Method and device for suppressing cross interference of millimeter wave radar signals in underground coal mine

Abstract

The invention relates to a method and a device for suppressing cross interference of millimeter wave radar signals in an underground coal mine. The method comprises the following steps: acquiring an underground millimeter wave radar signal of a coal mine; taking the maximum mutual information coefficient as a fitness function, taking the millimeter wave radar signal as input, and optimizing a maximum regularization parameter maxAlpha of successive variational mode decomposition (SVMD) by using an optimization search algorithm to obtain a target maxAlpha value; based on the target maxAlpha value, decomposing the millimeter wave radar signal through SVMD to obtain a plurality of intrinsic mode functions IMF; performing wavelet threshold processing on each IMF in the plurality of IMFs, and extracting to obtain an interference signal component in each IMF; and respectively removing the interference signal component corresponding to each IMF, and performing reconstruction by using the IMF from which the interference signal component is removed to obtain a target millimeter wave radar signal of which the interference signal is suppressed. According to the scheme, the signal cross interference suppression effect of the underground coal mine millimeter wave radar is improved.

Description

Technical Field

[0001] This disclosure relates to the field of coal mining technology, and in particular to a method and apparatus for suppressing cross-interference of millimeter-wave radar signals in underground coal mines. Background Technology

[0002] Among related technologies, millimeter-wave radar, due to its strong penetration and all-weather operation capabilities, has become a key sensor for underground vehicles and robots to perceive their environment. However, the narrow space and dense deployment of radar in underground tunnels lead to electromagnetic interference between devices, generating cross-interference signals. This interference significantly raises the radar echo noise floor, causing target misses and collision warning system failures or delays, seriously threatening the safety of underground operations. Existing anti-interference technologies, such as waveform design optimization, have fixed parameters that are difficult to adapt to complex dynamic environments; while filtering methods suffer from interference residues or high computational complexity, making it difficult to balance real-time performance and suppression effectiveness, thus restricting the reliable application of radar underground. Summary of the Invention

[0003] To overcome the problems existing in related technologies, this disclosure provides a method and device for suppressing cross-interference of millimeter-wave radar signals in underground coal mines.

[0004] According to a first aspect of the present disclosure, a method for suppressing cross-interference of millimeter-wave radar signals in underground coal mines is provided, comprising: Acquire millimeter-wave radar signals from underground coal mines; Using the maximum mutual information coefficient as the fitness function and the millimeter-wave radar signal as input, the maximum regularization parameter maxAlpha of the successive variational mode decomposition SVMD is optimized using an optimization search algorithm to obtain the target maxAlpha value. Based on the target maxAlpha value, the millimeter-wave radar signal is decomposed using SVMD to obtain multiple intrinsic mode functions (IMFs). Wavelet thresholding is performed on each of the plurality of IMFs to extract the interference signal components in each IMF; The interference signal components corresponding to each IMF are removed, and the IMFs after removing the interference signal components are reconstructed to obtain the target millimeter-wave radar signal with suppressed interference signals.

[0005] According to a second aspect of the present disclosure, a device for suppressing cross-interference of millimeter-wave radar signals in underground coal mines is provided, comprising: Acquisition unit, used to acquire millimeter-wave radar signals from underground coal mines; The optimization unit is used to optimize the maximum regularization parameter maxAlpha of the successive variational mode decomposition SVMD using the maximum mutual information coefficient as the fitness function and the millimeter-wave radar signal as input, and to obtain the target maxAlpha value by using the optimization search algorithm. The decomposition unit is used to decompose the millimeter-wave radar signal based on the target maxAlpha value using SVMD to obtain multiple intrinsic mode functions (IMFs). An extraction unit is used to perform wavelet thresholding on each of the plurality of IMFs to extract the interference signal components in each IMF. The suppression unit is used to remove the corresponding interference signal components in each IMF, and then reconstruct the target millimeter-wave radar signal with the interference signal suppressed by using the IMF after removing the interference signal components.

[0006] According to a third aspect of the present disclosure, an electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as described in any one of the first aspects.

[0007] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method as described in any one of the first aspects.

[0008] According to a fifth aspect of the present disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the method as described in any one of the first aspects.

[0009] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects: acquiring millimeter-wave radar signals from underground coal mines; using the maximum mutual information coefficient as the fitness function and the millimeter-wave radar signal as input, optimizing the maximum regularization parameter maxAlpha of successive variational mode decomposition (SVMD) using an optimization search algorithm to obtain the target maxAlpha value; based on the target maxAlpha value, decomposing the millimeter-wave radar signal using SVMD to obtain multiple intrinsic mode functions (IMFs); performing wavelet thresholding on each IMF to extract the interference signal components in each IMF; removing the corresponding interference signal components from each IMF respectively, and reconstructing the IMF after removing the interference signal components to obtain the target millimeter-wave radar signal with suppressed interference signals. By dynamically optimizing the SVMD decomposition parameters using the maximum mutual information coefficient as the fitness function, accurate separation of target and interference signals at the modal level is achieved. Combined with inverse wavelet thresholding, it can effectively suppress interference signals in strong cross-interference environments while preserving complete target information to the maximum extent. This significantly improves the suppression effect of cross-interference of millimeter-wave radar signals in underground coal mines and reduces the false alarm rate. Moreover, the entire processing flow has high computational efficiency and fully meets the real-time processing requirements of underground mobile equipment.

