Real-time parameter resolving method for semi-blind area Doppler micro-motion signal

Abstract

The invention discloses a real-time parameter resolving method for a semi-blind area Doppler micro-motion signal, and relates to the technical field of information processing. Acquiring a Doppler micro-motion signal to be processed, and determining a non-blind area signal in the Doppler micro-motion signal; constructing a forward autoregression equation and a backward autoregression equation according to the autoregression model of the univariate time series data and the non-blind area signal; solving the forward autoregression equation and the backward autoregression equation to obtain an autoregression model coefficient; the autoregression model coefficient is substituted into an autoregression model, recursion is carried out on the autoregression model according to a time sequence, and a full-period signal corresponding to the Doppler micro-motion signal is restored; and analyzing the slow change random process of the full-period signal to obtain the characteristic parameter of the full-period signal. According to the method, the real-time performance of parameter calculation of the semi-blind area Doppler micro-motion signal can be improved.

Description

Technical Field

[0001] The invention relates to the technical field of information processing, and in particular to a real-time parameter solving method for Doppler micro-motion signals in a semi-blind zone. Background Art

[0002] The parameter solution of Doppler micro-motion signals in the semi-blind zone is an important research direction in the field of information processing, which involves how to recover the original signal from the missing data and perform parameter solution to ensure the accurate recovery of the signal, which can significantly improve the performance and reliability of the system. It is widely used in radar detection, laser detection, non-contact measurement, satellite communication and sensor network systems. Existing technologies mainly focus on several key methods. For example, Kalman filtering has become a common tool due to its high efficiency in state estimation and signal tracking, especially for processing dynamic systems with noise; compressed sensing theory has also been applied in this field, allowing the reconstruction of signals at low sampling rates, thereby optimizing storage and processing resources.

[0003] Kalman filtering is sensitive to initial conditions and model parameters, so it may face greater uncertainty in practical applications, and it performs poorly in some nonlinear or strong noise environments. Although compressed sensing can effectively reduce the amount of data collection and improve signal recovery efficiency, the algorithm complexity of compressed sensing is relatively high, which may cause calculation delays, which is particularly critical for real-time solution requirements.

[0004] Therefore, there is an urgent need for a method that can improve the real-time performance of parameter solution of Doppler micro-motion signals in the semi-blind zone. Summary of the invention

[0005] Based on this, it is necessary to provide a real-time parameter solution method for Doppler micro-motion signals in a semi-blind zone in order to solve the above technical problems. This method can improve the real-time performance of parameter solution for Doppler micro-motion signals in a semi-blind zone.

[0006] The present invention adopts the following technical solutions: The present invention provides a real-time parameter calculation method for Doppler micro-motion signals in a semi-blind zone, comprising: Acquire the Doppler micro-motion signal to be processed, and determine the non-blind zone signal in the Doppler micro-motion signal; According to the autoregressive model of univariate time series data and the non-blind zone signal, the forward autoregressive equation and the backward autoregressive equation are constructed; Solve the forward autoregressive equation and the backward autoregressive equation to obtain the autoregressive model coefficients; Substitute the autoregressive model coefficients into the autoregressive model, and recursively perform the autoregressive model according to the time series to restore the full-period signal corresponding to the Doppler micromotion signal; The slowly varying random process of the full-periodic signal is analyzed to obtain the characteristic parameters of the full-periodic signal.

[0007] Preferably, determining a non-blind zone signal in a Doppler micro-motion signal includes: According to the preset sampling rules, the blind area signal and the non-blind area signal in the Doppler micro-motion signal are determined.

[0008] Preferably, according to the autoregressive model of the univariate time series data and the non-blind zone signal, a forward autoregressive equation and a backward autoregressive equation are constructed, including: Determine the order of the autoregressive model according to the number of sampling points in the non-blind zone signal; According to the order of the autoregressive model and the non-blind zone signal, the variables of the autoregressive model are assigned to obtain the forward autoregressive equation and the backward autoregressive equation.

