Signal processing method, system and device based on MIMO radar

Abstract

The application belongs to the field of signal processing, and particularly relates to a signal processing method, system and device based on MIMO radar, aiming to solve the problem of insufficient utilization of multi-channel signals and simple fusion strategy in the prior art, which limits the detection accuracy. The application comprises the following steps: obtaining target multi-channel echo signals and performing distance and angle processing, extracting phase changes by using phase unwrapping, performing two-stage weighted fusion based on the slope entropy of the receiving channel and the transmitting channel in sequence, obtaining micro-motion signals, performing variational mode decomposition and frequency band matching screening on the micro-motion signals, and reconstructing the final output signals. The application effectively overcomes the problem of insufficient utilization of multi-channel information, significantly improves the signal noise immunity and micro-motion feature fidelity through the double fusion strategy, and thus improves the detection and recognition accuracy of weak targets.

Description

Technical Field

[0001] This invention belongs to the field of signal processing, and specifically relates to a signal processing method, system and device based on MIMO radar. Background Technology

[0002] In the field of multichannel signal processing, how to effectively utilize information from multiple channels and fuse them to generate a higher-quality output signal is a common and crucial problem. For example, sensor arrays or multichannel systems are widely used for data acquisition in fields such as communication, acoustics, remote sensing, and biosignal detection.

[0003] Existing multi-channel signal fusion methods, such as maximum ratio combining, selective combining, or equal-gain combining, mostly rely on simple signal power or signal-to-noise ratio (SNR) estimation. However, for applications involving the acquisition of complex, non-stationary physiological signals with specific patterns (such as periodicity) (e.g., heartbeat, respiration), traditional power-based evaluation criteria often fail to accurately reflect signal quality. Channels with high SNR may contain interference such as motion artifacts, and their signal patterns may already be distorted; simply selecting or weighting them may actually reduce the fusion effect.

[0004] Another common practice is to simply add up and average the signals from all channels, but this ignores the differences in signal quality between channels. Low-quality or noisy channels will pollute the final synthesis result.

[0005] Therefore, existing technologies lack a universal method for accurately evaluating channel signal quality, particularly for characterizing the complexity and regularity of signal sequences, and for adaptive weighted fusion based on this criterion. A new signal processing algorithm is urgently needed to address these issues.

[0006] Based on this, the present invention proposes a signal processing method, system and device based on MIMO radar. Summary of the Invention

[0007] To address the aforementioned problems in existing technologies, namely, insufficient utilization of multi-channel signals and simplistic fusion strategies leading to limited detection accuracy, this invention provides a signal processing method based on MIMO radar, comprising:

[0008] The radar acquires multi-channel echo signals of the target under test, and performs range dimension processing and angle estimation on the echo signals to determine the position information of the target under test.

[0009] Based on the location information, the phase dewinding process is performed on the signals of each channel to extract the phase change signals of each channel;

[0010] Based on the slope entropy of the phase change signal of each receiving channel, the receiving channel signals corresponding to each transmitting channel are weighted and fused to generate a preliminary fused signal corresponding to each transmitting channel.

[0011] Calculate the slope entropy of the preliminary fused signal corresponding to each transmission channel, perform weighted fusion on the preliminary fused signal, and generate the final fused micro-motion signal;

[0012] The fused micro-motion signal is subjected to variational mode decomposition to obtain multiple intrinsic mode functions;

[0013] The mode functions whose frequencies match the preset frequency band are selected from multiple intrinsic mode functions, and the selected mode functions are then subjected to energy-weighted fusion to reconstruct the final output signal.

[0014] Furthermore, the echo signal is processed for range dimension and angle estimation to determine the position information of the target under test. The method is as follows:

[0015] A fast Fourier transform is performed on the fast time dimension of the echo signal to extract the distance information of the target and determine the distance cell where the target is located.

[0016] Based on the signal within the range cell, the angle information of the target under test is obtained through a spatial spectrum estimation algorithm;

[0017] The position information of the target to be measured is defined by the radial distance represented by the distance unit and the azimuth angle represented by the angle information.

[0018] Furthermore, based on the location information, phase dewinding processing is performed on the signals of each channel to extract the phase change signals of each channel. The method is as follows:

[0019] The guide vector corresponding to each receiving channel is calculated based on the angle information of the target to be measured, and the conjugate of the guide vector is multiplied by the signal vector of the corresponding receiving channel to achieve angle compensation for the signal of each channel.

[0020] The phase of the angle-compensated complex signal is taken, and the phase unwinding function is applied to process the acquired phase sequence to extract the phase change signal of each channel.

[0021] Furthermore, based on the slope entropy of the phase change signals of each receiving channel, the receiving channel signals corresponding to each transmitting channel are weighted and fused to generate a preliminary fused signal corresponding to each transmitting channel. The method is as follows:

[0022] For each transmit channel, calculate the slope entropy of the phase change signal of all corresponding receive channels;

[0023] The reciprocal of the slope entropy value of each receiving channel is used as the first fusion weight for each receiving channel.

[0024] The phase change signals of all receiving channels corresponding to the transmitting channel are weighted and summed using the first fusion weight to generate a preliminary fusion signal corresponding to the transmitting channel.

