Radar vibration signal extraction method and device

Abstract

The present invention discloses a radar vibration signal extraction method and device. The method comprises: performing pulse compression processing on the radar echo signal after being reflected by a vibrating target to obtain a range compression signal of the echo signal; performing peak detection to obtain the signal fundamental frequency of the range compression signal; performing periodic filtering on the range compression signal using a frequency domain periodic filter established according to the signal fundamental frequency to obtain a periodically filtered signal; performing clutter estimation on the periodically filtered signal to obtain a clutter-removed signal; performing differential interferometry to obtain the differential phase of the clutter-removed signal; performing accumulation processing to obtain a radar vibration target deformation inversion result; and performing low-pass filtering to obtain a radar vibration target deformation inversion result after filtering out deformation noise. The present invention is used to improve the extraction accuracy of radar vibration signals and improve the accuracy of estimating stationary clutter in radar vibration signals.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge vibration monitoring, and in particular to a radar vibration signal extraction method and device. Background Art

[0002] This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. No statement herein is admitted to be prior art by virtue of its inclusion in this section.

[0003] Radar, which uses microwaves, has become a crucial tool for bridge health monitoring due to its non-contact, all-weather, continuous operation, and sub-wavelength high-precision deformation measurement capabilities. Radar monitoring of bridge health primarily involves analyzing vibration signals generated by the bridge. Radar echo signals carry phase information, and differential interferometry can be used to obtain differential phase information at different times. This differential phase information is typically converted into deformation variables, which are then used to extract vibration parameters such as frequency and amplitude to assess bridge health.

[0004] In complex environments, ambient clutter and vibration signals overlap. When the target signal is weak relative to the ambient clutter—that is, when the signal-to-noise ratio is low—the deformation inversion result will be less than the actual deformation, resulting in inaccurate vibration parameter extraction. Clutter estimation is typically required, and then removed from the echo before deformation extraction. Circular fitting is commonly used to estimate stationary clutter. However, in low signal-to-noise ratio conditions, this method is sensitive to noise and struggles to accurately estimate clutter.

[0005] Furthermore, when the signal-to-noise ratio is low, deformation inversion may suffer from jump errors due to phase wrapping. These jump errors are random and difficult to resolve using phase unwrapping methods. Extracting accurate deformation information from vibrating targets in low signal-to-noise ratio and strong clutter conditions is extremely challenging.

[0006] Traditional low-pass filtering noise suppression methods place high demands on the PRF (pulse repetition frequency). The Doppler spectrum of the original vibration radar signal contains multiple harmonics of the target vibration frequency as the fundamental frequency, and the Doppler frequency bandwidth is typically several times larger than the target vibration frequency. To achieve ideal noise suppression, the PRF must be several times larger than the Doppler bandwidth of the vibration radar signal. This high PRF requirement results in heavy system load and large data volumes. Summary of the Invention

[0007] An embodiment of the present invention provides a radar vibration signal extraction method for improving the extraction accuracy of radar vibration signals, resolving the problem of jump errors in deformation inversion, and improving the accuracy of estimating stationary clutter in radar vibration signals. The method includes:

[0008] Performing pulse compression processing on the radar echo signal after being reflected by the vibrating target to obtain a range compression signal of the echo signal;

[0009] performing peak detection on the range Doppler domain of the range compression signal to obtain a signal fundamental frequency of the range compression signal;

[0010] Performing periodic filtering on the range compression signal using a frequency domain periodic filter established according to the fundamental frequency of the signal to obtain a periodically filtered signal;

[0011] performing clutter estimation on the periodically filtered signal to obtain an estimated clutter signal; subtracting the estimated clutter signal from the periodically filtered signal to obtain a signal after clutter removal;

[0012] Differential interference is performed on the signal after clutter removal to obtain a differential phase of the signal after clutter removal; and the differential phase is accumulated to obtain a radar vibration target deformation inversion result; the radar vibration target deformation inversion result is low-pass filtered to obtain a radar vibration target deformation inversion result after deformation noise is filtered out.