[0009] Preferably, the autoregressive model is: ; in, express t The signal of the moment, express n The historical signal before the moment, represents the coefficient of the autoregressive model; The forward autoregressive equation is: ; The backward autoregression equation is: ; in, n represents the order of the autoregressive model, N Indicates the number of sampling points in the non-blind zone signal.

[0010] Preferably, the forward autoregressive equation and the backward autoregressive equation are solved to obtain the autoregressive model coefficients, including: Get the augmented matrices of the forward autoregressive equation and the backward autoregressive equation; Decompose the augmented matrix to obtain the autoregressive model coefficients.

[0011] Preferably, analyzing the slow-changing random process of the full-period signal to obtain characteristic parameters of the full-period signal includes: Perform fast Fourier transform on the full-periodic signal to obtain the frequency domain feature quantity of the full-periodic signal; The slow-varying random process in the full-periodic signal is analyzed according to the frequency-domain characteristic quantity and Rice expression, and the time-domain data of the slow-varying random process of the full-periodic signal is obtained; Based on the time domain data, the characteristic parameters of the full-period signal are determined.

[0012] The present invention provides a real-time parameter solving device for Doppler micro-motion signal in a semi-blind zone, comprising: An acquisition module is used to acquire the Doppler micro-motion signal to be processed and determine the non-blind zone signal in the Doppler micro-motion signal; A construction module, used to construct a forward autoregressive equation and a backward autoregressive equation according to the autoregressive model of the univariate time series data and the non-blind zone signal; A solution module, used for solving the forward autoregressive equation and the backward autoregressive equation to obtain the autoregressive model coefficients; A restoration module is used to substitute the autoregressive model coefficients into the autoregressive model, and recursively perform the autoregressive model according to the time series to restore the full-period signal corresponding to the Doppler micro-motion signal; The analysis module is used to analyze the slow-changing random process of the full-period signal and obtain the characteristic parameters of the full-period signal.

[0013] The present invention provides a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the real-time parameter solution method of the semi-blind zone Doppler micro-motion signal is implemented.

[0014] The present invention provides a computer device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the real-time parameter solution method of the semi-blind zone Doppler micro-motion signal is implemented.

[0015] At least one of the above technical solutions adopted by the present invention can achieve the following beneficial effects: In the present invention, by constructing a forward autoregressive equation and a backward autoregressive equation and solving the autoregressive model coefficients, it is possible to accurately restore and predict the blind spot signal, thereby improving the accuracy of the full-cycle signal. Then, on the basis of the predicted full-cycle signal, the slowly varying random process of the full-cycle signal is analyzed to obtain the characteristic parameters of the full-cycle signal, which is equivalent to directly using the collected Doppler micro-motion signal to solve the parameters without using historical data, thereby improving the real-time performance of the parameter solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 A schematic flow chart of a method for calculating real-time parameters of a semi-blind zone Doppler micro-motion signal provided by the present invention; Figure 2 A schematic diagram of a Doppler micro-motion signal waveform to be processed provided by the present invention; Figure 3 A waveform diagram of a sampled signal provided by the present invention; Figure 4 A schematic diagram of a QR decomposition result provided by the present invention; Figure 5 A schematic diagram of a comparison between a full-period signal waveform restored and predicted using an autoregressive model and an actual waveform provided by the present invention; Figure 6 A schematic diagram of the error between an actual waveform and a predicted full-cycle signal waveform provided by the present invention; Figure 7 A spectrum diagram for predicting a full-period signal provided by the present invention; Figure 8 A schematic diagram of a real-time parameter solving device for Doppler micro-motion signals in a semi-blind zone provided by the present invention; Fig. 9 A schematic diagram of a computer device for implementing a real-time parameter solution method for Doppler micro-motion signals in a semi-blind zone provided by the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0018] The technical solutions provided by various embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0019] Figure 1 The figure is a flow chart of a method for calculating the real-time parameters of a semi-blind zone Doppler micro-motion signal in the present invention, which specifically includes the following steps: S101, obtaining a Doppler micro-motion signal to be processed, and determining a non-blind zone signal in the Doppler micro-motion signal.