[0025] Furthermore, the slope entropy of the preliminary fused signal corresponding to each transmission channel is calculated. Based on the slope entropy, the preliminary fused signal is weighted and fused to generate the final fused micro-motion signal. The method is as follows:

[0026] Calculate the slope entropy of the preliminary fused signal corresponding to each transmission channel;

[0027] The reciprocal of the slope entropy value of each preliminary fusion signal is used as the second fusion weight for each transmission channel.

[0028] The preliminary fusion signals of all transmission channels are weighted and summed using the second fusion weight to generate the final fused micro-motion signal.

[0029] Furthermore, variational mode decomposition is performed on the fused micro-motion signal to obtain multiple intrinsic mode functions. The method is as follows:

[0030] The preset variational mode decomposition algorithm has a preset number of decomposition layers and a penalty factor. The fused micro-motion signal is used as the input signal, and the preset variational mode decomposition algorithm is used to adaptively decompose the input signal into multiple eigenmode functions with finite bandwidth.

[0031] Furthermore, mode functions whose frequencies match a preset frequency band are selected from multiple intrinsic mode functions, and energy-weighted fusion is performed on the selected mode functions to reconstruct the final output signal. The method is as follows:

[0032] Calculate the frequency of each intrinsic mode function; select intrinsic mode functions whose frequencies fall within a preset interval as candidate mode functions;

[0033] Calculate the energy value of each candidate mode function, and calculate the corresponding weight based on the energy of each mode function;

[0034] The modal functions are weighted and fused based on the weights to reconstruct the final output signal.

[0035] Furthermore, after extracting the phase change signals of each channel, and before performing weighted fusion based on the slope entropy of the phase change signals of each received channel, the method further includes:

[0036] The phase change signal of each receiving channel is smoothed and filtered to suppress phase noise.

[0037] In a second aspect, the present invention provides a signal processing system based on MIMO radar for implementing a signal processing method based on MIMO radar, the system comprising:

[0038] The target location acquisition module is configured to acquire multi-channel echo signals of the target under test through radar, and perform range dimension processing and angle estimation on the echo signals to determine the location information of the target under test;

[0039] The phase change calculation module is configured to perform phase unwrapping processing on the signals of each channel based on the position information, and extract the phase change signal of each channel;

[0040] The weighted fusion module is configured to perform weighted fusion on the received channel signals corresponding to each transmitted channel based on the slope entropy of the phase change signals of each received channel, and generate a preliminary fusion signal corresponding to each transmitted channel.

[0041] The micro-motion signal fusion module is configured to calculate the slope entropy of the preliminary fusion signal corresponding to each transmission channel, perform weighted fusion on the preliminary fusion signal, and generate the final fused micro-motion signal;

[0042] The decomposition module is configured to perform variational mode decomposition on the fused micro-motion signal to obtain multiple intrinsic mode functions.

[0043] The reconstruction module is configured to select mode functions whose frequencies match a preset frequency band from multiple intrinsic mode functions, and perform energy-weighted fusion on the selected mode functions to reconstruct the final output signal.

[0044] In a third aspect, the present invention provides an electronic device comprising:

[0045] At least one processor; and

[0046] A memory communicatively connected to at least one of the processors; wherein,

[0047] The memory stores instructions that can be executed by the processor to implement a signal processing method based on MIMO radar.

[0048] The beneficial effects of this invention are:

[0049] (1) This invention proposes a two-level weighted fusion architecture based on slope entropy. First, the received channel signals are fused in each transmit channel, which effectively eliminates interference within the channel and improves the quality of the transmit channel signals. Then, the preliminary fused signals of each transmit channel are fused in a second level, making full use of the signal complementarity between different transmit channels. This hierarchical fusion strategy refines effective information layer by layer, which significantly improves the accuracy and robustness of the final fused signal.

[0050] (2) In both stages of fusion, slope entropy is used as the adaptive evaluation criterion for signal quality. This overcomes the limitations of traditional methods based on power or signal-to-noise ratio in complex non-stationary signal scenarios. It can accurately characterize the regularity and complexity of the signal, thereby more reasonably allocating fusion weights, effectively suppressing noise and highlighting useful components.

[0051] (3) The two-level fused signal is adaptively decomposed in the frequency domain by variational mode decomposition (VMD) and the target frequency band components are accurately extracted by energy weighted fusion, which further enhances the quality of the output signal and provides a reliable foundation for subsequent high-precision analysis and identification.

[0052] (4) This invention fully leverages the multi-channel advantages of MIMO radar. Through a two-level fusion-decomposition processing chain, it achieves complete optimization from multi-channel raw signals to high-quality output signals, significantly improving the system's ability to detect and recover weak motion signals in non-ideal environments. Attached Figure Description

[0053] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0054] Figure 1 This is a flowchart illustrating the first embodiment of the present invention;

[0055] Figure 2 This is a schematic diagram comparing the results of signal processing using different methods in the third embodiment of the present invention;

[0056] Figure 3 This is a schematic diagram of the structure of a computer system used to implement the methods, systems, and devices of this application. Detailed Implementation

[0057] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0058] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0059] In a first aspect, the present invention provides a signal processing method based on MIMO radar, the method comprising:

[0060] Step S10: Acquire the multi-channel echo signal of the target under test through radar, and perform range dimension processing and angle estimation on the echo signal to determine the position information of the target under test;

[0061] Step S20: Perform phase unwrapping processing on the signals of each channel according to the position information, and extract the phase change signal of each channel;

[0062] Step S30: Based on the slope entropy of the phase change signal of each receiving channel, the receiving channel signals corresponding to each transmitting channel are weighted and fused to generate a preliminary fused signal corresponding to each transmitting channel;

[0063] Step S40: Calculate the slope entropy of the preliminary fusion signal corresponding to each transmission channel, perform weighted fusion on the preliminary fusion signal, and generate the final fused micro-motion signal;

[0064] Step S50: Perform variational mode decomposition on the fused micro-motion signal to obtain multiple intrinsic mode functions;

[0065] Step S60: Select mode functions whose frequencies match the preset frequency band from multiple intrinsic mode functions, and perform energy-weighted fusion on the selected mode functions to reconstruct the final output signal.

