frequency domain jamming identification and signal reconstruction methods, effective suppression of high-power jamming signals and recovery of the real target signal can be achieved.

[0045] S2: Perform time-frequency transformation on the radar echo signal and use the threshold decision method to identify the time-frequency components corresponding to high-power interference; This includes performing time-frequency transformation on the radar echo signal and using a threshold decision method to identify the time-frequency components corresponding to high-power interference, including: The radar echo signal is subjected to a short-time Fourier transform to obtain the time-frequency amplitude matrix of the radar echo signal. Amplitude statistical distribution is constructed based on the time-frequency amplitude matrix, and the high-power decision threshold is determined according to the maximum inter-class variance method. Based on the high-power decision threshold, the time-frequency amplitude matrix is ​​divided into thresholds, and the time-frequency units that exceed the high-power decision threshold are identified as the time-frequency components corresponding to high-power interference.

[0046] In this embodiment, the acquired radar echo signal undergoes time-frequency transformation processing to map the one-dimensional time-domain signal to the two-dimensional time-frequency domain. Specifically, the radar echo signal is segmented into frames using the short-time Fourier transform method, and spectral analysis is performed on each time frame. The spectral results of each time frame are then concatenated in chronological order to obtain the time-frequency amplitude matrix of the radar echo signal. The time-frequency amplitude matrix reflects the energy distribution characteristics of the echo signal at different times and frequency locations. High-power interference signals typically exhibit regions in the time-frequency domain with amplitudes significantly higher than the actual target echo and noise background.

[0047] After obtaining the time-frequency amplitude matrix, this embodiment constructs an overall amplitude statistical distribution based on the matrix to distinguish high-power interference components from background components. Considering the significant energy advantage of high-power interference, this embodiment employs the Otsu's method (maximum inter-class variance method) to analyze the amplitude statistical distribution and adaptively determine a high-power decision threshold, maximizing the difference between time-frequency units exceeding this threshold and the remaining time-frequency units. This approach avoids the problem of insufficient adaptability of fixed thresholds under different interference-to-signal ratios and noise conditions, improving the stability and robustness of interference identification.

[0048] Subsequently, the time-frequency amplitude matrix is ​​divided into individual time-frequency components based on a high-power decision threshold. Time-frequency components with amplitude values ​​exceeding the high-power decision threshold are identified as high-power interference components, while the remaining time-frequency components are identified as non-interference components. Through this decision process, interference identification results corresponding to the time-frequency amplitude matrix are generated, providing accurate location data for subsequent suppression of high-power interference components and the construction of time-frequency gap regions.

[0049] Specifically, the calculation formula for the Otsu's inter-class variance method is as follows: ; in, This represents the pixel value corresponding to any pixel in the time-frequency domain image. Indicates the decision threshold; This indicates that the pixel value is greater than or equal to the decision threshold. The set of pixels corresponds to the high-power foreground pixels in the time-frequency domain; This indicates that the pixel value is less than the decision threshold. The set of pixels corresponds to the background pixels in the time-frequency domain; Represents the set of foreground pixels The number of pixels contained in it; Represents the set of background pixels The number of pixels contained in the image; m represents the number of rows in the time-frequency image; n represents the number of columns in the time-frequency image; and These represent the proportion and weight of foreground and background pixels in the entire time-frequency domain image, respectively. and Representing the foreground pixel set and background pixel set The sum of all pixel values ​​in the array; and These represent the average pixel values ​​of the foreground pixel set and the background pixel set, respectively. The inter-class variance function is a measure of the difference between foreground and background. This indicates that when traversing all possible thresholds, the inter-class variance is minimized. The threshold index for obtaining the maximum value; This represents the optimal threshold determined based on the maximum inter-class variance criterion.

[0050] S3: Suppress the time-frequency components to form a time-frequency representation that includes the missing regions; In this embodiment, suppression processing is performed on the time-frequency components based on the identification results of the time-frequency components corresponding to high-power interference. Specifically, in the time-frequency domain representation of the radar echo signal, the amplitude of the time-frequency components identified as high-power interference is directly set to zero, while the amplitude of time-frequency components not identified as interference remains unchanged. Through this processing method, high-power interference signals can be directly suppressed in the time-frequency domain, thereby avoiding the generation of strong sidelobes or false targets in subsequent signal processing.

[0051] Because high-power interference signals may partially or completely overlap with the real target signal in the time-frequency domain, especially when the time delays of the interference signal and the real target signal are similar or the same, the suppression process may also simultaneously suppress some of the time-frequency components of the real target signal when eliminating interference. This phenomenon will cause the real target signal to exhibit a discontinuous, fragmented, or locally missing distribution pattern in the time-frequency domain, thus making the processed time-frequency domain incomplete.