[0010] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0011] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0012] Figure 1 This is a flowchart illustrating a method for suppressing cross-interference of millimeter-wave radar signals in underground coal mines, according to an exemplary embodiment.

[0013] Figure 2 This is a flowchart illustrating the suppression of cross-interference of millimeter-wave radar signals in coal mines, as proposed in this embodiment.

[0014] Figure 3 This is a block diagram illustrating a device for suppressing cross-interference of millimeter-wave radar signals in an underground coal mine, according to an exemplary embodiment.

[0015] Figure 4 This is a block diagram of an apparatus for suppressing cross-interference of millimeter-wave radar signals in underground coal mines, according to an exemplary embodiment.

[0016] Figure Labels 301 - Acquisition unit; 302 - Optimization unit; 303 - Decomposition unit; 304 - Extraction unit; 305 - Suppression unit; 400 - Device; 402 - Processing component; 404 - Memory; 406 - Power component; 408 - Multimedia component; 410 - Audio component; 412 - I / O interface; 414 - Sensor component; 416 - Communication component; 420 - Processor. Detailed Implementation

[0017] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0018] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the present disclosure. The singular forms “a” and “the” as used in this disclosure and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise.

[0019] It should be understood that although the terms first, second, third, etc., may be used to describe various information in embodiments of this disclosure, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first information may also be referred to as second information without departing from the scope of embodiments of this disclosure, and similarly, second information may also be referred to as first information. Depending on the context, the words “if” and “suppose” as used herein may be interpreted as “when”, “when”, or “in response to a determination”.

[0020] Furthermore, various forms of processes shown in the embodiments of this disclosure can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and no limitation is imposed herein.

[0021] It should be noted that the collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution disclosed herein all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0022] In related technologies, with the deepening development of intelligent coal mine construction and the widespread application of various coal mine robots, traditional sensing equipment can no longer meet the precise sensing needs in mobile scenarios in complex environments characterized by explosive gases, high dust, high humidity, and low illumination in underground coal mines. Millimeter-wave radar, by emitting electromagnetic waves and analyzing the echo signals, detects the distance, speed, and direction of objects, and is suitable for detecting dynamic targets such as vehicles and pedestrians. Although millimeter-wave radar has relatively low resolution and cannot provide detailed shape information of objects, it has the advantages of low cost, all-weather operation, wide detection range, strong penetration, and is not easily affected by harsh environments such as dust and water mist. It can be effectively applied to the complex environment of underground coal mines to achieve high-precision detection and identification of targets such as vehicles, personnel, and obstacles, providing reliable sensing support for intelligent coal mine construction and robot applications.

[0023] However, with the widespread application of millimeter-wave radar in underground coal mines, the deployment density of radars has increased dramatically due to the narrow environment of the tunnels. This has led to increasingly prominent interference issues between millimeter-wave radars mounted on different mobile devices, such as trackless rubber-wheeled vehicles and quadrupedal inspection robots, becoming a significant factor restricting the reliability of millimeter-wave radar systems. Cross-interference between radars degrades the detection performance of the radar system and increases the probability of missed target detection. In mobile underground scenarios, this target detection failure caused by interference can lead to the failure or delay of collision warning systems, posing a serious threat to the safety of underground workers.

[0024] Existing millimeter-wave radars can be optimized through waveform design. This method uses waveform modulation techniques to change the waveform parameters of the radar's transmitted signal, effectively disrupting the correlation between the transmitted signal and the interference signal, thereby improving the radar system's anti-interference performance. Alternatively, interference suppression for vehicle-mounted millimeter-wave radars can be achieved through filtering methods. The core idea is to utilize the characteristic differences between the target signal and the interference signal in the time domain, frequency domain, or time-frequency domain, and to design appropriate filters to achieve signal separation and interference suppression.

[0025] However, waveform design optimization methods typically require pre-setting some waveform parameters, such as the modulation slope. Interference conditions in real-world environments are complex and varied, and fixed waveform parameters may not be suitable for all situations. For example, on roads with heavy traffic, if the modulation slope of the vehicle's radar is insufficient, the false alarm rate caused by mutual interference will increase; conversely, on roads with light traffic, if the modulation slope is too high, it will place an unnecessary burden on the radar's transmitting front-end.

[0026] Furthermore, existing filter-based methods have relatively poor performance and may not be able to completely eliminate interference signals, leaving some residual interference. These residual interference signals can still affect the radar's detection results, leading to an increased false alarm rate or inaccurate target detection. While some advanced filtering methods, such as adaptive filtering and space-time adaptive filtering, have better anti-interference performance, they have high computational complexity and require significant computing resources and time. This may place higher demands on the real-time performance and processing capabilities of vehicle-mounted millimeter-wave radar, increasing system cost and power consumption.