[0066] To more clearly illustrate the signal processing method based on MIMO radar of the present invention, the following description is provided in conjunction with... Figure 1 The steps in the embodiments of the present invention are described in detail below, including steps S10-S60.

[0067] Step S10: Acquire the multi-channel echo signal of the target under test through radar, and perform range dimension processing and angle estimation on the echo signal to determine the position information of the target under test;

[0068] In the specific implementation process, step S10 acquires the multi-channel echo signal of the target through a millimeter-wave MIMO radar system. This system uses a radar sensor operating at 80 GHz, with a 3-transmit, 4-receive configuration. The vital sign data acquisition parameters for this radar are configured as follows: radar slow-time dimension sampling rate. The Hz frequency is 1000 Hz, the fast-time sampling number is 256, and each frame uses one chirp. Radar data can be represented as a data cube. Let the original radar data be... Represented as:

[0069] ;

[0070] in, This refers to the number of fast-time sampling points (number of sampling points per frame). Rx is the number of receive antenna channels. Tx is the number of transmit antenna channels. This refers to the number of frames in slow time (one chirp per frame). It represents the complex field.

[0071] The echo signal is processed for range dimension and angle estimation to determine the position information of the target under test. The method is as follows:

[0072] A fast Fourier transform is performed on the fast time dimension of the echo signal to extract the distance information of the target and determine the distance cell where the target is located.

[0073] Based on the signal within the range cell, the angle information of the target under test is obtained through a spatial spectrum estimation algorithm;

[0074] The position information of the target to be measured is defined by the radial distance represented by the distance unit and the azimuth angle represented by the angle information.

[0075] In this embodiment, a 256-point FFT is performed on the fast time dimension to extract target distance information:

[0076] ;

[0077] Let the cell index of the distance to the chest wall be... , that is to say Distance The first The signals from all receiving channels corresponding to each transmitting antenna, for The angle value of the chest wall is obtained using the MUSIC algorithm. ,in, It represents a matrix consisting of the signals from all the receiving channels (Rx) corresponding to the tx-th transmitting antenna at a certain distance unit (such as the distance to the chest wall).

[0078] Step S10 achieves accurate estimation of the target's spatial position. By performing a range-dimensional Fast Fourier Transform on the radar echo signal, the radial range information of the target can be effectively extracted, accurately determining the range cell where the target is located. Based on this, a spatial spectrum estimation algorithm is used to estimate the angle of the signal within the range cell, obtaining the target's azimuth angle information. By combining the information from both the radial range and azimuth angle dimensions, two-dimensional precise positioning of the target's spatial position is finally achieved, providing accurate spatial pointing information for subsequent signal processing stages.

[0079] Step S20 involves performing phase unwrapping processing on the signals of each channel based on the position information, and extracting the phase change signals of each channel. Specifically, this includes:

[0080] Step S21: Calculate the steering vector corresponding to each receiving channel based on the angle information of the target to be measured, and perform a dot product operation between the conjugate of the steering vector and the signal vector of the corresponding receiving channel to achieve angle compensation for the signal of each channel.

[0081] Step S22: Perform phase extraction on the angle-compensated complex signal, and apply the phase unwinding function to process the acquired phase sequence to extract the phase change signal of each channel.

[0082] In the specific implementation of step S20, the angle value obtained in step S10 is first used as the basis. The corresponding array manifold of the radar antenna is calculated. The guide vector of each receiving channel This steering vector characterizes the phase relationship of electromagnetic waves arriving at each receiving antenna at a specific angle. Subsequently, the conjugate of the steering vector is denoted as... ,use right Angle compensation is performed to obtain motion signals on each receiving channel that are unaffected by the target angle:

[0083] ;

[0084] in, Represents the dot product operation. This indicates the received channel signal after angle compensation.

[0085] Subsequently, extract The phase signals are then unwrapped row by row to obtain signals that can be used for subsequent analysis:

[0086] ;

[0087] in, Here, "angle" represents the phase unwinding function, indicating the phase extraction operation. It represents the real number field. Each row represents the phase change signal corresponding to each receiving channel.

[0088] Considering that phase noise can severely affect detection accuracy, a smoothing technique is employed to reduce its impact. Specifically, the signal from each receiving channel is smoothed using a Savitzky-Golay polynomial smoothing filter. Here, the filter order is 3 and the filter length is 33. The result is denoted as follows: .

[0089] Step S30: Based on the slope entropy of the phase change signals of each receiving channel, the receiving channel signals corresponding to each transmitting channel are weighted and fused to generate a preliminary fused signal corresponding to each transmitting channel. The specific method includes:

[0090] Step S31: For each transmit channel, calculate the slope entropy of the phase change signal of all corresponding receive channels;

[0091] Step S32: The reciprocal of the slope entropy value of each receiving channel is used as the first fusion weight for each receiving channel.