[0052] In the time-frequency domain representation obtained after the above suppression processing, the regions set to zero constitute the time-frequency domain void regions. These void regions can be multiple discretely distributed regions in space, or they can be regions continuously distributed along the time or frequency dimensions; their specific shape is related to the time-frequency structure of the interference signal. These void regions directly reflect the locations of the time-frequency components lost due to suppression of the actual target signal, and are crucial for subsequent construction of the reference time-frequency model, execution of sliding matching, and reconstruction of the time-frequency domain signal.

[0053] refer to Figure 3 As shown, Figure 3 In the diagram, the horizontal axis represents distance in meters, and the vertical axis represents the output amplitude of the matched filter. Figure 3 This reflects the pulse compression result obtained by performing inverse time-frequency transformation and matched filtering on the radar echo signal after performing time-frequency domain interference identification and suppression processing. As can be seen from the figure, a distinct main lobe structure exists near the target location, but its amplitude is significantly lower than the peak value under ideal matched filtering conditions.

[0054] Furthermore, Figure 3 Multiple high-amplitude sidelobe components appear on both sides of the main lobe, and the overall level of the sidelobes is too high, exhibiting obvious envelope distortion characteristics. This phenomenon stems from the fact that when high-power interference signals are directly suppressed in the time-frequency domain, some time-frequency components of the real target signal are also suppressed simultaneously, resulting in an incomplete spectrum of the target signal, which in turn causes energy loss and increased sidelobes during matched filtering.

[0055] Figure 3 Intuitively, suppressing high-power interference components solely through time-frequency domain thresholding can eliminate false targets caused by interference, but it also reduces the matched-filter peak value and deteriorates the sidelobe structure of the real target signal, affecting target detection performance. Therefore, it is necessary to perform time-frequency domain signal reconstruction processing on the missing regions formed in the time-frequency domain after interference suppression to restore the complete time-frequency structure of the real target signal, improve the main lobe-to-sidelobe ratio, and enhance the pulse compression output quality.

[0056] S4: Based on the missing regions in the time-frequency representation, construct a reference time-frequency model for the original transmitted signal; In this embodiment, after obtaining the missing regions in the time-frequency representation, to ensure that the time-frequency components of the real target signal can be "filled in by shape" within the missing regions, a reference time-frequency model of the original transmitted signal is first constructed. Specifically, based on the radar's known transmitted waveform parameters (e.g., the time width, bandwidth / modulation slope of the linear frequency modulated signal), a reference transmitted signal consistent with the actual transmitted signal is generated offline or online. Time-frequency transformation processing (e.g., short-time Fourier transform) is then performed on this reference transmitted signal to obtain the two-dimensional distribution matrix of the original transmitted signal in the time-frequency domain.

[0057] Furthermore, to enable the reference time-frequency model to be directly used for geometric matching of "candidate completion components," this embodiment extracts signal components from the two-dimensional distribution matrix to form an effective support region for the reference time-frequency model. Specifically, the time-frequency domain amplitude matrix of the original transmitted signal is subjected to thresholding / binarization to obtain a reference binary mask. Regions with a mask value of 1 represent the main time-frequency energy components of the original transmitted signal, while regions with a mask value of 0 represent the background. This reference binary mask is used to characterize the "shape template" of the original transmitted signal in the time-frequency domain, thereby enabling the rapid acquisition of intersection components with the missing regions during subsequent sliding.

[0058] Furthermore, to improve the adaptability of the reference time-frequency model to real-world scenarios, this embodiment can perform consistency and scale alignment processing on the reference binary mask, including: ensuring that the time sampling interval and frequency resolution of the reference time-frequency model are consistent with the echo time-frequency representation in steps S2 / S3; normalizing the amplitude of the reference model so that it only expresses the structural morphology without presetting a target amplitude; and, if necessary, performing appropriate morphological dilation / erosion processing on the reference mask to cover edge expansion effects caused by window functions, spectral leakage, etc. Through the above processing, a reference time-frequency model for subsequent sliding matching along the time dimension can be obtained, providing a reliable prior template for determining candidate completion time-frequency components in the missing region.