[0027] To address the aforementioned issues, this disclosure provides a method and apparatus for suppressing cross-interference of millimeter-wave radar signals in underground coal mines. The method involves acquiring millimeter-wave radar signals from underground coal mines; using the maximum mutual information coefficient as the fitness function and the millimeter-wave radar signals as input, optimizing the maximum regularization parameter `maxAlpha` of Successive Variational Mode Decomposition (SVMD) using an optimization search algorithm to obtain a target `maxAlpha` value; based on the target `maxAlpha` value, decomposing the millimeter-wave radar signals using SVMD to obtain multiple Intrinsic Mode Functions (IMFs); performing wavelet thresholding on each IMF to extract the interference signal components within each IMF; removing the corresponding interference signal components from each IMF; and reconstructing the IMFs after removing the interference signal components to obtain the target millimeter-wave radar signal with suppressed interference signals. By dynamically optimizing the SVMD decomposition parameters using the maximum mutual information coefficient as the fitness function, accurate separation of target and interference signals at the modal level is achieved. Combined with inverse wavelet thresholding, it can effectively suppress interference signals in strong cross-interference environments while preserving complete target information to the maximum extent. This significantly improves the suppression effect of cross-interference of millimeter-wave radar signals in underground coal mines and reduces the false alarm rate. Moreover, the entire processing flow has high computational efficiency and fully meets the real-time processing requirements of underground mobile equipment.

[0028] Figure 1 This is a flowchart illustrating a method for suppressing cross-interference of millimeter-wave radar signals in underground coal mines, according to an exemplary embodiment. Figure 1 As shown, it should be noted that the method for suppressing cross-interference of millimeter-wave radar signals in underground coal mines, as described in this embodiment, is applied to a device for suppressing cross-interference of millimeter-wave radar signals in underground coal mines. Figure 1 As shown, the method may include the following steps: Step 101: Acquire millimeter-wave radar signals from underground coal mines.

[0029] In one embodiment, millimeter-wave radar signals collected by millimeter-wave radar in coal mines can be received in real time.

[0030] As an example of a possible implementation, after step 101, it can be determined whether the millimeter-wave radar signal is subject to cross-interference. If the millimeter-wave radar signal is subject to cross-interference, step 102 is executed.

[0031] In some embodiments, the average power or noise floor of the millimeter-wave radar signal outside the target range cell can be calculated. If this value exceeds a preset threshold, cross-interference is considered to exist. Alternatively, a conventional fast Fourier transform can be performed on the millimeter-wave radar signal to detect the presence of numerous weak, spurious points in the range or velocity spectrum that are difficult to explain using real physical targets. If the number and density of spurious points exceed a threshold, it is determined to be disturbed.

[0032] Step 102: Using the maximum mutual information coefficient as the fitness function and the millimeter-wave radar signal as input, the maximum regularization parameter maxAlpha of the successive variational mode decomposition SVMD is optimized using an optimization search algorithm to obtain the target maxAlpha value.

[0033] It should be noted that the goal of SVMD is to decompose a complex, non-stationary signal (i.e., a millimeter-wave radar signal) into a series of relatively simple sub-signals (i.e., intrinsic mode functions, IMFs) with different center frequencies. Each IMF represents an independent, physically meaningful component of the signal. To suppress cross-interference of millimeter-wave radar signals in underground coal mines, it is necessary to separate different target echoes and different interference signals into different IMFs.

[0034] The core of the SVMD mathematical model is a constrained optimization problem that requires a balance between the following two points: each decomposed IMF should be compact, meaning its energy is mainly concentrated around a certain center frequency; and all IMFs, when added together, should perfectly reconstruct the original signal. `maxAlpha` determines the "refinement" or "intensity" of the signal decomposition. In other words, if the `maxAlpha` value is too large, under-decomposition will occur, failing to fully extract features; if the `maxAlpha` value is too small, over-decomposition will occur, leading to increased computational cost.

[0035] Therefore, this disclosure utilizes an optimization search algorithm to optimize the maximum regularization parameter maxAlpha of successive variational mode decomposition (SVMD) to obtain the optimal target maxAlpha value, effectively overcoming the under-decomposition and over-decomposition problems caused by traditional empirical settings or fixed parameters.

[0036] In some embodiments of this disclosure, since SVMD has band-limited decomposition characteristics, the modes are orthogonal to each other and have different center frequencies. Targets at different distances can be separated into different modes for corresponding processing. While suppressing interference, the integrity of the target signal is preserved to the maximum extent, which is suitable for real-time processing and meets the real-time requirements of millimeter-wave radar.