[0092] Step S33: Use the first fusion weight to perform a weighted summation of the phase change signals of all receiving channels corresponding to the transmitting channel to generate a preliminary fusion signal corresponding to the transmitting channel.

[0093] In the specific implementation of step S31, for each transmit channel, the phase change signal time series of all corresponding receive channels are obtained. Based on a preset embedding dimension (usually 3) and time delay parameter (usually 1), the slope between adjacent sampling points in the phase change signal time series of each receive channel is calculated one by one, forming a slope sequence for each channel. Each slope sequence is symbolized, and the occurrence probability of a symbol pattern of a specific length is statistically analyzed. Finally, the slope entropy value of the signal for each receive channel is calculated using the Shannon entropy formula. ,Right now Each row corresponds to the slope entropy of each receiving channel.

[0094] In the specific implementation of step S32, the slope entropy values ​​of each receiving channel calculated in step S31 are used. ,pass The fusion weights for each receiving channel are calculated. This calculation first takes the reciprocal of the slope entropy value of each channel to obtain the initial weights. Then, all initial weights are normalized so that the sum of all weights is 1, thus obtaining the final normalized weights w used for weighted fusion. rx .

[0095] In the specific implementation of step S33, for the current transmitting channel, the phase change signals of all its receiving channels are combined into a two-dimensional signal matrix. The normalized fusion weight vector w obtained in step S32 is then used... rx and Perform a weighted summation operation, that is This generates a preliminary fusion signal that uniquely corresponds to the transmission channel. Φ tx Steps S31 to S33 are repeated for all transmission channels to generate multiple preliminary fusion signals corresponding to each transmission channel.

[0096] Step S40: Calculate the slope entropy of the preliminary fused signal corresponding to each transmission channel, and perform weighted fusion on the preliminary fused signal to generate the final fused micro-motion signal. The specific method is as follows:

[0097] Step S41: Calculate the slope entropy of the preliminary fused signal corresponding to each transmission channel;

[0098] Step S42: The reciprocal of the slope entropy value of each preliminary fused signal is used as the second fusion weight for each transmission channel.

[0099] Step S43: Use the second fusion weight to perform a weighted summation of the preliminary fusion signals of all transmission channels to generate the final fused micro-motion signal.

[0100] In the specific implementation of step S41, the preliminary fusion signal time series Φ corresponding to each transmission channel is obtained. tx ( n ),in n tx =1, 2, ..., N tx This provides an index for the transmission channels. For the initial fused signal of each transmission channel, based on the preset embedding dimension... m and time delay parameters τ Calculate the slope between adjacent sampling points in the time series of the signal to form a slope sequence. S tx The slope sequence is symbolized, converting continuous slope values ​​into a finite sequence of symbols. The probability of occurrence of symbol combinations of a preset length is calculated. Finally, the slope entropy value of the preliminary fused signal for each transmission channel is obtained according to the Shannon entropy formula, denoted as... .

[0101] In the specific implementation of step S42, the slope entropy value of the preliminary fused signal of each transmission channel calculated in step S41 is used. E tx ,pass The fusion weights for each transmission channel are calculated. This calculation first takes the reciprocal of the slope entropy value of each transmission channel to obtain the initial weights. Then, all initial weights are normalized so that the sum of all weights is 1, thus obtaining the final normalized fusion weight vector. .

[0102] In the specific implementation of step S43, the preliminary fusion signals from all transmission channels are combined into a two-dimensional signal matrix. Φ tx Each row corresponds to the initial fused signal of one transmission channel. The normalized fusion weight vector obtained in step S42 is then... and Φ tx Perform a weighted summation operation, that is This generates the final fused micro-motion signal Φ. This signal integrates the effective information from all transmission channels, resulting in a higher signal-to-noise ratio and higher reliability.

[0103] Step S50: Perform variational mode decomposition on the fused micro-motion signal to obtain multiple intrinsic mode functions. The specific method is as follows:

[0104] The preset variational mode decomposition algorithm has a preset number of decomposition layers and a penalty factor. The fused micro-motion signal is used as the input signal, and the preset variational mode decomposition algorithm is used to adaptively decompose the input signal into multiple eigenmode functions with finite bandwidth.

[0105] In the specific implementation of step S50, the key parameters of the variational mode decomposition (VMD) algorithm are first preset: the number of decomposition layers (i.e., the number of intrinsic mode functions (IMFs)) is set to 10, and the penalty factor is set to 8000. Then, the final fused micro-motion signal generated in step S40 is used as the input signal for the VMD algorithm. The preset variational mode decomposition algorithm is used to adaptively decompose the input signal, decomposing it into 10 finite-bandwidth intrinsic mode functions (IMFs) through an iterative optimization process. These mode functions have different center frequencies in the frequency domain, collectively and completely characterizing all the features of the original input signal.

[0106] Alternatively, step S50 can be implemented in another way as follows:

[0107] The fused micro-motion signal is subjected to variational mode decomposition to obtain multiple intrinsic mode functions, including the following steps:

[0108] Step S51: Preprocess the fused micro-motion signal, extract its frequency domain features, and calculate the signal-to-noise ratio level under the current environment;

[0109] Step S52: Determine the candidate range of the initial decomposition layer number for variational mode decomposition based on the frequency domain characteristics, and adaptively set the initial value range of the penalty factor according to the signal-to-noise ratio level.