[0059] S5: Perform sliding matching along the time dimension on the reference time-frequency model to determine candidate completion time-frequency components in the missing region; Among them, sliding matching is performed along the time dimension on the reference time-frequency model to determine candidate completion time-frequency components in the missing region, including: The reference time-frequency model is slid-aligned along the time dimension, and the overlapping time-frequency components between the reference time-frequency model and the missing region are extracted at each sliding position. Perform matching response calculation on the overlapping time-frequency components to obtain the matching response value at the corresponding sliding position; A sliding matching response sequence is constructed based on the matching response value, and time-frequency components that meet the preset criteria are selected from the sliding matching response sequence as candidate time-frequency components for filling in the missing region.

[0060] In this embodiment, after obtaining the reference time-frequency model of the original transmitted signal and the time-frequency representation containing the missing region, the reference time-frequency model is used as a "structural template" and sliding matching is performed along the time dimension of the time-frequency domain. Specifically, the reference time-frequency model is successively shifted forward / backward by a preset sliding step size, so that it is in a different time alignment relationship with the missing time-frequency representation at each sliding position. At each sliding position, the spatial overlap relationship between the effective components of the reference time-frequency model and the missing region is calculated, and the overlapping part is extracted as the "overlapping time-frequency component" at that sliding position. The overlapping time-frequency component can be understood as follows: when the reference time-frequency model slides to a certain time position, if its effective energy distribution covers the vicinity of the missing region, then the part covered by the missing region is the set of time-frequency components that may need to be filled at that position.

[0061] After extracting the overlapping time-frequency components, this embodiment performs matching response calculations on the overlapping time-frequency components to quantify the "degree of matching" between the current sliding position and the empty region. Specifically, the overlapping time-frequency components can be mapped to corresponding time-frequency masks or weight matrices, and participate in correlation / consistency measurements together with the non-empty background components in the empty time-frequency representation. For example, the effective energy continuity within the overlapping region, the edge connectivity between the overlapping region and the retained target components, and the coverage consistency of the overlapping region within the frequency band are statistically analyzed to obtain the matching response value corresponding to the sliding position. This matching response value reflects whether the reference time-frequency model is more likely to correspond to the location of the real target signal under the current time alignment, thus providing a basis for subsequent screening.

[0062] Subsequently, this embodiment constructs a sliding matching response sequence based on the matching response values ​​corresponding to each sliding position, and performs candidate filtering on this sequence. Specifically, the sliding position index and the matching response value are recorded in time sliding order to form a sequence, and then a preset criterion is used to filter out sliding positions with significant responses, such as selecting sliding positions with response values ​​higher than a threshold, or selecting sliding positions with local peaks and satisfying the minimum interval constraint, to avoid adjacent positions being selected repeatedly. For the filtered sliding positions, their corresponding overlapping time-frequency components are taken as the candidate completion time-frequency component set in the missing region, and this set is output to subsequent steps to further determine the effective completion positions and amplitude parameters, thereby realizing the gradual reconstruction of the missing region.

[0063] S6: Based on the candidate completion time-frequency components, calculate the main lobe-side lobe ratio based on the matched filter output, and determine the effective completion position and corresponding amplitude parameters according to the preset decision criteria; Specifically, based on the candidate completion time-frequency components, the main lobe-side lobe ratio is calculated based on the matched filter output, and the effective completion position and corresponding amplitude parameters are determined according to a preset decision criterion, including: The candidate time-frequency components are filled into the missing regions of the time-frequency representation to construct the corresponding completed time-frequency representation; Perform matched filtering on the completed time-frequency representation and calculate the corresponding main lobe-side lobe ratio; Based on the comparison between the main lobe-side lobe ratio and the preset decision threshold, the completion positions that meet the decision conditions are determined as valid completion positions. Based on the peak value of the matched filter main lobe at the effective completion position, and combined with the linear relationship between the proportion of time-frequency components and the peak value of the matched filter, the corresponding amplitude parameter is determined.

[0064] In this embodiment, the obtained candidate time-frequency components are filled into the corresponding empty regions of the time-frequency representation one by one, constructing multiple sets of candidate time-frequency representations. During the filling process, only the candidate filling positions and their corresponding time-frequency components are filled, while the remaining non-empty regions retain their original time-frequency components to ensure comparability between different candidate filling schemes. In this way, a set of filled time-frequency representations corresponding one-to-one with the candidate filling positions can be obtained, providing a basis for subsequent matched filter evaluation.