[0037] Variational Mode Decomposition (VMD) can decompose complex signals into several intrinsic mode functions (IMFs) with different center frequencies and quasi-orthogonal characteristics. These modes exhibit non-overlapping frequency bandwidths in the frequency domain, and each mode represents a local frequency component of the signal. VMD achieves signal decomposition through variational optimization, aiming to minimize the signal reconstruction error while ensuring the independence of the frequency bands of each mode. SVMD builds upon VMD by introducing a stepwise optimization strategy, thereby achieving the gradual extraction of complex components from the signal and further improving the accuracy and performance of decomposition when processing complex signals.

[0038] In some embodiments of this disclosure, step 102 may specifically include the following steps: The population is initialized within a preset maxAlpha search range using the PID (Proportional-Integral-Derivative) search algorithm (PSA). The population is updated iteratively, and the performance of each individual in the population is evaluated based on the fitness function value in each iteration. If the current optimization round is equal to the preset round, the maxAlpha value corresponding to the maximum function value is obtained from the population corresponding to the current optimization round, and the target maxAlpha value is obtained.

[0039] It should be noted that PSA is a global optimization metaheuristic algorithm. Based on the incremental PID method, it converges by continuously adjusting the system's deviation and iteratively finds the optimal value. The optimal value of maxAlpha is found by optimizing the PSA algorithm. The optimization problem based on PSA consists of a set of optimization variables, constraints, and a fitness function. The optimization variable is maxAlpha, and the search range of maxAlpha is [u, l], with a maximum number of iterations. The population size is n.

[0040] In some embodiments of this disclosure, the fitness function value can be calculated using the following steps: SVMD decomposition of millimeter-wave radar signals is performed using candidate maxAlpha values ​​to obtain multiple IMFs; Calculate the first maximum information coefficient (MIC) between adjacent IMFs among multiple IMFs, and calculate the sum of all first MICs for multiple IMFs to obtain the first sum value; the first MIC is the maximum information coefficient. Calculate the second maximum mutual information coefficient (MIC) between each IMF and the millimeter-wave radar signal in multiple IMFs, calculate the sum of all second MICs to obtain the second sum value; calculate the proportion of the first sum value in the second sum value to obtain the function value.

[0041] In some embodiments, the maxAlpha value of all individuals in the population can be initialized using the following formula:

[0042] In the formula A random number within the range (0,1). This is the initialization result for the i-th maxAlpha value.

[0043] After initialization, the fitness value is calculated for all individuals in the population based on the initialization results. The maximum information coefficient (MIC) is selected as the fitness function. MIC is based on mutual information and can be used to measure the correlation between two signals. The discrete form of mutual information is:

[0044] Here, X and Y are two random variables, p(X,Y) is the joint probability of X and Y, p(X) is the marginal probability of X, and p(Y) is the marginal probability of Y. MIC divides the values ​​of X and Y into dx and dy quantity blocks, forming a grid G, and calculates the mutual information within each grid. The maximum mutual information within the grid is... Defined as:

[0045] in, For different sample spaces within grid G, the maximum cross-multiplication information of each space is further normalized and recorded in the feature matrix, denoted as MIC.

[0046] in, It is the number of samples, B( The ) represents the upper limit of the grid size. When millimeter-wave radar signals are subjected to cross-interference, in order to decompose target echo signals at different distances into different modes during mode decomposition, it is necessary to ensure that the MIC between each mode is minimized and the MIC between each mode and the disturbed signal is maximized. Assume the disturbed signal is x(t), and the number of mode decompositions is... m is the index variable, and the fitness function is... Represented as:

[0047] The fitness value of all individuals in the population is calculated using a fitness function and compared. A higher fitness value indicates a better maxAlpha performance for the corresponding individual. PSA uses systematic bias for iterative optimization.

[0048] Step 103: Based on the target maxAlpha value, the millimeter-wave radar signal is decomposed by SVMD to obtain multiple intrinsic mode functions (IMFs).

[0049] In this embodiment of the disclosure, the millimeter-wave radar signal can be set as... Decompose it into the Lth mode and residual signal , Is it except Other input signals, including the sum of modes after decomposition. And unprocessed signals . Represented as:

[0050] SVMD extracts the IMF using a continuous variational mode and has four constraints: Each mode is focused on the center frequency; Constraints are applied using filters to make... Under the premise of having effective components, Minimize energy; Under the first two constraints, to avoid repetition of the Lth mode and the (L-1)th mode, a filter is used to... There is less energy near the center frequency of the first L-1 modes; Each modal and residual signal can be reconstructed into the original signal.

[0051] Based on the four constraints mentioned above, the problem of extracting the Lth mode is transformed into a constraint minimization problem, balanced by the parameter factor α. , , (These correspond to the first three of the four constraints mentioned above):

[0052]

[0053] in, Let be the center frequency of the Lth mode.

[0054] Step 104: Perform wavelet thresholding on each of the multiple IMFs to extract the interference signal components in each IMF.