[0110] Step S53: Construct an optimization function with modal component bandwidth as the central objective, and use an intelligent optimization algorithm to perform parallel search within the initial candidate range of decomposition layers and the range of penalty factor values, and iteratively calculate the optimal combination of decomposition layers and penalty factor that makes the optimization function reach a minimum value.

[0111] Step S54: Using the optimal combination as the operating parameters for variational mode decomposition, adaptive decomposition is performed on the fused micro-motion signal to obtain multiple intrinsic mode functions with significant center frequency differences.

[0112] In this embodiment, during the variational mode decomposition of the fused micro-motion signal to obtain multiple intrinsic mode functions, the micro-motion signal is first preprocessed, specifically including zero-mean normalization and smoothing denoising to eliminate trend terms and high-frequency interference. Subsequently, the amplitude spectrum of the signal is obtained through fast Fourier transform, and the signal bandwidth is initially determined based on the main lobe width and side lobe attenuation characteristics of the amplitude spectrum. The signal-to-noise ratio level of the current environment is estimated using the ratio of signal power to noise floor power.

[0113] Based on the frequency domain analysis results above, an initial candidate range for the number of decomposition layers in variational mode decomposition is set. Specifically, the lower limit of the number of decomposition layers is determined by the number of significant harmonic components in the signal, while the upper limit is obtained by rounding up the ratio of signal bandwidth to sampling rate. The initial range of the penalty factor is adaptively adjusted according to the signal-to-noise ratio (SNR). The higher the SNR, the larger the range of penalty factor values; when the SNR is low, the range is appropriately narrowed and tilted towards smaller values ​​to avoid mode aliasing or over-decomposition.

[0114] Furthermore, an objective function for parameter optimization is constructed. This function uses the sum of the bandwidths of each modal component as its core metric, and its expression is:

[0115] ;

[0116] in, K Let α be the number of decomposition layers and α be the penalty factor. u k ( t ) is the first k There are several modal functions. A particle swarm optimization algorithm is used to perform a parallel search within a set parameter range, iteratively updating particle positions and velocities to find a way to optimize the objective function. J ( K , α The parameter combination that achieves the minimum value yields the optimal number of decomposition layers.K opt With penalty factor α opt .

[0117] Finally, the optimal parameter combination ( K opt , α opt As the actual operating parameter for variational mode decomposition, the micro-motion signal is adaptively decomposed. This process decomposes the original signal into multiple essential mode functions with finite bandwidth and distinct center frequencies by iteratively solving the constrained variational problem, laying the foundation for subsequent frequency band selection and signal reconstruction.

[0118] Specifically, the process of determining the initial candidate range for the number of decomposition layers in variational mode decomposition based on the frequency domain characteristics, and adaptively setting the initial value range of the penalty factor according to the signal-to-noise ratio level, includes:

[0119] Based on the frequency domain characteristics, the ratio of the effective bandwidth of the signal to the sampling frequency is rounded down as the lower limit of the number of decomposition layers, and twice the ratio is rounded up as the upper limit of the number of decomposition layers, thereby determining the candidate range of the number of decomposition layers.

[0120] Set a preset range of base values ​​for the penalty factor. ;

[0121] Obtain the numerical value of the signal-to-noise ratio (SNR) level, and calculate the intermediate value range of the penalty factor according to the first mapping relationship, which is:

[0122] ;

[0123] in, and These are the preset low signal-to-noise ratio and high signal-to-noise ratio thresholds, respectively, and k1 and k2 are scaling factors preset based on experience;

[0124] Calculate the spectral entropy value of the micro-motion signal spectrum, and make a final adjustment to the intermediate value interval according to the second mapping relationship to obtain the initial value interval of the penalty factor. The second mapping relationship is:

[0125] Where H is the calculated spectral entropy value. and These are the preset low spectral entropy and high spectral entropy thresholds, respectively. and These are the lower and upper limits of the intermediate value range, respectively, and k3 and k4 are entropy adjustment coefficients preset based on experience.

[0126] In the specific implementation process, the initial decomposition layer candidate range of the variational mode decomposition is determined based on the frequency domain characteristics. Simultaneously, the initial value range of the penalty factor is adaptively set according to the signal-to-noise ratio level. The specific implementation method is as follows: First, frequency domain analysis is performed on the preprocessed micro-motion signal. The effective bandwidth Bw of the signal is estimated by calculating the signal power spectral density and identifying its -3dB drop point. The normalized bandwidth ratio R is obtained by dividing the effective bandwidth Bw by the system sampling frequency Fs. bw =Bw / Fs. R bw Round down to get the integer K low , as the lower bound of the decomposition level; R bw Multiply by 2 and round up to get the integer K. high , as the upper limit of the decomposition level, thus determining the candidate range of the decomposition level as [K]. low K high ].

[0127] Define a preset, empirical range of base values ​​for the penalty factor, for example, [α]. min α max [100, 10000]. Obtain the signal-to-noise ratio (SNR) value (in dB) calculated through power. Calculate the intermediate value range of the penalty factor based on the first mapping formula;

[0128] For example, SNR can be set. low =5dB, SNR high =25dB; k1 and k2 are preset scaling factors used to control the influence of the signal-to-noise ratio on the interval boundaries. For example, k1=50 and k2=200 can be chosen. This formula calculates a preliminary intermediate value range based on the adjusted signal-to-noise ratio. .