[0065] Subsequently, matched filtering is performed on each group of completed time-frequency representations. Specifically, the completed time-frequency representation is first subjected to inverse time-frequency transformation to recover the time-domain echo signal. Then, a filter matching the radar transmitted signal is used to perform matched filtering on the time-domain signal to obtain the corresponding pulse compression output result. Taking the range cell corresponding to the candidate completion position as the center, the main lobe peak value at that position is extracted, and the maximum side lobe peak value is searched within a preset distance range in its vicinity. The main lobe-side lobe ratio corresponding to the candidate completion position is calculated to measure the comprehensive effect of the completion scheme on target main lobe enhancement and side lobe suppression.

[0066] After obtaining the main lobe-to-side lobe ratios corresponding to each candidate completion position, this embodiment compares the main lobe-to-side lobe ratios with preset decision criteria to determine the completion positions that meet the decision conditions as valid completion positions. Decision criteria may include conditions such as a main lobe-to-side lobe ratio higher than a threshold, a significant peak in the main lobe-to-side lobe ratio during the sliding sequence, and sufficient distinguishability from adjacent completion positions, to avoid misclassifying noise peaks or side lobe peaks as the true target position. Through this decision process, the completion position corresponding to the true target can be accurately located among multiple candidate completion positions.

[0067] After determining the effective completion location, this embodiment further determines the corresponding amplitude parameters at that location. Specifically, based on the main lobe peak value of the matched filter output at that location, and combined with the overall linear relationship between the proportion of the filled time-frequency components in the missing region and the peak value of the matched filter, the energy loss of the main lobe caused by the absence of time-frequency components is estimated to compensate for the loss, thereby deducing the amplitude scale that the real target signal should have at that location. The determined amplitude parameters will be used in subsequent steps to perform final reconstruction processing on the missing time-frequency region to achieve accurate recovery of the real target signal.

[0068] Specifically, the formula for calculating the preset decision threshold is as follows:

[0069] in, Indicates the decision threshold parameter; Indicates the threshold scaling factor; Indicates the first The amplitude statistics corresponding to each time-frequency unit; i represents the time-frequency index variable; This indicates the index position corresponding to the maximum target amplitude. This represents the half-width parameter of the sliding window.

[0070] threshold It can be calculated Nearby 2 The average value of the ratio of the main lobe to the side lobe If Significantly higher than the neighboring 2 If the main lobe-to-side lobe ratio is a certain value, then it can pass the threshold detection; otherwise, it cannot pass the threshold detection. When the main lobe-to-side lobe ratio... If the threshold detection fails, it indicates that the main lobe peak is low or the side lobe peak is high. A low main lobe peak means that a high matched filter peak cannot be formed at that location, while a high side lobe peak may indicate high noise power or large gaps in the signal spectrum at that location. Both possible reasons suggest that there is no real target signal at that location or that the real target signal has been completely submerged by noise, making signal reconstruction impossible. At this point, the signal reconstruction step is complete, and the inverse short-time Fourier transform can be directly performed to the time domain for subsequent signal processing such as matched filtering.

[0071] The formula for calculating the amplitude parameter is as follows: ; in, This represents the complex amplitude of the reconstructed signal obtained after reconstructing the signal in the missing time-frequency domain. Indicates the normalized complex amplitude. Indicates phase; This represents the peak amplitude of the matched filter obtained in the pulse compression domain after filling in and reconstructing the missing time-frequency domain signal; This represents the peak value of the matched filter corresponding to the missing time-frequency domain; This represents the area of ​​the time-frequency component of the target signal in the reconstructed time-frequency domain; This represents the area of ​​the target signal time-frequency component that has not yet been disturbed and set to zero in the empty time-frequency domain; The area of ​​the complete time-frequency domain signal corresponding to the complete real target signal is represented by N; N represents the number of sampling points.

[0072] S7: Based on the determined effective completion position and amplitude parameters, complete the reconstruction of the time-frequency missing region, and perform inverse time-frequency transformation and matched filtering on the reconstructed time-frequency domain representation to output the target signal after suppressing high-power interference.

[0073] Specifically, the time-frequency gap region is reconstructed based on the determined effective completion position and amplitude parameters. The reconstructed time-frequency domain representation then undergoes inverse time-frequency transformation and matched filtering to output the target signal after suppressing high-power interference, including: Based on the effective completion position and amplitude parameters, the amplitude of the corresponding candidate completion time-frequency components is assigned and filled into the missing area of ​​the time-frequency representation to form the reconstructed time-frequency domain representation; Perform an inverse short-time Fourier transform on the reconstructed time-frequency domain representation to obtain the corresponding reconstructed time-domain echo signal; The reconstructed time-domain echo signal is subjected to matched filtering to output the target signal after suppressing high-power interference.