[0013] An embodiment of the present invention further provides a radar vibration signal extraction device for improving the extraction accuracy of radar vibration signals, resolving the problem of jump errors in deformation inversion, and improving the accuracy of estimating stationary clutter in radar vibration signals. The device comprises:

[0014] A pulse compression processing module is used to perform pulse compression processing on the radar echo signal after it is reflected by the vibrating target to obtain a range compression signal of the echo signal;

[0015] a peak detection module, configured to perform peak detection on the range Doppler domain of the range compression signal to obtain a signal fundamental frequency of the range compression signal;

[0016] a periodic filtering module, configured to perform periodic filtering on the range compression signal using a frequency domain periodic filter established according to the fundamental frequency of the signal to obtain a periodically filtered signal;

[0017] a clutter removal module configured to perform clutter estimation on the periodically filtered signal to obtain an estimated clutter signal; and to subtract the estimated clutter signal from the periodically filtered signal to obtain a clutter-removed signal;

[0018] The low-pass filtering module is used to perform differential interference on the signal after clutter removal to obtain the differential phase of the signal after clutter removal; and to perform accumulation processing on the differential phase to obtain the radar vibration target deformation inversion result; and to perform low-pass filtering on the radar vibration target deformation inversion result to obtain the radar vibration target deformation inversion result after filtering out the deformation noise.

[0019] An embodiment of the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned radar vibration signal extraction method when executing the computer program.

[0020] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned radar vibration signal extraction method is implemented.

[0021] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the above-mentioned radar vibration signal extraction method is implemented.

[0022] In an embodiment of the present invention, pulse compression processing is performed on an echo signal of a radar after being reflected by a vibrating target to obtain a range compression signal of the echo signal; peak detection is performed on a range Doppler domain of the range compression signal to obtain a signal fundamental frequency of the range compression signal; periodic filtering is performed on the range compression signal using a frequency domain periodic filter established according to the signal fundamental frequency to obtain a periodically filtered signal; clutter estimation is performed on the periodically filtered signal to obtain an estimated clutter signal; the estimated clutter signal is subtracted from the periodically filtered signal to obtain a clutter-removed signal; differential interference is performed on the clutter-removed signal to obtain a differential phase of the clutter-removed signal; and accumulation processing is performed on the differential phase to obtain an inversion result of the radar vibrating target deformation; and the radar vibrating target is subjected to the inversion of the radar vibrating target deformation. The standard shape variable inversion result is low-pass filtered to obtain the radar vibration target shape variable inversion result after filtering out the shape variable noise. By establishing a frequency domain periodic filter, differential phase accumulation processing and low-pass filtering processing, the periodic repetition characteristics of the periodic signal can be used to suppress noise, and the purpose of accumulating and denoising the signals of adjacent periods by using the periodic repetition characteristics of the periodic signal is achieved. Compared with the low-pass filtering method under the existing technology, there is no oversampling requirement for PRF, and the radar vibration signal can be extracted in a complex environment, which improves the extraction accuracy of the radar vibration signal; at the same time, after periodic filtering, the stationary clutter can be estimated more accurately, and the problem of deformation inversion jump error can be solved, which effectively solves the problem of difficult estimation of stationary clutter under low signal-to-noise ratio, and improves the accuracy of stationary clutter estimation in radar vibration signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0024] Figure 1 1 is a flow chart of a radar vibration signal extraction method according to an embodiment of the present invention;

[0025] Figure 2 This is a specific example diagram of a radar vibration signal extraction method according to an embodiment of the present invention;

[0026] Figure 3 This is a structural example diagram of a radar vibration signal extraction device according to an embodiment of the present invention;

[0027] Figure 4 This is a specific example diagram of a radar vibration signal extraction device according to an embodiment of the present invention;

[0028] Figure 5 Schematic diagram of a computer device used for radar vibration signal extraction in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] To make the purpose, technical solutions and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0030] The term "and / or" herein simply describes an association relationship, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, the simultaneous existence of A and B, and the existence of B alone. In addition, the term "at least one" herein refers to any combination of at least two of any one or more of a plurality of items. For example, "at least one of A, B, and C" can represent any one or more elements selected from the set consisting of A, B, and C.