[0055] In this embodiment, since the energy of noise is generally less than that of the useful signal, the energy difference between noise and the useful signal can be used to suppress noise while preserving the useful signal. After performing mode decomposition on the disturbed signal, the energy of the interference in different IMFs is greater than that of the target echo signal and exhibits sparsity in the time domain. Based on this, the wavelet thresholding idea can be applied in reverse: the target echo is regarded as a "noise-like" component, and the target signal in each IMF is removed step by step through an adaptive threshold to extract the dominant interference component; finally, the interference component is reconstructed from the original disturbed signal and subtracted to achieve interference suppression.

[0056] In some embodiments of this disclosure, step 104 may specifically include the following steps: For each IMF, perform a wavelet transform on the IMF to obtain wavelet coefficients; Determine the adaptive threshold for the IMF; If the amplitude of the wavelet coefficients in the IMF is less than or equal to the adaptive threshold, the wavelet coefficients are set to zero. The processed wavelet coefficients are subjected to inverse wavelet transform to obtain the interference signal components of the IMF.

[0057] In this embodiment, for each intrinsic mode function (IMF) obtained by SVMD decomposition, a wavelet transform is performed to convert it from the time domain to the time-frequency domain, obtaining wavelet coefficients that reflect the local time-frequency characteristics of the signal. Based on the statistical characteristics of the wavelet coefficients of each IMF (such as estimating the noise standard deviation by the median of their absolute values), the corresponding adaptive threshold is calculated independently. Next, the core interference separation operation is performed, setting the wavelet coefficients in each IMF with amplitudes less than or equal to their corresponding adaptive thresholds to zero, thereby retaining the components with strong energy that are considered to be interference-dominant, and suppressing the target signal components with weak energy that are considered to be "noise-like". Finally, the residual wavelet coefficients after thresholding are subjected to inverse wavelet transform to reconstruct them back to the time domain, thereby accurately extracting the interference signal components contained in each IMF, laying the foundation for finally achieving interference cancellation from the original signal.

[0058] In some embodiments of this disclosure, step 104, which determines the adaptive threshold based on the wavelet coefficients of multiple IMFs, may specifically include the following steps: The noise standard deviation is calculated based on the wavelet coefficients of multiple IMFs. Calculate the correlation strength r between the wavelet coefficients of the j-th layer and the IMF; Based on the noise standard deviation σ, the signal length M of the IMF, and the correlation strength r, the baseline threshold λ of the j-th layer is calculated. j; Obtain multiple preset correlation intensity ranges and the scaling ratio corresponding to each correlation intensity range; Determine the interval to which the correlation intensity r belongs from multiple correlation intensity intervals, and adjust the benchmark threshold λ according to the scaling ratio corresponding to the interval. j Scaling is performed to obtain the adaptive threshold λ.

[0059] In this embodiment of the disclosure, the adaptive threshold λ can be calculated using the following formula:

[0060]

[0061]

[0062] in, Let M be the threshold value for the j-th layer of the wavelet decomposition, M be the signal length of the IMF, and σ be the noise standard deviation. denoted as the median of the signal, k as the position index of the wavelet coefficient, and r as the correlation strength between the wavelet decomposition coefficient of the j-th layer and the original signal coefficient (i.e., the wavelet coefficient corresponding to the decomposition level (j-th layer) of the IMF currently being processed after wavelet decomposition of the original millimeter-wave radar signal). This threshold can be adaptively changed according to the different correlation coefficients r.

[0063] Step 105: Remove the corresponding interference signal components from each IMF, and reconstruct the target millimeter-wave radar signal with the interference signals suppressed using the IMF after removing the interference signal components.

[0064] In this embodiment, after accurately extracting the corresponding interference signal components from each intrinsic mode function (IMF), the interference components contained in each IMF are removed from the original components of each IMF to obtain a series of pure IMFs containing only target information. Subsequently, all purified IMFs are reconstructed to effectively remove cross-interference components while fully preserving the echoes of useful targets such as underground vehicles and personnel. Finally, a millimeter-wave radar signal with a clean background and clear target features is output, providing a reliable data foundation for the intelligent sensing system in coal mines.

[0065] In some embodiments of this disclosure, step 105 may specifically include the following steps: The target millimeter-wave radar signal is reconstructed by linearly superimposing the IMFs after removing all interference signal components.

[0066] In some embodiments, such as Figure 2As shown, the process acquires the disturbed radar signal, sets the PSA parameters, initializes the population, sets the current iteration number n=1, and then enters a loop: sequentially calculates the fitness of individual population members, records the global optimum and the optimum value of the nth iteration, calculates the incremental PID and condition factor, updates the population, and completes the iteration count update for n=n+1; then it determines "n <n max "?", where n max The maximum value of the preset iteration rounds is used. If the condition is met, the loop continues; otherwise, the loop exits and the optimal maxAlpha is output. This optimal parameter is then input into the signal processing branch. The SVMD parameters are optimized using PSA, and the signal is decomposed into several Intrinsic Mode Function (IMF) components based on the optimized parameters. Each IMF component is then decomposed into wavelet components and thresholded based on wavelet thresholds to extract interference components. Subsequently, the corresponding interference components are removed from each IMF component in sequence. All processed IMF components are then superimposed and reconstructed to output a clean radar signal with suppressed interference. This effectively suppresses interference and preserves the target signal in complex downhole environments.