[0129] Subsequently, the spectral entropy value H of the micro-motion signal spectrum is calculated to quantify its flatness. The formula for calculating the spectral entropy H is H = -∑(P i ×log2(P i )), where P i This is the normalized power spectral density. The intermediate value range is then adjusted according to the second mapping equation to obtain the initial value range of the penalty factor.

[0130] For example, H can be set. low =0.3, H high =0.8; k3 and k4 are preset entropy adjustment coefficients used to control the extent to which the spectral entropy expands or contracts the interval range. For example, k3 = 500 and k4 = 1000 can be chosen. Through this calculation, the adaptive initial value range of the penalty factor for the variational mode decomposition algorithm is finally determined.

[0131] Step S60: Select mode functions whose frequencies match a preset frequency band from multiple intrinsic mode functions, and perform energy-weighted fusion on the selected mode functions to reconstruct the final output signal. The specific method is as follows:

[0132] Step S61: Calculate the frequency of each intrinsic mode function; select intrinsic mode functions whose frequencies are within a preset interval as candidate mode functions;

[0133] Step S62: Calculate the energy value of each candidate mode function, and calculate the corresponding weight based on the energy of each mode function;

[0134] Step S63: Based on the weights, the modal functions are weighted and fused to reconstruct the final output signal.

[0135] In the specific implementation of step S61, the center frequency of each intrinsic mode function (IMF) obtained after variational mode decomposition is first calculated using Hilbert transform or zero-crossing detection method. Then, the calculated IMF frequencies are compared with a preset signal frequency band range (0.8 Hz to 2.5 Hz, corresponding to 48-150 BPM), and all IMF components whose frequencies fall within this range are selected and marked as candidate mode functions for subsequent fusion processing.

[0136] In the specific implementation of step S62, the energy value of each candidate mode function is calculated to evaluate its signal strength. Specifically, the sum of squares of all data points for each candidate mode function is calculated, and this sum of squares is taken as the energy value of that mode function. Subsequently, the corresponding fusion weight is calculated based on the energy value of each mode function. The specific method is as follows: the energy value of each candidate mode function is divided by the sum of the energy values ​​of all candidate mode functions to obtain the normalized weight coefficient of each mode function, ensuring that the sum of all weight coefficients is 1.

[0137] In the specific implementation of step S63, based on the normalized weight coefficients calculated in step S62, all candidate mode functions are weighted and fused. Each candidate mode function is multiplied by its corresponding weight coefficient to obtain a weighted mode function component. Then, all weighted mode function components are superimposed to reconstruct the final output signal. This output signal highlights the signal components with strong energy within the relevant frequency band and effectively suppresses the interference of noise and irrelevant components.

[0138] Although the steps in the above embodiments are described in the above order, those skilled in the art will understand that in order to achieve the effect of this embodiment, different steps do not need to be executed in such an order. They can be executed simultaneously (in parallel) or in a reverse order. These simple variations are all within the protection scope of this invention.

[0139] A second embodiment of the present invention provides a signal processing system based on MIMO radar, used to implement a signal processing method based on MIMO radar, the system comprising:

[0140] The target location acquisition module is configured to acquire multi-channel echo signals of the target under test through radar, and perform range dimension processing and angle estimation on the echo signals to determine the location information of the target under test;

[0141] The phase change calculation module is configured to perform phase unwrapping processing on the signals of each channel based on the position information, and extract the phase change signal of each channel;

[0142] The weighted fusion module is configured to perform weighted fusion on the received channel signals corresponding to each transmitted channel based on the slope entropy of the phase change signals of each received channel, and generate a preliminary fusion signal corresponding to each transmitted channel.

[0143] The micro-motion signal fusion module is configured to calculate the slope entropy of the preliminary fusion signal corresponding to each transmission channel, perform weighted fusion on the preliminary fusion signal, and generate the final fused micro-motion signal;

[0144] The decomposition module is configured to perform variational mode decomposition on the fused micro-motion signal to obtain multiple intrinsic mode functions.

[0145] The reconstruction module is configured to select mode functions whose frequencies match a preset frequency band from multiple intrinsic mode functions, and perform energy-weighted fusion on the selected mode functions to reconstruct the final output signal.

[0146] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the system described above can be found in the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0147] It should be noted that the signal processing system based on MIMO radar provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be further decomposed or combined. For example, the modules in the above embodiments can be merged into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the various modules or steps and are not considered as an improper limitation of the present invention.

[0148] The third embodiment of the present invention proposes a non-contact heart rate detection method based on MIMO radar to solve the problems of low heart rate detection accuracy and poor anti-interference ability in the existing non-contact physiological signal detection technology, which are caused by the fact that traditional signal quality assessment methods are not applicable to weak physiological signal characteristics, insufficient utilization of multi-channel information, and lack of targeted signal processing procedures.

[0149] The method is based on a MIMO radar-based signal processing method described in the first embodiment, and includes the following steps: Step A10, acquiring multi-channel raw signals for the chest cavity region through radar, and performing range dimension processing and angle estimation on the raw signals to determine the position information of the chest wall target;

[0150] Step A20: Perform phase unwrapping processing on the signals of each channel based on the position information, and extract the phase change signal of each channel;

[0151] Step A30: Based on the slope entropy of the phase change signal of each receiving channel, the receiving channel signals corresponding to each transmitting channel are weighted and fused to generate a preliminary fused signal corresponding to each transmitting channel;

[0152] Step A40: Calculate the slope entropy of the preliminary fusion signal corresponding to each transmission channel, perform weighted fusion on the preliminary fusion signal, and generate the final fused chest wall micromotion signal;

[0153] Step A50: Perform variational mode decomposition on the fused chest wall micromotion signal to obtain multiple intrinsic mode functions;

[0154] Step A60: Select a mode function whose frequency matches the range of human heart rate from multiple intrinsic mode functions, and perform energy-weighted fusion on the selected mode function to reconstruct the final output signal.