[0031] In the description of this specification, the terms "include", "including", "have", "contain", etc. are all open terms, which mean including but not limited to. The descriptions with reference to the terms "one embodiment", "a specific embodiment", "some embodiments", "for example", etc. mean that the specific features, structures or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. The order of steps involved in each embodiment is used to schematically illustrate the implementation of the present application, and the order of steps therein is not limited and can be appropriately adjusted as needed.

[0032] The acquisition, storage, use, and processing of data in this application's technical solution comply with relevant national laws and regulations.

[0033] The difficulty in extracting radar vibration signals lies in the fact that the complexity of the environment can lead to inaccurate vibration parameter extraction. When the signal-to-noise ratio is low, the presence of clutter can cause the extracted deformation to be smaller than the actual deformation. Currently, circular fitting is commonly used to estimate stationary clutter. However, under low signal-to-noise ratio conditions, circular fitting clutter estimation methods are sensitive to noise, making it difficult to accurately estimate clutter.

[0034] In order to solve the above problems, the embodiment of the present invention provides a radar vibration signal extraction method to improve the extraction accuracy of radar vibration signals, solve the problem of jump error in deformation inversion, and improve the accuracy of stationary clutter estimation in radar vibration signals. Figure 1 , the method may include:

[0035] Step 101: performing pulse compression processing on the radar echo signal after being reflected by the vibrating target to obtain a range compression signal of the echo signal;

[0036] Step 102: performing peak detection on the range Doppler domain of the range compression signal to obtain the signal fundamental frequency of the range compression signal;

[0037] Step 103: performing periodic filtering on the range compression signal using a frequency domain periodic filter established according to the signal fundamental frequency to obtain a periodically filtered signal;

[0038] Step 104: performing clutter estimation on the periodically filtered signal to obtain an estimated clutter signal; subtracting the estimated clutter signal from the periodically filtered signal to obtain a signal after clutter removal;

[0039] Step 105: Perform differential interference on the signal after clutter removal to obtain a differential phase of the signal after clutter removal; perform accumulation processing on the differential phase to obtain a radar vibration target deformation inversion result; perform low-pass filtering on the radar vibration target deformation inversion result to obtain a radar vibration target deformation inversion result after filtering out deformation noise.

[0040] In an embodiment of the present invention, pulse compression processing is performed on an echo signal of a radar after being reflected by a vibrating target to obtain a range compression signal of the echo signal; peak detection is performed on a range Doppler domain of the range compression signal to obtain a signal fundamental frequency of the range compression signal; periodic filtering is performed on the range compression signal using a frequency domain periodic filter established according to the signal fundamental frequency to obtain a periodically filtered signal; clutter estimation is performed on the periodically filtered signal to obtain an estimated clutter signal; the estimated clutter signal is subtracted from the periodically filtered signal to obtain a clutter-removed signal; differential interference is performed on the clutter-removed signal to obtain a differential phase of the clutter-removed signal; and accumulation processing is performed on the differential phase to obtain an inversion result of the radar vibrating target deformation; and the radar vibrating target is subjected to the inversion of the radar vibrating target deformation. The standard shape variable inversion result is low-pass filtered to obtain the radar vibration target shape variable inversion result after filtering out the shape variable noise. By establishing a frequency domain periodic filter, differential phase accumulation processing and low-pass filtering processing, the periodic repetition characteristics of the periodic signal can be used to suppress noise, and the purpose of accumulating and denoising the signals of adjacent periods by using the periodic repetition characteristics of the periodic signal is achieved. Compared with the low-pass filtering method under the existing technology, there is no oversampling requirement for PRF, and the radar vibration signal can be extracted in a complex environment, which improves the extraction accuracy of the radar vibration signal; at the same time, after periodic filtering, the stationary clutter can be estimated more accurately, and the problem of deformation inversion jump error can be solved, which effectively solves the problem of difficult estimation of stationary clutter under low signal-to-noise ratio, and improves the accuracy of stationary clutter estimation in radar vibration signals.