[0067] According to the method for suppressing cross-interference of millimeter-wave radar signals in underground coal mines proposed in this disclosure, the following steps are taken: First, millimeter-wave radar signals from underground coal mines are acquired. Using the maximum mutual information coefficient as the fitness function and the millimeter-wave radar signals as input, an optimization search algorithm is used to optimize the maximum regularization parameter maxAlpha of the successive variational mode decomposition (SVMD) to obtain a target maxAlpha value. Based on the target maxAlpha value, the millimeter-wave radar signals are decomposed using SVMD to obtain multiple intrinsic mode functions (IMFs). Wavelet thresholding is performed on each IMF to extract the interference signal components in each IMF. The corresponding interference signal components in each IMF are removed, and the IMFs after removing the interference signal components are reconstructed to obtain the target millimeter-wave radar signal with suppressed interference signals. By dynamically optimizing the SVMD decomposition parameters using the maximum mutual information coefficient as the fitness function, precise separation of target and interference signals at the modal level is achieved. Combined with inverse wavelet thresholding, this effectively suppresses interference signals while preserving complete target information to the maximum extent in environments with strong cross-interference. This significantly improves the suppression effect of cross-interference of millimeter-wave radar signals in underground coal mines and reduces the false alarm rate. Moreover, the entire processing flow has high computational efficiency, fully meeting the real-time processing requirements of underground mobile equipment. Furthermore, after obtaining several intrinsic mode functions (IMFs) through SVMD decomposition of the signal, wavelet transform is performed on each IMF. Since the energy of cross-interference signals in the underground environment is usually stronger than that of the target echo, the wavelet coefficients corresponding to the interference components have higher amplitudes in the time-frequency domain of each IMF, while the target echo behaves like "noise" with lower amplitudes. Based on this, the scheme sets an adaptive threshold, sets wavelet coefficients with amplitudes below the threshold (corresponding to the target signal) to zero, and retains wavelet coefficients with amplitudes above the threshold (corresponding to interference); finally, it performs inverse wavelet transform on the retained coefficients, thereby extracting the pure interference component from each IMF, thus achieving the goal of accurately removing interference and retaining the target from the original signal.

[0068] Figure 3 This is a block diagram illustrating a device for suppressing cross-interference of millimeter-wave radar signals in underground coal mines, according to an exemplary embodiment. (Refer to...) Figure 3 The device includes an acquisition unit 301, an optimization unit 302, a decomposition unit 303, an extraction unit 304, and a suppression unit 305.

[0069] Among them, the acquisition unit 301 is used to acquire millimeter-wave radar signals from underground coal mines; Optimization unit 302 is used to optimize the maximum regularization parameter maxAlpha of successive variational mode decomposition SVMD using the maximum mutual information coefficient as the fitness function and the millimeter-wave radar signal as input, and to obtain the target maxAlpha value by using the optimization search algorithm. Decomposition unit 303 is used to decompose the millimeter-wave radar signal based on the target maxAlpha value using SVMD to obtain multiple intrinsic mode functions (IMFs). Extraction unit 304 is used to perform wavelet thresholding on each of the multiple IMFs to extract the interference signal components in each IMF. The suppression unit 305 is used to remove the corresponding interference signal components in each IMF, and reconstruct the target millimeter-wave radar signal with the interference signal suppressed by using the IMF after removing the interference signal components.

[0070] In some embodiments of this disclosure, the optimization unit 302 may specifically be used for: The PID search algorithm PSA is used to initialize the population within a preset maxAlpha search range; The population is updated iteratively, and the performance of each individual in the population is evaluated based on the fitness function value in each iteration. If the current optimization round is equal to the preset round, the maxAlpha value corresponding to the maximum function value is obtained from the population corresponding to the current optimization round, and the target maxAlpha value is obtained.

[0071] In some embodiments of this disclosure, the optimization unit 302 may specifically be used for: SVMD decomposition of millimeter-wave radar signals is performed using candidate maxAlpha values ​​to obtain multiple IMFs; Calculate the first mutual information coefficient (MIC) between adjacent IMFs among multiple IMFs, and calculate the sum of all first MICs corresponding to multiple IMFs to obtain the first sum value; the first MIC is the maximum mutual information coefficient. Calculate the second MIC between each IMF and the millimeter-wave radar signal in multiple IMFs, calculate the sum of all second MICs to obtain the second sum value; calculate the proportion of the first sum value in the second sum value to obtain the function value.