[0020] Among them, the Doppler micro-motion signal can be divided into a narrowband Doppler micro-motion signal or a broadband Doppler micro-motion signal. The present invention is applicable to narrowband Doppler micro-motion signals. Narrowband Doppler micro-motion signals can be expressed in mathematical theory as: (1) in, is the narrowband Doppler micro-motion signal, t Indicates time, , They are all slow-changing random processes. is the center frequency, usually representing the carrier frequency.

[0021] Narrowband Doppler micro-motion signals are common in electronic systems such as communications and radars. In engineering systems, they can be expressed as: (2) in, is the narrowband Doppler micro-motion signal, is the carrier angular frequency, which can be regarded as a constant value within the solution period. , , For time t The function can be regarded as a linear change in the solution cycle. , , =10, =0.05. The present invention will accurately restore the signal based on limited signal data. Signal and calculate parameters , and .

[0022] Acquiring the Doppler micro-motion signal to be processed includes acquiring the Doppler micro-motion signal to be processed output by the sensor, such as Figure 2 As shown, Figure 2 is a schematic diagram of a Doppler micro-motion signal waveform, the horizontal axis is time ( t / s ), the vertical axis is the signal amplitude (V). However, in order to improve the channel utilization efficiency in engineering practice, it is often impossible to obtain a full-cycle sampling signal. Therefore, the blind spot position can be determined according to specific sampling rules to determine the non-blind spot signal and the blind spot signal.

[0023] Specifically, determining the non-blind zone signal in the Doppler micro-motion signal includes: determining the blind zone signal and the non-blind zone signal in the Doppler micro-motion signal according to a preset sampling rule.

[0024] For example, sampling is performed through an AD converter, and the sampling rules are as follows: the sampling rate is 4MHz, the interval period is 0.5ms, and the sampling period is between 0 and 1 / 8. x The non-blind zone signal is only 1 / 8 of the total signal, and the rest of the time is the blind zone. The waveform after sampling is as follows: Figure 3 As shown, the horizontal axis is time ( t / s ), the vertical axis is the signal amplitude (V).

[0025] Therefore, the blind zone signal and the non-blind zone signal in the Doppler micro-motion signal can be determined according to the sampling interval period.

[0026] S102, constructing a forward autoregressive equation and a backward autoregressive equation according to the autoregressive model of the univariate time series data and the non-blind zone signal.

[0027] In an exemplary embodiment, a forward autoregressive equation and a backward autoregressive equation are constructed based on the autoregressive model of univariate time series data and the non-blind zone signal, including: determining the order of the autoregressive model based on the number of sampling points in the non-blind zone signal; assigning variables to the autoregressive model based on the autoregressive model order and the non-blind zone signal to obtain the forward autoregressive equation and the backward autoregressive equation.

[0028] The autoregressive model is: (3) in, express t The signal of the moment, express n The historical signal before the moment, Indicates i The autoregressive model coefficients.

[0029] The order of the autoregressive model is n For example, the autoregressive model coefficient is , in order to solve the autoregressive model coefficients , using non-blind zone signals , build n The forward autoregressive equation and the backward autoregressive equation of the order are optionally determined based on part of the non-blind zone signal. For example, the number of sampling points in the non-blind zone signal is N , autoregressive model order n The selection can be N / 3~ N / 2, N is the number of sampling points within a 1 / 8 interval period. Optionally, N is 250, n It can be 100.

[0030] The forward autoregressive equation is: (4) Recorded as ,in is a column vector of autoregressive model coefficients.

[0031] Add the backward autoregressive equation, and the backward autoregressive equation is: (5) Among them, it is recorded as .

[0032] S103, solving the forward autoregressive equation and the backward autoregressive equation to obtain autoregressive model coefficients.