[0041] In a specific implementation, firstly, pulse compression processing is performed on the radar echo signal after being reflected by the vibrating target to obtain a range compression signal of the echo signal.

[0042] In specific implementation, after the radar echo signal after being reflected by the vibrating target is pulse compressed to obtain the range compression signal of the echo signal, peak detection is performed on the range Doppler domain of the range compression signal to obtain the signal fundamental frequency of the range compression signal.

[0043] In the embodiment, in the range Doppler domain of the original vibration radar signal, the signal fundamental frequency is obtained by a peak detection method, which provides input data for the subsequent design of a periodic filter.

[0044] The spectrum of a periodic signal is discrete, distributed at discrete frequency points around the target vibration frequency and its harmonics. In the Doppler domain, the energy of the periodic signal accumulates at the harmonics, resulting in a high signal-to-noise ratio. Therefore, the fundamental frequency of the spectrum can be determined through spectrum peak detection.

[0045] In a specific implementation, after peak detection is performed on the range Doppler domain of the range compression signal to obtain the signal fundamental frequency of the range compression signal, the range compression signal is periodically filtered using a frequency domain periodic filter established according to the signal fundamental frequency to obtain a periodically filtered signal.

[0046] In the embodiment, a frequency domain periodic filter is designed, and a signal is obtained by multiplying the range Doppler domain signal with the frequency domain periodic filter and performing inverse Fourier transform, thereby completing periodic filtering, suppressing noise, and improving the signal-to-noise ratio.

[0047] In the above embodiment, noise suppression is performed by using the periodic repetitive characteristics of the periodic signal through the periodic filtering method. Unlike the low-pass filtering method, the periodic filtering method does not use a sliding window smoothing filter of the signal adjacent units, but instead uses the cumulative average of several adjacent periodic signals to suppress noise.

[0048] In one embodiment, it further includes:

[0049] A frequency domain periodic filter is established based on the fundamental frequency of the signal according to the following formula:

[0050]

[0051] in, represents the frequency domain expression of the frequency domain periodic filter; f d represents the Doppler frequency; v represents the slow time; g(v) is the periodic filter; δ represents the impulse function; T = 1 / f v , T is the vibration target period, f v is the vibration frequency; m=1,2,..., m is a preset constant; M is the sequence number of the adjacent cycles involved in filtering, and M is manually set.

[0052] In the embodiment, f d The overall parameter meaning is the Doppler frequency; because the Doppler spectrum is a discrete spectrum, only f d =m·f v Spectrum at harmonic frequencies, where m = 0, 1, ...; v represents slow time, which is also known as fast time in radar technology.

[0053] In one embodiment, periodic filtering is performed on the range compression signal using a frequency domain periodic filter established according to the fundamental frequency of the signal to obtain a periodically filtered signal, including:

[0054] The frequency domain periodic filter and the range Doppler domain of the range compression signal are multiplied and inverse Fourier transformed to obtain a periodically filtered signal.

[0055] In one embodiment, the range compression signal is periodically filtered using a frequency domain periodic filter established according to the fundamental frequency of the signal according to the following formula to obtain a periodically filtered signal:

[0056]

[0057] Among them, s f (v) represents the frequency domain expression of the signal after periodic filtering; s d (f d ) represents the range Doppler domain of the range compression signal; G(f d ) represents the frequency domain expression of the frequency domain periodic filter; f d represents the Doppler frequency; v represents the slow time.

[0058] In a specific implementation, the range compression signal is periodically filtered using a frequency domain periodic filter established according to the signal fundamental frequency to obtain a periodically filtered signal, and then clutter estimation is performed on the periodically filtered signal to obtain an estimated clutter signal; and the estimated clutter signal is subtracted from the periodically filtered signal to obtain a signal after clutter removal.

[0059] In an embodiment, a least squares circle fitting method may be used to estimate the clutter of the signal after periodic filtering, and the estimated clutter is subtracted therefrom to obtain a signal after clutter removal.