[0072] In some embodiments of this disclosure, the extraction unit 304 may specifically be used for: For each IMF, perform a wavelet transform on the IMF to obtain wavelet coefficients; Determine the adaptive threshold for the IMF; If the amplitude of the wavelet coefficients in the IMF is less than or equal to the adaptive threshold, the wavelet coefficients are set to zero. The processed wavelet coefficients are subjected to inverse wavelet transform to obtain the interference signal components of the IMF.

[0073] In some embodiments of this disclosure, the extraction unit 304 may specifically be used for: The noise standard deviation is calculated based on the wavelet coefficients of multiple IMFs. Calculate the correlation strength r between the wavelet coefficients of the j-th layer and the IMF; Based on the noise standard deviation σ, the signal length M of the IMF, and the correlation strength r, the baseline threshold λ of the j-th layer is calculated. j ; Obtain multiple preset correlation intensity ranges and the scaling ratio corresponding to each correlation intensity range; Determine the interval to which the correlation intensity r belongs from multiple correlation intensity intervals, and adjust the benchmark threshold λ according to the scaling ratio corresponding to the interval. j Scaling is performed to obtain the adaptive threshold λ.

[0074] In some embodiments of this disclosure, the suppression unit 305 may specifically be used for: Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0075] The target millimeter-wave radar signal is reconstructed by linearly superimposing the IMFs after removing all interference signal components.

[0076] According to the embodiment of this disclosure, the device for suppressing cross-interference of millimeter-wave radar signals in underground coal mines acquires millimeter-wave radar signals from underground coal mines; using the maximum mutual information coefficient as the fitness function and the millimeter-wave radar signals as input, it optimizes the maximum regularization parameter maxAlpha of successive variational mode decomposition (SVMD) using an optimization search algorithm to obtain a target maxAlpha value; based on the target maxAlpha value, it decomposes the millimeter-wave radar signals using SVMD to obtain multiple intrinsic mode functions (IMFs); it performs wavelet thresholding on each IMF to extract the interference signal components in each IMF; it removes the corresponding interference signal components from each IMF, and reconstructs the target millimeter-wave radar signal with suppressed interference signals using the IMFs after removing the interference signal components. By dynamically optimizing the SVMD decomposition parameters using the maximum mutual information coefficient as the fitness function, accurate separation of target and interference signals at the modal level is achieved. Combined with inverse wavelet thresholding, it can effectively suppress interference signals in strong cross-interference environments while preserving complete target information to the maximum extent. This significantly improves the suppression effect of cross-interference of millimeter-wave radar signals in underground coal mines and reduces the false alarm rate. Moreover, the entire processing flow has high computational efficiency and fully meets the real-time processing requirements of underground mobile equipment.

[0077] Figure 4This is a block diagram illustrating an apparatus for suppressing cross-interference of millimeter-wave radar signals in underground coal mines, according to an exemplary embodiment. For example, apparatus 400 may be an electronic device, such as a mobile phone, computer, digital broadcasting terminal, messaging device, tablet device, personal digital assistant, etc.

[0078] Reference Figure 4 The device 400 may include one or more of the following components: a processing component 402, a memory 404, a power component 406, a multimedia component 408, an audio component 410, an input / output (I / O) interface 412, a sensor component 414, and a communication component 416.

[0079] Processing component 402 typically controls the overall operation of device 400, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 402 may include one or more processors 420 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 402 may include one or more modules to facilitate interaction between processing component 402 and other components. For example, processing component 402 may include a multimedia module to facilitate interaction between multimedia component 408 and processing component 402.

[0080] Memory 404 is configured to store various types of data to support the operation of device 400. Examples of this data include instructions for any application or method operating on device 400, contact data, phonebook data, messages, pictures, videos, etc. Memory 404 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0081] The power supply component 406 provides power to the various components of the device 400. The power supply component 406 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power to the device 400.

[0082] Multimedia component 408 includes a screen that provides an output interface between the device 400 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 408 includes a front-facing camera and / or a rear-facing camera. When the device 400 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0083] Audio component 410 is configured to output and / or input audio signals. For example, audio component 410 includes a microphone (MIC) configured to receive external audio signals when device 400 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 404 or transmitted via communication component 416. In some embodiments, audio component 410 also includes a speaker for outputting audio signals.

[0084] I / O interface 412 provides an interface between processing component 402 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0085] Sensor assembly 414 includes one or more sensors for providing status assessments of various aspects of device 400. For example, sensor assembly 414 may detect the on / off state of device 400, the relative positioning of components such as the display and keypad of device 400, changes in the position of device 400 or a component of device 400, the presence or absence of user contact with device 400, the orientation or acceleration / deceleration of device 400, and temperature changes of device 400. Sensor assembly 414 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 414 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 414 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0086] Communication component 416 is configured to facilitate wired or wireless communication between device 400 and other devices. Device 400 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 416 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 416 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0087] In an exemplary embodiment, the apparatus 400 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0088] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 404 including instructions, which can be executed by a processor 420 of the device 400 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0089] In an exemplary embodiment, a computer program product is also provided, including a computer program that implements the above-described method when executed by a processor 420 of the device 400.