[0033] Solving the forward autoregressive equation and the backward autoregressive equation to obtain autoregressive model coefficients includes: obtaining augmented matrices of the forward autoregressive equation and the backward autoregressive equation; and decomposing the augmented matrix to obtain the autoregressive model coefficients.

[0034] Specifically, the forward autoregressive equation and the backward autoregressive equation are augmented to obtain the augmented matrices of the forward autoregressive equation and the backward autoregressive equation: Then Perform orthogonal-trapezoidal decomposition (QR) and use some elements of R after decomposition to construct the system of equations: , then Perform singular value decomposition to obtain The Moore-Penrose pseudo-inverse matrix , thus obtaining the autoregressive model coefficients: .like Figure 4 As shown, Figure 4 It is a schematic diagram of the result of QR decomposition, where: Figure 4 middle All elements below the main diagonal of are 0.

[0035] Optionally, Performing a QR decomposition involves using the Gram-Schmidt method or other QR decomposition methods (such as Householder transformation or Givens rotation) to transform the matrix A = This breaks down into: ,in, Q is an orthogonal matrix ( Q T Q = I ),and is an upper triangular matrix.

[0036] It should be noted that other methods may also be used to solve the Moore-Penrose pseudo-inverse matrix, and the present invention is not limited thereto.

[0037] S104, substituting the autoregressive model coefficients into the autoregressive model, and recursively performing the autoregressive model according to the time series to restore the full-period signal corresponding to the Doppler micro-motion signal.

[0038] Substitute the autoregressive model coefficients obtained in S103 into the autoregressive model and recursively restore the blind area signal and predict the future multi-period signal to obtain a full-period signal of a certain length. .

[0039] In an exemplary embodiment, the difference between the predicted full-cycle signal waveform and the actual waveform is compared, such as Figure 5 As shown, Figure 5 This is a schematic diagram comparing the full-period signal waveform restored and predicted using the autoregressive model with the actual waveform. Figure 5 Figure (a) is the actual waveform. Figure 5 Figure (b) is the predicted waveform. Figure 5 The horizontal axes of (a) and (b) are time ( t / s ), the vertical axis is the signal amplitude (V); calculate the error between the predicted waveform and the actual waveform, the result is as follows Figure 6 As shown, Figure 6 This is a schematic diagram of the error between the actual waveform and the predicted full-cycle signal waveform. Figure 6 The horizontal axis represents time ( t / s ), the ordinate represents the error amplitude (V). It can be seen that although the error increases with the extension of the prediction time, the absolute error is generally within 10 -3 Below, the results show that this method can accurately restore the full-wave signal when only the 1 / 8 period sampling signal is known.

[0040] S105, analyzing the slow-changing random process of the full-period signal to obtain characteristic parameters of the full-period signal.

[0041] In an exemplary embodiment, the slowly varying random process of a full-periodic signal is analyzed to obtain characteristic parameters of the full-periodic signal, including: performing a fast Fourier transform on the full-periodic signal to obtain frequency domain characteristic quantities of the full-periodic signal; analyzing the slowly varying random process in the full-periodic signal according to the frequency domain characteristic quantities and Rice expression to obtain time domain data of the slowly varying random process of the full-periodic signal; and determining the characteristic parameters of the full-periodic signal according to the time domain data.

[0042] Full cycle signal predicted for S104 Perform Fast Fourier Transform (FFT) to obtain the frequency domain characteristics of the full-period signal: carrier frequency and signal wave frequency , the prediction time length of the full-period signal can be adjusted according to the resolution requirements.

[0043] Among them, in After FFT operation, two peaks will be obtained, and their frequencies are recorded as , the strengths are recorded as . So the carrier frequency is , the signal wave frequency is , The change value of .when hour, is a positive value; when hour, is a negative value. The spectrum diagram of the predicted full-periodic signal is as follows Figure 7 As shown, Figure 7 The horizontal axis represents frequency (Hz), and the vertical axis represents power density (W / Hz). , , then: , .

