A real-time parameter calculation method, device, readable storage medium and computer equipment for semi-blind zone Doppler micro-motion signal

Abstract

The present invention discloses a real-time parameter solution method, device, readable storage medium and computer equipment for a semi-blind zone Doppler micro-motion signal, and relates to the field of information processing technology. The Doppler micro-motion signal to be processed is obtained, and the non-blind zone signal in the Doppler micro-motion signal is determined; a forward autoregressive equation and a backward autoregressive equation are constructed according to the autoregressive model of the single variable time series data and the non-blind zone signal; the forward autoregressive equation and the backward autoregressive equation are solved to obtain the autoregressive model coefficients; the autoregressive model coefficients are substituted into the autoregressive model, and the autoregressive model is recursively deduced according to the time series to restore the full-period signal corresponding to the Doppler micro-motion signal; the slow-changing random process of the full-period signal is analyzed to obtain the characteristic parameters of the full-period signal. This method can improve the real-time performance of the parameter solution of the semi-blind zone Doppler micro-motion signal.

Description

Technical Field

[0001] This invention relates to the field of information processing technology, and in particular to a method, apparatus, readable storage medium, and computer equipment for real-time parameter calculation of semi-blind zone Doppler micro-motion signals. Background Technology

[0002] Parametric analysis of semi-blind zone Doppler micro-motion signals is an important research direction in the field of information processing. It involves recovering the original signal from missing data and performing parameter calculations. Ensuring accurate signal recovery significantly improves system performance and reliability, and is widely used in systems such as radar detection, laser detection, non-contact measurement, satellite communication, and sensor networks. Existing technologies mainly focus on several key methods. For example, Kalman filtering is a commonly used tool due to its efficiency in state estimation and signal tracking, especially suitable for processing noisy dynamic systems. Compressed sensing theory is also applied in this field, allowing signal reconstruction at low sampling rates, thereby optimizing storage and processing resources.

[0003] Kalman filtering is highly sensitive to initial conditions and model parameters, which can lead to significant uncertainties in practical applications, and it performs poorly in certain nonlinear or noisy environments. While compressed sensing can effectively reduce data acquisition and improve signal recovery efficiency, its high algorithmic complexity can cause computational delays, which is particularly critical for real-time processing requirements.

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

[0005] Therefore, it is necessary to provide a method, apparatus, readable storage medium, and computer device for real-time parameter calculation of semi-blind zone Doppler micro-motion signals to address the above-mentioned technical problems. This method can improve the real-time performance of parameter calculation for semi-blind zone Doppler micro-motion signals.

[0006] The present invention adopts the following technical solution:

[0007] This invention provides a real-time parameter calculation method for semi-blind zone Doppler micro-motion signals, comprising:

[0008] Acquire the Doppler micro-motion signal to be processed and determine the non-blind zone signal in the Doppler micro-motion signal;

[0009] Based on the autoregressive model of univariate time series data and the non-blind zone signal, forward autoregressive equations and backward autoregressive equations are constructed;

[0010] Solve the forward and backward autoregressive equations to obtain the autoregressive model coefficients;

[0011] Substitute the coefficients of the autoregressive model into the autoregressive model, and recursively extrapolate the autoregressive model over time to reconstruct the full-cycle signal corresponding to the Doppler micro-motion signal.

[0012] By analyzing the slowly changing random process of a full-period signal, the characteristic parameters of the full-period signal can be obtained.

[0013] Preferably, determining the non-blind zone signal in the Doppler micro-motion signal includes:

[0014] Based on the preset sampling rules, the blind zone signal and the non-blind zone signal in the Doppler micro-motion signal are determined.

[0015] Preferably, based on the autoregressive model of univariate time series data and the non-blind zone signal, a forward autoregressive equation and a backward autoregressive equation are constructed, including:

[0016] The order of the autoregressive model is determined based on the number of sampling points in the non-blind zone signal.

[0017] The autoregressive model is assigned variable values ​​based on its order and the non-blind zone signal, resulting in the forward autoregressive equation and the backward autoregressive equation.

[0018] Preferably, the autoregressive model is:

[0019] x t =a1x t-1 +a2x t-2 +…+a n x t-n ;

[0020] Where, x t Let x represent the signal at time t. t-i a represents the historical signal at time ti. i Represents the coefficients of the autoregressive model; i = 1, 2, ..., n;

[0021] The forward autoregressive equation is:

[0022]

[0023] The backward autoregressive equation is:

[0024]

[0025] Where n represents the order of the autoregressive model, and N represents the number of sampling points in the non-blind zone signal.