[0090] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0091] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for suppressing cross-interference of millimeter-wave radar signals in underground coal mines, characterized in that, include: Acquire millimeter-wave radar signals from underground coal mines; Using the maximum mutual information coefficient as the fitness function and the millimeter-wave radar signal as input, the maximum regularization parameter maxAlpha of the successive variational mode decomposition SVMD is optimized using an optimization search algorithm to obtain the target maxAlpha value. Based on the target maxAlpha value, the millimeter-wave radar signal is decomposed using SVMD to obtain multiple intrinsic mode functions (IMFs). Wavelet thresholding is performed on each of the plurality of IMFs to extract the interference signal components in each IMF; The interference signal components corresponding to each IMF are removed, and the IMFs after removing the interference signal components are reconstructed to obtain the target millimeter-wave radar signal with suppressed interference signals.

2. The method for suppressing cross-interference of millimeter-wave radar signals in underground coal mines according to claim 1, characterized in that, The optimization of the maximum regularization parameter maxAlpha of successive variational mode decomposition (SVMD) using an optimization search algorithm to obtain the target maxAlpha value includes: The proportional-integral-differential search algorithm (PSA) is used to initialize the population within a preset maxAlpha search range; The population is updated iteratively, and the performance of each individual in the population is evaluated based on the function value of the fitness function in each iteration. When the current optimization round is equal to the preset round, the maxAlpha value corresponding to the maximum function value is obtained from the population corresponding to the current optimization round, and the target maxAlpha value is obtained.

3. The method for suppressing cross-interference of millimeter-wave radar signals in underground coal mines according to claim 2, characterized in that, The fitness function value can be calculated using the following steps: The millimeter-wave radar signal is decomposed using candidate maxAlpha values ​​to obtain multiple IMFs; Calculate the first mutual information coefficient (MIC) between adjacent IMFs among multiple IMFs, and calculate the sum of all first MICs corresponding to multiple IMFs to obtain the first sum value; the first MIC is the maximum mutual information coefficient. Calculate the second MIC between each IMF and the millimeter-wave radar signal in the plurality of IMFs, calculate the sum of all second MICs to obtain the second sum value; calculate the proportion of the first sum value in the second sum value to obtain the function value.

4. The method for suppressing cross-interference of millimeter-wave radar signals in underground coal mines according to claim 1, characterized in that, The step of performing wavelet thresholding on each of the plurality of IMFs to extract the interference signal components in each IMF includes: For each IMF, perform a wavelet transform on the IMF to obtain wavelet coefficients; Determine the adaptive threshold for the IMF; If the amplitude of the wavelet coefficients in the IMF is less than or equal to the adaptive threshold, the wavelet coefficients are set to zero. The processed wavelet coefficients are subjected to inverse wavelet transform to obtain the interference signal components of the IMF.

5. The method for suppressing cross-interference of millimeter-wave radar signals in underground coal mines according to claim 4, characterized in that, Determining the adaptive threshold of the IMF includes: The noise standard deviation is calculated based on the wavelet coefficients of each of the multiple IMFs. Calculate the correlation strength r between the wavelet coefficients of the j-th layer and the IMF; Based on the noise standard deviation σ, the signal length M of the IMF and the correlation strength r, the reference threshold λ of the jth layer is calculated j ; Obtain multiple preset correlation intensity ranges and the scaling ratio corresponding to each correlation intensity range; determining a belonging interval of the correlation strength r from multiple correlation strength intervals, scaling the reference threshold λ according to a scaling ratio corresponding to the belonging interval j scaling processing is performed to obtain an adaptive threshold λ.

6. The method for suppressing cross-interference of millimeter-wave radar signals in underground coal mines according to claim 1, characterized in that, The process of reconstructing the target millimeter-wave radar signal by removing interference signal components from the IMF to obtain the target millimeter-wave radar signal with suppressed interference includes: The target millimeter-wave radar signal is reconstructed by linearly superimposing the IMFs after removing all interference signal components.

7. A device for suppressing cross-interference of millimeter-wave radar signals in underground coal mines, characterized in that, include: Acquisition unit, used to acquire millimeter-wave radar signals from underground coal mines; The optimization unit is used to optimize the maximum regularization parameter maxAlpha of the successive variational mode decomposition SVMD using the maximum mutual information coefficient as the fitness function and the millimeter-wave radar signal as input, and to obtain the target maxAlpha value by using the optimization search algorithm. The decomposition unit is used to decompose the millimeter-wave radar signal based on the target maxAlpha value using SVMD to obtain multiple intrinsic mode functions (IMFs). An extraction unit is used to perform wavelet thresholding on each of the plurality of IMFs to extract the interference signal components in each IMF. The suppression unit is used to remove the corresponding interference signal components in each IMF, and then reconstruct the target millimeter-wave radar signal with the interference signal suppressed by using the IMF after removing the interference signal components.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, The computer program, when executed by a processor, implements the method as described in any one of claims 1 to 6.

Images