[0044] Then according to the frequency domain feature Calculate the carrier angular frequency , and then substitute the carrier angular frequency and the full-period signal into the Rice expression to analyze the slow-changing random process in the full-period signal, and get , For two sets of time domain data series, the Rice expression can be expressed by formula (6).

[0045] (6) in, yes The Hilbert transform of , is a standard sinusoidal quantity, respectively detected and The maximum value of , It should be noted that since the error of the autoregressive model in predicting data will accumulate, the longer the prediction time, , The greater the deviation of the maximum value, the greater the maximum value of the previous cycles must be detected to ensure the accuracy of the solution. The solution result is =9.999997654407430, =0.050000101044027, the relative error can reach less than one ten-thousandth. It should be noted that if and If it is a periodic signal, it can be decomposed into a superposition of multiple sine and cosine functions through Fourier transform, and the signal frequency can be obtained by using the frequency domain characteristics of the full-period signal, which is convenient for other subsequent analysis.

[0046] It should be noted that The range of is [-1, 1], then and The maximum value of is and However, is a linear change, so if and only if the time corresponding to the restored full-period signal is > The maximum value is and .

[0047] The present invention realizes accurate signal restoration in the case of large amount of data missing, constructs an autoregressive model, and can accurately restore the blind area signal and predict the signal of multiple cycles by calculating the autoregressive model coefficients, with an error of 10 -3 The following can meet most engineering requirements. In addition, the present invention can achieve real-time parameter solution, and each interval period is solved independently, that is, it is calculated immediately after sampling. Compared with the traditional Kalman filtering method, the present invention does not need to use historical data, can quickly identify the changes in signal waves, and meet real-time requirements.

[0048] In addition, in the real-time parameter solution method of the semi-blind zone Doppler micro-motion signal provided by the present invention, the forward-backward autoregressive equation is constructed to solve the autoregressive model coefficients to accurately restore and predict the blind zone signal; the parameters are solved in real time using the properties of the real stable narrowband Doppler micro-motion signal to meet the needs of accurate signal restoration and real-time parameter solution. The present invention can cope with high-speed big data flows in complex environments and high-dynamic scenes, ensure the accuracy and real-time performance of parameter solution, and provide a reliable basis for real-time monitoring, fault detection and decision support.

[0049] When applying the real-time parameter calculation method of the semi-blind zone Doppler micro-motion signal provided by the present invention, it is not necessary to use Figure 1 The steps are executed in the order shown. The specific execution order of the steps can be determined according to needs, and the present invention does not limit this.

[0050] The above is a real-time parameter calculation method for a semi-blind zone Doppler micro-motion signal provided by one or more embodiments of the present invention. Based on the same idea, the present invention also provides a corresponding real-time parameter calculation device for a semi-blind zone Doppler micro-motion signal, such as Figure 8 shown.

[0051] Figure 8 A schematic diagram of a real-time parameter solving device for a semi-blind zone Doppler micro-motion signal provided by the present invention, the device 800 comprises: An acquisition module 801 is used to acquire a Doppler micro-motion signal to be processed and determine a non-blind zone signal in the Doppler micro-motion signal; A construction module 802 is used to construct a forward autoregressive equation and a backward autoregressive equation according to the autoregressive model of the univariate time series data and the non-blind zone signal; A solution module 803 is used to solve the forward autoregressive equation and the backward autoregressive equation to obtain the autoregressive model coefficients; Obtaining module 804, for substituting the autoregressive model coefficients into the autoregressive model, and recursively performing the autoregressive model according to the time sequence to restore the full-period signal corresponding to the Doppler micromotion signal; The analysis module 805 is used to analyze the slow-changing random process of the full-period signal to obtain the characteristic parameters of the full-period signal.