[0026] Preferably, the forward autoregressive equation and the backward autoregressive equation are solved to obtain the autoregressive model coefficients, including:

[0027] Obtain the augmented matrices of the forward autoregressive equation and the backward autoregressive equation;

[0028] The augmented matrix is ​​decomposed to obtain the autoregressive model coefficients.

[0029] Preferably, the characteristic parameters of the full-period signal are obtained by analyzing the slowly changing random process of the full-period signal, including:

[0030] Perform a Fast Fourier Transform on the full-cycle signal to obtain the frequency domain characteristics of the full-cycle signal;

[0031] By analyzing the slow-changing random process in the full-cycle signal using frequency domain characteristics and Rice's expression, the time-domain data of the slow-changing random process in the full-cycle signal can be obtained.

[0032] Based on time-domain data, determine the characteristic parameters of the full-cycle signal.

[0033] This invention provides a real-time parameter calculation device for semi-blind zone Doppler micro-motion signals, comprising:

[0034] The acquisition module is used to acquire the Doppler micro-motion signal to be processed and to determine the non-blind zone signal in the Doppler micro-motion signal;

[0035] The construction module is used to construct forward autoregressive equations and backward autoregressive equations based on the autoregressive model of univariate time series data and the non-blind zone signal;

[0036] The solver module is used to solve the forward autoregressive equation and the backward autoregressive equation to obtain the autoregressive model coefficients;

[0037] The restoration module is used to substitute the coefficients of the autoregressive model into the autoregressive model and recursively extrapolate the autoregressive model according to the time sequence to restore the full-cycle signal corresponding to the Doppler micro-motion signal.

[0038] The analysis module is used to analyze the slow-changing random process of a full-period signal to obtain the characteristic parameters of the full-period signal.

[0039] The present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described method for real-time parameter calculation of semi-blind zone Doppler micro-motion signals.

[0040] The present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the above-mentioned real-time parameter calculation method for semi-blind zone Doppler micro-motion signals.

[0041] The above-mentioned at least one technical solution adopted in this invention can achieve the following beneficial effects:

[0042] In this invention, by constructing forward and backward autoregressive equations and solving the autoregressive model coefficients, accurate restoration and prediction of blind zone signals can be achieved, improving the accuracy of full-cycle signals. Then, based on the predicted full-cycle signals, the slow-changing random process of the full-cycle signals is analyzed to obtain the characteristic parameters of the full-cycle signals. This is equivalent to directly using the acquired Doppler micro-motion signals to solve the parameters without using historical data, thereby improving the real-time performance of parameter calculation. Attached Figure Description

[0043] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0044] Figure 1 A schematic flowchart of a real-time parameter calculation method for semi-blind zone Doppler micro-motion signals provided by the present invention;

[0045] Figure 2 A schematic diagram of the waveform of the Doppler micro-motion signal to be processed is provided for this invention;

[0046] Figure 3 A waveform diagram of a sampled signal provided by the present invention;

[0047] Figure 4 A schematic diagram of the QR decomposition result provided by the present invention;

[0048] Figure 5 This is a schematic diagram showing the comparison between the full-cycle signal waveform recovered and predicted using an autoregressive model and the actual waveform, provided by the present invention.

[0049] Figure 6 This invention provides a schematic diagram illustrating the error between the actual waveform and the predicted full-cycle signal waveform.

[0050] Figure 7 The present invention provides a spectrogram for predicting a full-cycle signal;

[0051] Figure 8 A schematic diagram of a real-time parameter calculation device for semi-blind zone Doppler micro-motion signals provided by the present invention;

[0052] Figure 9 This is a schematic diagram of a computer device for implementing a real-time parameter calculation method for semi-blind zone Doppler micro-motion signals, as provided by the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0054] The technical solutions provided by the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0055] Figure 1 This is a schematic diagram of a real-time parameter calculation method for semi-blind zone Doppler micro-motion signals according to the present invention, which specifically includes the following steps:

[0056] S101, acquire the Doppler micro-motion signal to be processed, and determine the non-blind zone signal in the Doppler micro-motion signal.

[0057] Doppler micro-motion signals can be categorized into narrowband Doppler micro-motion signals and wideband Doppler micro-motion signals. This invention applies to narrowband Doppler micro-motion signals. In mathematical theory, narrowband Doppler micro-motion signals can be represented as:

[0058] x(t)=A(t)cosω c tB(t)sinω c t

[0059]

[0060] Where x(t) is a narrowband Doppler micro-motion signal, t represents time, A(t) and B(t) are both slowly changing random processes, and ω c The center frequency is usually represented by the carrier frequency.