[0074] In this embodiment, after determining the effective completion position and its corresponding amplitude parameter, the final reconstruction process is performed on the missing regions in the time-frequency representation. Specifically, based on the effective completion position, a corresponding component set is selected from the candidate completion time-frequency components, and the amplitude of the candidate completion time-frequency component is assigned an amplitude value according to the amplitude parameter to match its energy level with the real target echo. Then, the amplitude-adjusted time-frequency components are filled into the missing regions of the time-frequency representation to form the reconstructed time-frequency domain representation. During the filling process, only the missing regions are filled, while the other time-frequency components remain unchanged to ensure the stability and consistency of the reconstruction results.

[0075] After time-frequency domain reconstruction is completed, an inverse short-time Fourier transform is performed on the reconstructed time-frequency domain representation to obtain the corresponding reconstructed time-domain echo signal. Specifically, according to the framing method and overlap parameters used during the time-frequency transformation, each time frame is inversely transformed and then overlapped and added together to reconstruct the continuous time-domain echo signal. Since the time-frequency components in the missing regions have been effectively filled in, the reconstructed time-domain echo signal has more complete target signal components compared to the echo signal that only undergoes interference suppression.

[0076] Subsequently, matched filtering is performed on the reconstructed time-domain echo signal to output the target signal after suppressing high-power interference. Through matched filtering, the main lobe peak corresponding to the real target is more prominent, the side lobe level tends to normal, and the false targets and noise distortion caused by high-power interference are significantly suppressed, thereby achieving effective recovery of the real target signal and improving the target detection performance of the radar system in strong interference environments.

[0077] This embodiment illustrates how to combat frequency hopping interference and spectrum dispersion interference: Countermeasures against frequency hopping interference are as follows: refer to Figure 4 As shown, Figure 4 (a) is a schematic diagram of the time-frequency domain representation of the radar echo signal acquired under frequency-hopping jamming conditions. The horizontal axis represents time delay in microseconds, and the vertical axis represents frequency in megahertz. It can be seen that frequency-hopping jamming manifests in the time-frequency domain as multiple high-energy patches that randomly jump in the frequency dimension and are intermittently distributed in the time dimension. Their frequency positions are not fixed and their distribution is discrete, significantly different from continuous frequency modulation or single-frequency interference. This type of frequency-hopping jamming severely covers the real target signal in the time-frequency domain, causing the time-frequency structure of the target signal to be segmented and masked.

[0078] Figure 4 (b) is for Figure 4 (a) shows a time-frequency domain representation of the gaps after frequency-hopping interference has been identified and suppressed using the threshold decision method. By setting the time-frequency components of the frequency-hopping interference with amplitudes significantly higher than the background to zero, multiple discrete regions where the original frequency-hopping interference was located are effectively removed, thus forming irregularly distributed and randomly located gaps in the time-frequency domain. Since the frequency-hopping interference overlaps with the real target signal at some time-frequency locations, some time-frequency components of the real target signal are also removed during the interference suppression process, resulting in a significant discontinuity in the target signal in the time-frequency domain.

[0079] Figure 4 (c) For Figure 4 (b) shows a schematic diagram of the result after time-frequency domain signal reconstruction in the missing time-frequency domain. By constructing a reference time-frequency model of the original transmitted signal and performing sliding matching along the time dimension, the effective completion position and corresponding amplitude parameters are determined in the missing region, thereby completing the time-frequency components missing due to frequency hopping interference suppression. In the reconstructed time-frequency domain, the time-frequency components of the real target signal are restored to a continuous and complete structure, while the discrete high-energy components corresponding to frequency hopping interference no longer appear, indicating that the present invention can effectively recover the target signal under complex frequency hopping interference conditions.

[0080] Figure 4 (d) is for Figure 4(c) shows a schematic diagram of the pulse compression result obtained by performing inverse time-frequency transform and matched filtering in the reconstructed time-frequency domain. The horizontal axis represents distance, and the vertical axis represents normalized amplitude. It can be seen that the main lobe peak corresponding to the real target is clearly prominent, the side lobe levels are within the normal range, and the false targets and noise floor rise caused by frequency hopping interference are significantly suppressed, further demonstrating the structure of the target's main lobe and its adjacent side lobes. This result indicates that the echo signal reconstructed in the time-frequency domain effectively eliminates false targets and distortions caused by high-power interference in the matched filter output, achieving accurate recovery of the real target signal.