[0060] In one embodiment, performing clutter estimation on the periodically filtered signal to obtain an estimated clutter signal includes:

[0061] The clutter estimation is performed on the periodically filtered signal in a least squares circle fitting manner to obtain an estimated clutter signal.

[0062] During specific implementation, clutter estimation is performed on the periodically filtered signal to obtain an estimated clutter signal; after subtracting the estimated clutter signal from the periodically filtered signal to obtain a clutter-removed signal, differential interference is performed on the clutter-removed signal to obtain a differential phase of the clutter-removed signal; and accumulation processing is performed on the differential phase to obtain a radar vibration target deformation inversion result; and low-pass filtering is performed on the radar vibration target deformation inversion result to obtain a radar vibration target deformation inversion result after filtering out deformation noise.

[0063] In the embodiment, the frequency of the deformation variable is , and the signal bandwidth is smaller than the Doppler bandwidth of the original dynamic radar signal. A low-pass filtering method can be further used to filter out the deformation variable noise.

[0064] A specific embodiment is given below to illustrate the specific application of the method of the present invention.

[0065] First, the vibration signal expression containing stationary clutter and Gaussian white noise in this embodiment is proposed:

[0066] The radar performs pulse compression on the echo signal after it is reflected by the vibrating target to obtain a range compression signal (i.e., a one-dimensional slow-time signal). The peak of the range compression signal is the distance between the target and the radar. Vibration analysis is to analyze the one-dimensional signal that varies with slow time at the distance, namely:

[0067]

[0068] Among them, s v (v) represents the vibration signal under ideal conditions; σ p represents the complex scattering coefficient of the vibration point P; τ represents the complex scattering coefficient of the vibration point; R0 represents the distance between the radar and the center of the vibration point; v represents the slow time; λ c Indicates wavelength, λ c =C / f c , C is the wave speed, f c Represents the center frequency; X represents the simple harmonic vibration of the vibration point P, which is a function of the slow time v, and its expression is:

[0069]

[0070] Where X(v) represents the simple harmonic vibration model of the vibration point; A is the vibration amplitude of the vibration point P, f v is the vibration frequency, is the initial phase of simple harmonic oscillation.

[0071] In the complex plane, stationary noise causes the center of the vibration signal's circular ring or arc to deviate from the origin. The coordinates of the center are the real and imaginary parts of the stationary noise. The presence of stationary noise causes the differential phase to be smaller than the differential phase due to the actual vibration deformation, resulting in the vibration amplitude estimate being smaller than the actual value. Circle fitting is commonly used to estimate the center of the complex plane signal, obtain the stationary noise, and remove it. When the signal-to-noise ratio is low, Gaussian white noise can make the center estimate inaccurate, especially when the vibration amplitude is small.

[0072] The expression of the vibration signal containing stationary clutter and Gaussian white noise is:

[0073] s(v)=s v (v)+s c +s n (3)

[0074] Where s(v) represents the vibration signal expression containing stationary clutter and Gaussian white noise; s cRepresents the stationary clutter within the same distance unit as the vibrating target; s n stands for Gaussian white noise.

[0075] Then, the vibration signal expression including stationary clutter and Gaussian white noise is combined, and the periodic repetition characteristic of the periodic signal is used to suppress noise, thereby accurately estimating the stationary clutter. Figure 2 As shown, the following steps may be included:

[0076] Step 1: Vibration frequency estimation.

[0077] In the range Doppler domain s of the original vibration radar signal (1) d (f d ), the signal fundamental frequency f is obtained by the peak detection method as shown in formula (4) v , providing input for periodic filter design.

[0078] f v =arc(max(s d (f d ))) (4)

[0079] The spectrum of the periodic signal is a discrete spectrum, and the spectrum is distributed at the target vibration frequency f v In the Doppler domain, the energy of the periodic signal accumulates at the harmonic frequency, the signal-to-noise ratio is high, and the fundamental frequency f of the spectrum can be obtained by spectrum peak detection. v .

[0080] Step 2: Periodic filtering.