[0061] Narrowband Doppler micro-motion signals are commonly found in electronic systems such as communications and radar. In engineering systems, they can be represented as:

[0062] x=a cosθ(t)cosωt-b sinθ(t)sinωt (2)

[0063] Where x is the narrowband Doppler micro-motion signal, ω is the carrier angular frequency, which can be considered constant within the solution period, ω=2πf c f c =3000Hz, θ(t) is a function of time t, and can be considered to change linearly within the solution period. Let θ(t) = 2πf θ t, f θ =100Hz, a=10, b=0.05. This invention will accurately reconstruct the x signal based on limited signal data and calculate parameters a, b and θ(t).

[0064] Acquiring the Doppler micro-motion signal to be processed includes acquiring the Doppler micro-motion signal output by the sensor, such as... Figure 2 As shown, Figure 2 This is a schematic diagram of a Doppler micro-motion signal waveform, with the horizontal axis representing time (t / s) and the vertical axis representing signal amplitude (V). However, in practical engineering, to improve channel utilization efficiency, it is often impossible to obtain a full-cycle sampled signal. Therefore, the location of the blind zone can be determined based on specific sampling rules, thereby identifying the non-blind zone signal and the blind zone signal.

[0065] Specifically, determining the non-blind zone signal in the Doppler micro-motion signal includes: determining the blind zone signal and non-blind zone signal in the Doppler micro-motion signal according to a preset sampling rule.

[0066] For example, sampling is performed using an AD converter, with the following sampling rules: a sampling rate of 4MHz, an interval of 0.5ms, sampling of the x signal during the 0-1 / 8 interval period, and the remaining time used for system control and other tasks. The non-blind zone signal is only 1 / 8 of the total signal; the remaining time is the blind zone. The sampled waveform is as follows: Figure 3 As shown, the horizontal axis represents time (t / s), and the vertical axis represents the signal amplitude (V).

[0067] Therefore, the blind zone signal and non-blind zone signal in the Doppler micro-motion signal can be determined based on the sampling interval period.

[0068] S102, based on the autoregressive model of univariate time series data and the non-blind zone signal, construct the forward autoregressive equation and the backward autoregressive equation.

[0069] In an exemplary embodiment, constructing a forward autoregressive equation and a backward autoregressive equation based on an autoregressive model of univariate time series data and a non-blind zone signal includes: determining the order of the autoregressive model based on the number of sampling points in the non-blind zone signal; assigning variable values ​​to the autoregressive model based on the order of the autoregressive model and the non-blind zone signal to obtain the forward autoregressive equation and the backward autoregressive equation.

[0070] The autoregressive model is:

[0071] x t =a1x t-1 +a2x t-2 +…+a n x t-n (3)

[0072] Where, x t Let x represent the signal at time t. t-i a represents the historical signal at time ti. i Represents the coefficients of the autoregressive model; i = 1, 2, ..., n.

[0073] Taking an autoregressive model of order n as an example, with autoregressive model coefficients a1, a2, ..., a n In order to solve the autoregressive model coefficients a1, a2, ..., a n Using non-blind zone signals x1, x2, ..., x N An n-order forward autoregressive equation and a backward autoregressive equation are constructed. Optionally, the forward and backward autoregressive equations can be determined based on a portion of the non-blind zone signal. For example, if the number of sampling points in the non-blind zone signal is N, the order n of the autoregressive model can be chosen between N / 3 and N / 2, where N is the number of sampling points within a 1 / 8 interval period. Optionally, N is 250, and n can be 100.

[0074] The forward autoregressive equation is:

[0075]

[0076] Recorded as in This is a column vector consisting of the coefficients of the autoregressive model.

[0077] A back-autoregressive equation has been added, and the back-autoregressive equation is now:

[0078]

[0079] Among them, denoted as

[0080] S103, solve the forward and backward autoregressive equations to obtain the autoregressive model coefficients.

[0081] Solving the forward and backward autoregressive equations to obtain the autoregressive model coefficients includes: obtaining the augmented matrices of the forward and backward autoregressive equations; and decomposing the augmented matrices to obtain the autoregressive model coefficients.

[0082] 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 to Perform orthogonal-trapezoidal decomposition (QR) of the matrix R, and construct a system of equations using some elements of the decomposed matrix R: Then for R n×n Perform singular value decomposition to find P n×n The Moore-Penrose pseudo-inverse matrix P is obtained, thus yielding the autoregressive model coefficients: like Figure 4 As shown, Figure 4 This is a schematic diagram of the QR decomposition results, where, Figure 4 Chinese R n×n All elements below the main diagonal are 0.