[0081] comprehensive Figure 4 (a) to Figure 4 (d) It can be seen that the present invention first suppresses high-power interference in the time-frequency domain, then performs time-frequency domain signal reconstruction for the missing region introduced by interference suppression, and finally achieves effective recovery of the real target signal in the matched filter output. Figure 4 The invention demonstrates that even under complex conditions where the frequency position of frequency hopping interference changes randomly and the time-frequency distribution is discrete and irregular, it can still achieve the technical effect of both interference suppression and target signal reconstruction, significantly improving the anti-interference performance and target detection reliability of the radar system.

[0082] The following are examples of spectral dispersion interference: refer to Figure 5 As shown, Figure 5 (a) is a schematic diagram of the time-frequency domain representation of radar echo signals acquired under spectral dispersion interference. The horizontal axis represents time delay in microseconds, and the vertical axis represents frequency in megahertz. It can be seen that spectral dispersion interference manifests in the time-frequency domain as multiple short-duration, wide-bandwidth high-energy components densely distributed along the frequency dimension and exhibiting a recurring structure along the time dimension, causing the interference energy to spread throughout the entire operating bandwidth. This type of interference has a wide coverage and uniform distribution in the time-frequency domain, continuously obscuring the real target signal and making it difficult to directly identify the time-frequency structure of the target signal.

[0083] Figure 5 (b) is for Figure 5 (a) illustrates the time-frequency domain representation of the gaps after identifying and suppressing spectral dispersion interference using a threshold decision method. By setting the time-frequency components of spectral dispersion interference with amplitudes significantly higher than the background to zero, the broadband interference energy, originally continuously distributed in the frequency dimension, is effectively removed, thus forming large-scale, strip-shaped, or block-shaped gaps in the time-frequency domain. Since spectral dispersion interference has broad coverage of the real target signal within its frequency band, this suppression process also simultaneously removes some of the time-frequency components of the real target signal, resulting in a significant absence of the target signal in the time-frequency domain.

[0084] Figure 5 (c) For Figure 5 (b) shows a schematic diagram of the result after time-frequency domain signal reconstruction in the missing time-frequency domain. By constructing a reference time-frequency model of the original transmitted signal and performing sliding matching along the time dimension, the effective completion position and corresponding amplitude parameters are gradually determined in the missing region, thereby completing the target time-frequency components missing due to spectral dispersion interference suppression. In the reconstructed time-frequency domain, the time-frequency components of the real target signal are restored to a continuous and complete oblique line structure, while the broadband high-energy components corresponding to spectral dispersion interference no longer appear, indicating that the present invention can effectively recover the target signal under broadband and dense interference conditions.

[0085] Figure 5 (d) is for Figure 5 (c) shows a schematic diagram of the pulse compression result obtained by performing inverse time-frequency transform and matched filtering in the reconstructed time-frequency domain. The horizontal axis represents distance, and the vertical axis represents normalized amplitude. It can be seen that the main lobe peak corresponding to the real target is clearly prominent, and the side lobe level is maintained within the normal range. No false targets or envelope distortion caused by spectral dispersion interference are observed. The inset further magnifies the target main lobe and its adjacent side lobe structure, verifying the effective recovery effect of the target echo.

[0086] comprehensive Figure 5 (a) to Figure 5 (d) It can be seen that under the complex conditions of a large number of spectrum dispersion interference subbands, wide coverage bandwidth, and dense time-frequency distribution, the present invention can effectively suppress spectrum dispersion interference and restore the real target signal without relying on prior interference information by identifying interference in the time-frequency domain, constructing missing regions, and processing signal reconstruction. Figure 5 The applicability and robustness of the invention in countering spectrum dispersion interference scenarios have been fully verified, demonstrating its technical advantages in complex electromagnetic countermeasures environments.

[0087] Please see Figure 6 This application also provides a time-frequency domain signal reconstruction system for combating high-power interference signals, the system comprising: The acquisition module is used to acquire radar echo signals; The interference identification module is used to perform time-frequency transformation on the radar echo signal and use a threshold decision method to identify the time-frequency components corresponding to high-power interference. The interference suppression module is used to suppress time-frequency components and form a time-frequency representation that includes the missing region. The time-frequency model construction module is used to construct a reference time-frequency model of the original transmitted signal based on the missing regions in the time-frequency representation; The candidate completion determination module is used to perform sliding matching along the time dimension on the reference time-frequency model to determine the candidate completion time-frequency components in the missing region; The decision parameter determination module is used to calculate the main lobe-side lobe ratio based on the matched filter output according to the candidate completion time-frequency components, and determine the effective completion position and corresponding amplitude parameters according to the preset decision criteria. The signal reconstruction output module is used to reconstruct the time-frequency gap region based on the determined effective completion position and amplitude parameters, and to perform inverse time-frequency transformation and matched filtering on the reconstructed time-frequency domain representation to output the target signal after suppressing high-power interference.