[0081] Design a frequency domain periodic filter G(f d ), through the range Doppler domain signal s d (f d ) is multiplied by the frequency domain periodic filter and the inverse Fourier transform to obtain the signal s f (v) Complete periodic filtering to suppress noise and improve signal-to-noise ratio.

[0082] The frequency domain expression of periodic filtering is:

[0083]

[0084] The expression of frequency domain periodic filtering is:

[0085]

[0086] Step 3: Clutter estimation. The signal s after periodic filtering is estimated by the least squares circular fitting method. f (v) Perform clutter estimation and obtain the clutter from s f (v) will be subtracted to estimate the clutter Get the signal

[0087] The least squares circle fitting is to minimize the sum of the squared errors between the square of the distance from the vibration vector signal to the center of the circle and the square of the radius. The optimization cost function is:

[0088]

[0089] in,

[0090] The symbol T represents transposition, the center of the circle is x, and x=(I(s c ),Q(s c )) T , the radius is r, r=|s p |, the vibration signal is s k =|Is f (v k ), Qs f (v k )|, where v k is the discrete value of slow time, k=1,2,...; g k (x, r) is the error between the square of the distance from the vibration vector signal to the center of the circle and the square of the radius that is minimized.

[0091] Step 4: Deformation inversion. Obtained by differential interferometry The differential phase is then accumulated to obtain the deformation variable

[0092] The expression for differential phase is as follows:

[0093]

[0094] Among them, φ d (v k ) represents the differential phase; function arg represents the angle.

[0095]

[0096] Step 5: Low-pass filtering of the deformation. The frequency is f v , the signal bandwidth is smaller than the Doppler bandwidth of the original dynamic radar signal, and the deformation noise can be further filtered out by low-pass filtering.

[0097] Taking frequency domain low-pass filtering as an example, its expression is:

[0098]

[0099] Where FT represents Fourier transform, IFT represents inverse Fourier transform, and H lfis a frequency domain low-pass filter, and its expression is:

[0100]

[0101] Among them, rect represents the rectangular function, B lf is the low-pass filter bandwidth, B lf It needs to be greater than 2 times the vibration frequency, that is:

[0102] f lf >2f v (13)

[0103] In summary, this specific embodiment provides a method for periodically filtering low-SNR strong clutter radar vibration signals, leveraging the repetitive nature of periodic signals for noise suppression. Unlike low-pass filtering, this method does not employ a sliding window smoothing filter on adjacent signal elements, but rather uses the cumulative average of several adjacent periodic signals to suppress noise. This method does not require oversampling of the PRF; the PRF only needs to satisfy the Nyquist sampling theorem. After periodic filtering, stationary clutter can be more accurately estimated and the problem of jump errors in deformation inversion can be resolved.

[0104] This method offers the following advantages: 1. It effectively solves the problem of estimating stationary clutter under low signal-to-noise ratio conditions. 2. The main operation steps involve deriving the time-domain and frequency-domain expressions of the periodic filter. This method utilizes the periodic repetition of periodic signals to cumulatively denoise signals from adjacent periods. Compared to traditional low-pass filtering methods, it does not require oversampling of the PRF. This allows for simple operation and extraction of radar vibration signals in complex environments.

[0105] Of course, it is understandable that the above detailed process may have other variations, and all relevant variations should fall within the scope of protection of the present invention.