[0083] Optionally, for Performing QR decomposition involves using the Gram-Schmidt method or other QR decomposition methods (such as Householder transformation or Givens rotation) to transform the matrix. Decomposed into: A = QR, where Q is an orthogonal matrix (Q T Q = I), and R is an upper triangular matrix.

[0084] It should be noted that other methods can also be used to solve the Moore-Penrose pseudo-inverse matrix, and this invention does not limit the methods used.

[0085] S104, substitute the autoregressive model coefficients into the autoregressive model, and recursively extrapolate the autoregressive model according to the time series to restore the full-cycle signal corresponding to the Doppler micro-motion signal.

[0086] Substituting the autoregressive model coefficients obtained from S103 into the autoregressive model and recursively extrapolating, the blind zone signal is recovered and future multi-period signals are predicted to obtain a full-period signal x of a certain duration. forecast (t).

[0087] In one 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 diagram illustrates the comparison between the full-cycle signal waveform recovered and predicted using an autoregressive model and the actual waveform. Figure 5 Figure (a) shows the actual waveform. Figure 5 Figure (b) shows the predicted waveform. Figure 5 In Figures (a) and (b), the horizontal axis represents time (t / s), and the vertical axis represents signal amplitude (V). The error between the predicted waveform and the actual waveform is calculated, and the results are as follows: Figure 6 As shown, Figure 6 This is a schematic diagram illustrating the error between the actual waveform and the predicted full-cycle signal waveform. Figure 6 The horizontal axis represents time (t / s), and the vertical axis represents the error magnitude (V). It can be seen that although the error increases with the extension of the prediction time, the absolute error remains generally within 10. -3 The results below show that this method can accurately reconstruct the full-wave signal when only 1 / 8 period sampling signal is known.

[0088] S105 analyzes the slow-changing random process of the full-cycle signal to obtain the characteristic parameters of the full-cycle signal.

[0089] In an exemplary embodiment, analyzing the slow-changing random process of a full-cycle signal to obtain the characteristic parameters of the full-cycle signal includes: performing a fast Fourier transform on the full-cycle signal to obtain frequency domain characteristic quantities of the full-cycle signal; analyzing the slow-changing random process in the full-cycle signal based on the frequency domain characteristic quantities and Rice's expression to obtain time domain data of the slow-changing random process of the full-cycle signal; and determining the characteristic parameters of the full-cycle signal based on the time domain data.

[0090] The full-cycle signal x predicted by S104 forecast (t) Perform a Fast Fourier Transform (FFT) to obtain the frequency domain characteristics of the full-cycle signal: carrier frequency f c and signal wave frequency f θ The prediction time length of the full-cycle signal can be adjusted according to the resolution requirements.

[0091] Among them, in x forecast After performing an FFT operation on (t), two peaks will be obtained, with frequencies denoted as f1 and f2 (f1 < f2) and intensities denoted as p1 and p2, respectively. Therefore, the carrier frequency can be obtained as follows: The frequency of the signal wave is The change in θ(t) is Δθ = 2πf θ Δt. When p2 > p1, f θ It is a positive value; when p2 < p1, f θ The value is negative. The spectrum of the predicted full-cycle signal is shown below. Figure 7 As shown, Figure 7 The horizontal axis represents frequency (Hz), and the vertical axis represents power density (W / Hz). Given f1 = 2900Hz and f2 = 3100Hz, then: f c =3000Hz, f θ =100Hz.

[0092] Then based on the frequency domain characteristic quantity f c Calculate the carrier angular frequency ω c =2πf c Then, based on the carrier angular frequency and the full-cycle signal, the Rice expression is used to analyze the slow-changing random process in the full-cycle signal, and two sets of time-domain data sequences a cosθ(t) and b sinθ(t) are obtained. The Rice expression can be expressed by formula (6).

[0093]

[0094] in, It is X forecastThe Hilbert transform of (t) is used. a cosθ(t) and b sinθ(t) are standard sinusoidal quantities. The values ​​of a and b are obtained by detecting the maxima of a cosθ(t) and b sinθ(t), respectively. It should be noted that because the error of the autoregressive model accumulates when predicting data, the longer the prediction time, the greater the deviation of the maxima of a and b. Therefore, it is necessary to detect the maxima of the first few periods to ensure the accuracy of the solution. The solution results are a = 9.999997654407430 and b = 0.050000101044027, with a relative error of less than one ten-thousandth. It should be noted that if a cosθ(t) and b sinθ(t) are periodic signals, they can be decomposed into multiple superimposed sine and cosine functions through Fourier transform. The signal frequency can be obtained using the frequency domain characteristics of the full-cycle signal, facilitating other subsequent analyses.