[0155] This embodiment, through the aforementioned two-level slope entropy weighted fusion mechanism, can adaptively evaluate the signal quality of each channel and allocate fusion weights, effectively suppressing the influence of low-quality channels containing interference such as motion artifacts, fully utilizing the multi-channel advantages of MIMO radar, and significantly improving the accuracy and robustness of weak heart rate signal extraction in complex environments.

[0156] This embodiment uses an IWR1843 millimeter-wave radar sensor for data acquisition. The radar system is configured with a 3-transmit, 4-receive antenna array, generating 12 virtual channels. Specific radar parameter settings are as follows: 256 sampling points in the fast time dimension, a fast time sampling rate of 5MHz, a slow time dimension sampling frequency of 1000Hz, and a signal acquisition duration of 60 seconds. The frequency modulation of the frequency-modulated continuous wave (FMCW) chirp signal transmitted by the radar is set to 66.626MHz / μs, and the signal bandwidth is 3.997GHz. During the test, the subject sits approximately 50 cm directly in front of the radar, maintaining normal breathing. The radar beam is aimed at the subject's chest area to collect the micro-motion signals caused by the heartbeat.

[0157] After data acquisition, the proposed method is used for processing: first, the original echo signal is subjected to range-dimensional FFT and angle estimation (MUSIC algorithm) to accurately locate the chest wall target; then, phase unwrapping is performed to extract the phase signal of each channel; next, a two-level fusion algorithm based on slope entropy (receive channel-level fusion and transmit channel-level fusion) is used to perform weighted fusion of the multi-channel signals; finally, the heart rate signal is extracted through variational mode decomposition (VMD) and energy-weighted fusion.

[0158] To verify the effectiveness of this invention, the slope entropy fusion method proposed in this embodiment is compared with two traditional methods. The comparison results are as follows: Figure 2 As shown, the maximum signal-to-noise ratio method (selecting the single channel signal with the highest signal-to-noise ratio from 12 channels for processing) and the summation fusion method (directly adding and averaging the 12 channel signals) are used. The synchronously acquired electrocardiogram (ECG) signal is used as the reference true value, and the average absolute error percentage (AAEP) is used as the evaluation index. Its calculation formula is as follows:

[0159] ;

[0160] in, h est ( n () represents the estimated heart rate for the nth time. h ref ( n () is the ECG reference value.

[0161] The comparative experimental results are shown in Table 1. The slope entropy fusion method proposed in this invention achieved the lowest AAEP value (7.48%), which is significantly better than the maximum signal-to-noise ratio method (8.97%) and the additive fusion method (8.83%). This proves that the method can effectively reduce heart rate detection error and improve the accuracy and reliability of heart rate detection in real environment.

[0162] Table 1:

[0163] method AAEP Slope Entropy Fusion Method 7.48% Maximum signal-to-noise ratio method 8.97% Additive fusion method 8.83%

[0164] In this embodiment, the heart rate value is calculated and output based on the final output signal. Those skilled in the art can clearly understand that, for the sake of convenience and brevity, the specific working process and related descriptions in the third embodiment described above can be referred to the corresponding process in the method of the first embodiment mentioned above, and will not be repeated here.

[0165] An electronic device according to a fourth embodiment of the present invention includes:

[0166] At least one processor; and

[0167] A memory communicatively connected to at least one of the processors; wherein,

[0168] The memory stores instructions that can be executed by the processor to implement the aforementioned signal processing method based on MIMO radar.

[0169] A computer-readable storage medium according to a fifth embodiment of the present invention stores computer instructions, which are executed by the computer to implement the above-described signal processing method based on MIMO radar.

[0170] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the storage device and processing device described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0171] Those skilled in the art will recognize that the modules and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. The programs corresponding to the software modules and method steps can be placed in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the art. To clearly illustrate the interchangeability of electronic hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the invention.

[0172] The following is for reference. Figure 3 It shows a schematic diagram of the structure of a computer system for implementing the methods, systems, and devices of this application. Figure 3The server shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0173] like Figure 3 As shown, the computer system includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes based on programs stored in Read Only Memory (ROM) 302 or programs loaded from storage section 308 into Random Access Memory (RAM) 303. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An Input / Output (I / O) interface 305 is also connected to the bus 304.

[0174] The following components are connected to I / O interface 305: an input section 306 including a keyboard, mouse, etc.; an output section 307 including a cathode ray tube (CRT), liquid crystal display (LCD), and speakers, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card and a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0175] Specifically, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the functions defined in the methods of this application. It should be noted that the computer-readable medium described above in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0176] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0177] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0178] The terms “first”, “second”, etc., are used to distinguish similar objects, not to describe or indicate a specific order or sequence.

[0179] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent in such process, method, article, or apparatus / device.