[0088] It is understood that the content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0089] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0090] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0091] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0092] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0093] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0094] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0095] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0096] This application provides a time-frequency domain signal reconstruction method and system for combating high-power interference signals. This scheme acquires radar echo signals and combines time-frequency transformation with threshold decision to accurately identify and locate the time-frequency components of high-power interference. Based on this, targeted suppression of the interference components is performed, avoiding the large-area information loss caused by traditional overall filtering or frequency band shielding methods on the effective target signal, thus reducing the degree of interference damage to the signal structure at the source. Furthermore, by constructing a reference time-frequency model of the original transmitted signal and performing sliding matching along the time dimension, structured completion of missing regions and constraint search of candidate time-frequency components are achieved, enabling... The reconstruction process has been transformed from "blind completion" to "model-guided completion," significantly improving the physical consistency and signal continuity of time-frequency reconstruction. Simultaneously, by combining the matched filter output to calculate the main lobe-side lobe ratio and introducing decision criteria to screen effective completion positions and amplitude parameters, the problems of spurious completion and noise amplification are effectively suppressed, avoiding the introduction of false energy that interferes with target detection and parameter estimation. Finally, through time-frequency gap region reconstruction, inverse time-frequency transform, and matched filtering, high-power interference is efficiently suppressed and target signal is recovered with high fidelity. This significantly improves the radar system's target detection reliability, signal-to-noise ratio, time-frequency structure integrity, and the stability and accuracy of subsequent signal processing algorithms under strong interference environments.

[0097] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0098] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0099] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0100] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0101] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A time-frequency domain signal reconstruction method for combating high-power interference signals, characterized in that, The method includes the following steps: Acquire radar echo signals; The radar echo signal is subjected to time-frequency transformation, and the time-frequency component corresponding to high-power interference is identified by a threshold decision method. The time-frequency components are suppressed to form a time-frequency representation that includes the missing regions; Based on the missing regions in the time-frequency representation, a reference time-frequency model of the original transmitted signal is constructed; Sliding matching is performed along the time dimension on the reference time-frequency model to determine candidate completion time-frequency components in the missing region; Based on the candidate completion time-frequency components, the main lobe-side lobe ratio is calculated based on the matched filter output, and the effective completion position and corresponding amplitude parameters are determined according to the preset decision criteria. Based on the determined effective completion position and amplitude parameters, the time-frequency missing region is reconstructed, and the reconstructed time-frequency domain representation is subjected to inverse time-frequency transformation and matched filtering to output the target signal after suppressing high-power interference.

2. The method according to claim 1, characterized in that, The radar echo signal includes the echo signal of the real target and the high-power jamming signal.

3. The method according to claim 1, characterized in that, The step of performing time-frequency transformation on the radar echo signal and identifying the time-frequency components corresponding to high-power interference using a threshold decision method includes: The radar echo signal is subjected to a short-time Fourier transform to obtain the time-frequency amplitude matrix of the radar echo signal. An amplitude statistical distribution is constructed based on the time-frequency amplitude matrix, and a high-power decision threshold is determined according to the maximum inter-class variance method. Based on the high power decision threshold, the time-frequency amplitude matrix is ​​divided into thresholds, and time-frequency units exceeding the high power decision threshold are identified as time-frequency components corresponding to high power interference.

4. The method according to claim 3, characterized in that, The calculation formula for the Otsu's inter-class variance method is as follows: ； in, This represents the pixel value corresponding to any pixel in the time-frequency domain image. Indicates the decision threshold; This indicates that the pixel value is greater than or equal to the decision threshold. The set of pixels corresponds to the high-power foreground pixels in the time-frequency domain; This indicates that the pixel value is less than the decision threshold. The set of pixels corresponds to the background pixels in the time-frequency domain; Represents the set of foreground pixels The number of pixels contained in it; Represents the set of background pixels The number of pixels contained in the image; m represents the number of rows in the time-frequency image; n represents the number of columns in the time-frequency image; and These represent the proportion and weight of foreground and background pixels in the entire time-frequency domain image, respectively. and Representing the foreground pixel set and background pixel set The sum of all pixel values ​​in the array; and These represent the average pixel values ​​of the foreground pixel set and the background pixel set, respectively. The inter-class variance function is a measure of the difference between foreground and background. This indicates that when traversing all possible thresholds, the inter-class variance is minimized. The threshold index for obtaining the maximum value; This represents the optimal threshold determined based on the maximum inter-class variance criterion.