[0095] It should be noted that the range of cosθ(t) is [-1, 1], so the maxima of a cosθ(t) and b sinθ(t) are the values ​​of a and b. However, θ(t) changes linearly, so the maxima are a and b if and only if the time T corresponding to the reconstructed full-cycle signal is greater than the period of cos(θ).

[0096] This invention achieves accurate signal reconstruction even with significant data loss. It constructs an autoregressive model, and by calculating the model coefficients, it can accurately reconstruct signals in blind zones and predict signals across multiple periods, with an error within 10%. -3 The following features can meet most engineering requirements. Furthermore, this invention enables real-time parameter calculation, with independent calculation for each interval period, allowing for immediate calculation upon data acquisition. Compared to traditional Kalman filtering methods, this invention does not require historical data and can quickly identify changes in signal waves, meeting real-time requirements.

[0097] Furthermore, the real-time parameter calculation method for semi-blind zone Doppler micro-motion signals provided by this invention constructs a forward-backward autoregressive equation, solves the autoregressive model coefficients, and performs accurate reconstruction and prediction of the blind zone signal; it utilizes the properties of real-stationary narrowband Doppler micro-motion signals to calculate parameters in real time, meeting the requirements for accurate signal reconstruction and real-time parameter calculation. This invention can handle high-speed big data streams in complex environments and highly dynamic scenarios, ensuring the accuracy and real-time performance of parameter calculation, and providing a reliable basis for real-time monitoring, fault detection, and decision support.

[0098] When applying the real-time parameter calculation method for the semi-blind zone Doppler micro-motion signal provided by this invention, it is not necessary to rely on... Figure 1 The steps shown are executed in sequence. The specific execution order of each step can be determined as needed, and this invention does not impose any restrictions on it.

[0099] The above describes a real-time parameter calculation method for semi-blind zone Doppler micro-motion signals 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 semi-blind zone Doppler micro-motion signals, such as... Figure 8 As shown.

[0100] Figure 8 This is a schematic diagram of a real-time parameter calculation device for semi-blind zone Doppler micro-motion signals provided by the present invention. The device 800 includes:

[0101] The acquisition module 801 is used to acquire the Doppler micro-motion signal to be processed and to determine the non-blind zone signal in the Doppler micro-motion signal;

[0102] Module 802 is used to construct forward autoregressive equations and backward autoregressive equations based on the autoregressive model of univariate time series data and non-blind zone signals.

[0103] The solver module 803 is used to solve the forward autoregressive equation and the backward autoregressive equation to obtain the autoregressive model coefficients;

[0104] Module 804 is obtained, which is used to substitute the autoregressive model coefficients into the autoregressive model and recursively calculate the autoregressive model according to the time sequence to restore the full-cycle signal corresponding to the Doppler micro-motion signal.

[0105] 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.

[0106] Specific limitations regarding the real-time parameter calculation device for semi-blind zone Doppler micro-motion signals can be found in the limitations of the real-time parameter calculation method for semi-blind zone Doppler micro-motion signals described above, and will not be repeated here. Each module in the aforementioned real-time parameter calculation device for semi-blind zone Doppler micro-motion signals can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0107] The present invention also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figure 1 A method for real-time parameter calculation of semi-blind zone Doppler micro-motion signals is provided.

[0108] The present invention also provides Figure 9 The schematic diagram of the computer device shown is as follows: Figure 9As shown, at the hardware level, this computer device includes a processor, internal bus, network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then executes it to achieve the above. Figure 1 A method for real-time parameter calculation of semi-blind zone Doppler micro-motion signals is provided.

[0109] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided by this 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 storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0110] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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 this 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; The autoregressive model is: x t =a1x t-1 +a2x t-2 +…+a n x t-n ; Among them, x t represents the signal at time t, x t-i represents the historical signal at time ti, a i Represents the coefficient of the autoregressive model; i=1,2,…,n; The forward autoregressive equation is: The backward autoregressive equation is: Wherein, n represents the order of the autoregressive model, and N represents the number of sampling points in 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 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.

5. 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.

6. 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; The autoregressive model is: x t =a1x t-1 +a2x t-2 +…+a n x t-n ; Among them, x t represents the signal at time t, x t-i represents the historical signal at time ti, a i Represents the coefficient of the autoregressive model; i=1,2,…,n; The forward autoregressive equation is: The backward autoregressive equation is: Wherein, n represents the order of the autoregressive model, and N represents the number of sampling points in 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.

7. 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 5 is implemented.

8. 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 5 is implemented.

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