[0052] The specific definition of the real-time parameter solution device for the semi-blind zone Doppler micro-motion signal can be found in the above definition of the real-time parameter solution method for the semi-blind zone Doppler micro-motion signal, which will not be repeated here. Each module in the above-mentioned real-time parameter solution device for the semi-blind zone Doppler micro-motion signal can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0053] The present invention also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 A real-time parameter solution method for Doppler micro-motion signals in the semi-blind zone is provided.

[0054] The present invention also provides Fig. 9 The structural diagram of the computer device shown in FIG. Fig. 9 As shown in the figure, at the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 A real-time parameter solution method for Doppler micro-motion signals in the semi-blind zone is provided.

[0055] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0056] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of the present invention.

Claims

1. A real-time parameter calculation method for Doppler micro-motion signals in a semi-blind zone, characterized in that: include: Acquire a Doppler micro-motion signal to be processed, and determine a non-blind zone signal in the Doppler micro-motion signal; Constructing a forward autoregressive equation and a backward autoregressive equation according to the autoregressive model of the univariate time series data and the non-blind zone signal; Solving the forward autoregressive equation and the backward autoregressive equation to obtain autoregressive model coefficients; Substituting the autoregressive model coefficients into the autoregressive model, and recursively performing the autoregressive model according to the time sequence to restore the full-period signal corresponding to the Doppler micromotion signal; The slow-varying random process of the full-period signal is analyzed to obtain characteristic parameters of the full-period signal.

2. The method according to claim 1, characterized in that Determining a non-blind zone signal in the Doppler micro-motion signal includes: According to a preset sampling rule, the blind area signal and the non-blind area signal in the Doppler micro-motion signal are determined.

3. The method according to claim 1, characterized in that According to the autoregressive model of the univariate time series data and the non-blind zone signal, a forward autoregressive equation and a backward autoregressive equation are constructed, including: Determining the order of the autoregressive model according to the number of sampling points in the non-blind zone signal; Variables of the autoregressive model are assigned according to the order of the autoregressive model and the non-blind zone signal to obtain the forward autoregressive equation and the backward autoregressive equation.

4. The method according to claim 3, characterized in that The autoregressive model is: ； in, express t The signal of the moment, express n The historical signal before the moment, represents the coefficient of the autoregressive model; The forward autoregressive equation is: ； The backward autoregressive equation is: ； in, n represents the order of the autoregressive model, N Indicates the number of sampling points in the non-blind zone signal.

5. The method according to claim 1, characterized in that: Solving the forward autoregressive equation and the backward autoregressive equation to obtain autoregressive model coefficients includes: Obtaining augmented matrices of the forward autoregressive equation and the backward autoregressive equation; The augmented matrix is ​​decomposed to obtain the autoregressive model coefficients.

6. The method according to claim 1, characterized in that The step of analyzing the slow-changing random process of the full-period signal to obtain characteristic parameters of the full-period signal includes: Performing a fast Fourier transform on the full-period signal to obtain a frequency domain feature quantity of the full-period signal; Analyze the slow-varying random process in the full-period signal according to the frequency domain feature quantity and Rice expression to obtain time domain data of the slow-varying random process of the full-period signal; According to the time domain data, characteristic parameters of the full-period signal are determined.

7. A real-time parameter calculation device for Doppler micro-motion signals in a semi-blind zone, characterized in that: include: An acquisition module, used for acquiring the Doppler micro-motion signal to be processed, and determining the non-blind zone signal in the Doppler micro-motion signal; A construction module, used to construct a forward autoregressive equation and a backward autoregressive equation according to the autoregressive model of the univariate time series data and the non-blind zone signal; A solution module, used for solving the forward autoregressive equation and the backward autoregressive equation to obtain autoregressive model coefficients; A restoration module, used for substituting the autoregressive model coefficients into the autoregressive model, and recursively performing time series deduction on the autoregressive model to restore the full-period signal corresponding to the Doppler micro-motion signal; The analysis module is used to analyze the slow-changing random process of the full-period signal to obtain the characteristic parameters of the full-period signal.

8. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

9. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 6 is implemented.

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