5. The method according to claim 1, characterized in that, The step of performing sliding matching along the time dimension on the reference time-frequency model to determine candidate completion time-frequency components in the missing region includes: The reference time-frequency model is slid-aligned along the time dimension, and the overlapping time-frequency components of the reference time-frequency model and the missing region are extracted at each sliding position. Perform matching response calculation on the overlapping time-frequency components to obtain the matching response value at the corresponding sliding position; A sliding matching response sequence is constructed based on the matching response value, and time-frequency components that meet the preset criteria are selected from the sliding matching response sequence as candidate time-frequency components for filling in the missing region.

6. The method according to claim 1, characterized in that, The step of calculating the main lobe-side lobe ratio based on the matched filter output according to the candidate completion time-frequency components, and determining the effective completion position and corresponding amplitude parameters according to a preset decision criterion, includes: The candidate completed time-frequency components are filled into the missing regions of the time-frequency representation to construct the corresponding completed time-frequency representation; Perform matched filtering on the completed time-frequency representation and calculate the corresponding main lobe-side lobe ratio; Based on the comparison result between the main lobe-side lobe ratio and the preset decision threshold, the completion position that meets the decision condition is determined as the effective completion position; Based on the peak value of the matched filter main lobe at the effective completion position, and combined with the linear relationship between the proportion of time-frequency components and the peak value of the matched filter, the corresponding amplitude parameter is determined.

7. The method according to claim 6, characterized in that, The formula for calculating the preset decision threshold is as follows: in, Indicates the decision threshold parameter; Indicates the threshold scaling factor; Indicates the first The amplitude statistics corresponding to each time-frequency unit; i represents the time-frequency index variable; This indicates the index position corresponding to the maximum target amplitude. This represents the half-width parameter of the sliding window.

8. The method according to claim 6, characterized in that, The formula for calculating the amplitude parameter is as follows: ； in, This represents the complex amplitude of the reconstructed signal obtained after reconstructing the signal in the missing time-frequency domain. Indicates the normalized complex amplitude. Indicates phase; This represents the peak amplitude of the matched filter obtained in the pulse compression domain after filling in and reconstructing the missing time-frequency domain signal; This represents the peak value of the matched filter corresponding to the missing time-frequency domain; This represents the area of ​​the time-frequency component of the target signal in the reconstructed time-frequency domain; This represents the area of ​​the target signal time-frequency component that has not yet been disturbed and set to zero in the empty time-frequency domain; The area of ​​the complete time-frequency domain signal corresponding to the complete real target signal is represented by N; N represents the number of sampling points.

9. The method according to claim 1, characterized in that, The process of reconstructing the time-frequency gap region based on the determined effective completion position and amplitude parameters, performing inverse time-frequency transformation and matched filtering on the reconstructed time-frequency domain representation, and outputting the target signal after suppressing high-power interference includes: Based on the effective completion position and the amplitude parameter, the amplitude of the corresponding candidate completion time-frequency component is assigned and filled into the missing area of ​​the time-frequency representation to form the reconstructed time-frequency domain representation; Perform an inverse short-time Fourier transform on the reconstructed time-frequency domain representation to obtain the corresponding reconstructed time-domain echo signal; The reconstructed time-domain echo signal is subjected to matched filtering to output the target signal after suppressing high-power interference.

10. A time-frequency domain signal reconstruction system for combating high-power interference signals, characterized in that, The system includes: The acquisition module is used to acquire radar echo signals; An interference identification module is used to perform time-frequency transformation on the radar echo signal and use a threshold decision method to identify the time-frequency components corresponding to high-power interference. An interference suppression module is used to suppress the time-frequency components to form a time-frequency representation that includes the missing region; The time-frequency model construction module is used to construct a reference time-frequency model of the original transmitted signal based on the missing regions in the time-frequency representation; The candidate completion determination module is used to perform sliding matching along the time dimension on the reference time-frequency model to determine the candidate completion time-frequency components in the missing region; The decision parameter determination module is used to calculate the main lobe-side lobe ratio based on the matched filter output according to the candidate completion time-frequency components, and determine the effective completion position and corresponding amplitude parameters according to the preset decision criteria. The signal reconstruction output module is used to reconstruct the time-frequency gap region based on the determined effective completion position and amplitude parameters, and to perform inverse time-frequency transformation and matched filtering on the reconstructed time-frequency domain representation to output the target signal after suppressing high-power interference.

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