[0180] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method of signal processing based on MIMO radar, characterized in that, The method comprises: acquiring a multi-channel echo signal of a target to be detected by radar, and performing distance dimension processing and angle estimation on the echo signal to determine position information of the target to be detected: performing fast Fourier transform on the fast time dimension of the echo signal to extract distance information of the target to be detected and determine a distance unit in which the target to be detected is located; acquiring angle information of the target to be detected by a spatial spectrum estimation algorithm based on signals in the distance unit; the position information of the target to be detected is jointly defined by a radial distance represented by the distance unit and an azimuth angle represented by the angle information; performing phase unwrapping processing on each channel signal according to the position information to extract a phase change signal of each channel; based on the slope entropy of the phase change signal of each receiving channel, the receiving channel signals corresponding to each transmitting channel are weighted and fused to generate a preliminary fusion signal corresponding to each transmitting channel; the slope entropy of the preliminary fusion signal corresponding to each transmitting channel is calculated, and the preliminary fusion signals are weighted and fused to generate a final fused micro-motion signal; performing variational mode decomposition on the fused micro-motion signal to obtain a plurality of intrinsic mode functions: the decomposition layer number and the penalty factor of the preset variational mode decomposition algorithm are preset, the fused micro-motion signal is taken as an input signal, and the input signal is adaptively decomposed by using the preset variational mode decomposition algorithm to decompose the input signal into a plurality of intrinsic mode functions with limited bandwidth; from the plurality of intrinsic mode functions, a mode function with a frequency matched with a preset frequency band range is selected, and the selected mode function is energy-weighted and fused to reconstruct a final output signal.

2. The signal processing method based on MIMO radar according to claim 1, characterized in that, According to the position information, the phase unwrapping processing is performed on each channel signal to extract the phase change signal of each channel, and the method is as follows: the steering vector corresponding to each receiving channel is calculated according to the angle information of the target to be detected, and the conjugate of the steering vector is multiplied by the signal vector of the corresponding receiving channel to realize angle compensation of each channel signal; the phase operation is performed on the complex signal after the angle compensation, and the phase unwrapping function is applied to process the obtained phase sequence to extract the phase change signal of each channel.

3. The signal processing method based on MIMO radar according to claim 1, characterized in that, Based on the slope entropy of the phase change signal of each receiving channel, the receiving channel signals corresponding to each transmitting channel are weighted and fused to generate a preliminary fusion signal corresponding to each transmitting channel, and the method is as follows: for each transmitting channel, the slope entropy of the phase change signal of all receiving channels corresponding to the transmitting channel is calculated; the reciprocal of the slope entropy value of each receiving channel is taken as a first fusion weight generated by each receiving channel; the phase change signals of all receiving channels corresponding to the transmitting channel are weighted and summed by using the first fusion weight to generate a preliminary fusion signal corresponding to the transmitting channel.

4. The signal processing method based on MIMO radar according to claim 1, characterized in that, The slope entropy of the preliminary fusion signal corresponding to each transmitting channel is calculated, and the preliminary fusion signals are weighted and fused based on the slope entropy to generate a final fused micro-motion signal, and the method is as follows: the slope entropy of the preliminary fusion signal corresponding to each transmitting channel is calculated; the reciprocal of the slope entropy value of each preliminary fusion signal is taken as a second fusion weight generated by each transmitting channel; The preliminary fused signals of all transmitting channels are weighted and summed using the second fusion weight to generate a final fused micro-motion signal.

5. The method of claim 1, wherein, The method comprises the following steps: calculating the frequency of each eigenmode function; selecting the eigenmode functions with frequencies within a preset interval as candidate eigenmode functions; calculating the energy value of each candidate eigenmode function and calculating the corresponding weight according to the energy of each eigenmode function; weighting and fusing each eigenmode function based on the weight to reconstruct the final output signal.

6. The signal processing method based on MIMO radar according to claim 1, characterized in that, After extracting the phase change signals of each channel, and before weighting and fusing based on the slope entropy of the phase change signals of each receiving channel, the method further comprises: performing smoothing filtering processing on the phase change signals of each receiving channel to suppress phase noise.

7. A signal processing system based on MIMO radar for implementing a signal processing method based on MIMO radar according to any one of claims 1 to 6, characterized in that The system comprises: a target position acquisition module configured to acquire multi-channel echo signals of a target to be measured by a radar, and perform distance dimension processing and angle estimation on the echo signals to determine the position information of the target to be measured; a phase change calculation module configured to perform phase unwrapping processing on each channel signal according to the position information to extract the phase change signals of each channel; a weight fusion module configured to weight and fuse the receiving channel signals corresponding to each transmitting channel based on the slope entropy of the phase change signals of each receiving channel to generate preliminary fused signals corresponding to each transmitting channel; a micro-motion signal fusion module configured to calculate the slope entropy of the preliminary fused signals corresponding to each transmitting channel, weight and fuse the preliminary fused signals to generate a final fused micro-motion signal; a decomposition module configured to perform variational mode decomposition on the fused micro-motion signal to obtain a plurality of eigenmode functions; a reconstruction module configured to select eigenmode functions with frequencies matching a preset frequency band range from the plurality of eigenmode functions, and weight and fuse the selected eigenmode functions to reconstruct a final output signal.

8. An electronic device, comprising: comprise: at least one processor; and a memory connected in communication with the at least one processor; wherein the memory stores instructions executable by the processor, the instructions being configured to be executed by the processor to implement the signal processing method based on the MIMO radar according to any one of claims 1-6.

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