[0106] In an embodiment of the present invention, pulse compression processing is performed on an echo signal of a radar after being reflected by a vibrating target to obtain a range compression signal of the echo signal; peak detection is performed on a range Doppler domain of the range compression signal to obtain a signal fundamental frequency of the range compression signal; periodic filtering is performed on the range compression signal using a frequency domain periodic filter established according to the signal fundamental frequency to obtain a periodically filtered signal; clutter estimation is performed on the periodically filtered signal to obtain an estimated clutter signal; the estimated clutter signal is subtracted from the periodically filtered signal to obtain a clutter-removed signal; differential interference is performed on the clutter-removed signal to obtain a differential phase of the clutter-removed signal; and accumulation processing is performed on the differential phase to obtain an inversion result of the radar vibrating target deformation; and the radar vibrating target is subjected to the inversion of the radar vibrating target deformation. The standard shape variable inversion result is low-pass filtered to obtain the radar vibration target shape variable inversion result after filtering out the shape variable noise. By establishing a frequency domain periodic filter, differential phase accumulation processing and low-pass filtering processing, the periodic repetition characteristics of the periodic signal can be used to suppress noise, and the purpose of accumulating and denoising the signals of adjacent periods by using the periodic repetition characteristics of the periodic signal is achieved. Compared with the low-pass filtering method under the existing technology, there is no oversampling requirement for PRF, and the radar vibration signal can be extracted in a complex environment, which improves the extraction accuracy of the radar vibration signal; at the same time, after periodic filtering, the stationary clutter can be estimated more accurately, and the problem of deformation inversion jump error can be solved, which effectively solves the problem of difficult estimation of stationary clutter under low signal-to-noise ratio, and improves the accuracy of stationary clutter estimation in radar vibration signals.

[0107] The present invention also provides a radar vibration signal extraction device, as described in the following embodiments. Because the principles of this device are similar to those of the radar vibration signal extraction method, the implementation of this device can be referenced to the implementation of the radar vibration signal extraction method, and any repetitions will not be repeated.

[0108] The embodiment of the present invention also provides a radar vibration signal extraction device to improve the extraction accuracy of radar vibration signals, solve the problem of jump error in deformation inversion, and improve the accuracy of estimating stationary clutter in radar vibration signals. Figure 3 As shown, the device includes:

[0109] The pulse compression processing module 301 is used to perform pulse compression processing on the radar echo signal after it is reflected by the vibrating target to obtain a range compression signal of the echo signal;

[0110] The peak detection module 302 is configured to perform peak detection on the range Doppler domain of the range compression signal to obtain a signal fundamental frequency of the range compression signal;

[0111] A periodic filtering module 303 is configured to perform periodic filtering on the range compression signal using a frequency domain periodic filter established according to the fundamental frequency of the signal to obtain a periodically filtered signal;

[0112] The clutter removal module 304 is configured to perform clutter estimation on the periodically filtered signal to obtain an estimated clutter signal; and subtract the estimated clutter signal from the periodically filtered signal to obtain a clutter-removed signal.

[0113] The low-pass filtering module 305 is used to perform differential interference on the signal after clutter removal to obtain the differential phase of the signal after clutter removal; and to accumulate the differential phase to obtain the radar vibration target deformation inversion result; and to perform low-pass filtering on the radar vibration target deformation inversion result to obtain the radar vibration target deformation inversion result after filtering out the deformation noise.

[0114] In one embodiment, Figure 4 As shown, it also includes:

[0115] The frequency domain periodic filter establishment module 401 is used to:

[0116] A frequency domain periodic filter is established based on the fundamental frequency of the signal according to the following formula:

[0117]

[0118] Among them, G(f d ) represents the frequency domain expression of the frequency domain periodic filter; f d represents the Doppler frequency; v represents the slow time; g(v) is the periodic filter; δ represents the impulse function; T = 1 / f v , T is the vibration target period, f v is the vibration frequency; m=1,2,..., m is a preset constant; M is the sequence number of the adjacent cycles involved in filtering, and M is manually set.

[0119] In one embodiment, the periodic filtering module is specifically configured to:

[0120] The frequency domain periodic filter and the range Doppler domain of the range compression signal are multiplied and inverse Fourier transformed to obtain a periodically filtered signal.

[0121] In one embodiment, the periodic filtering module is specifically configured to:

[0122] The range compression signal is periodically filtered using a frequency domain periodic filter established according to the signal fundamental frequency according to the following formula to obtain a periodically filtered signal:

[0123]

[0124] Among them, s f (v) represents the frequency domain expression of the signal after periodic filtering; s d (f d ) represents the range Doppler domain of the range compression signal; G(f d ) represents the frequency domain expression of the frequency domain periodic filter; f d represents the Doppler frequency; v represents the slow time.

[0125] In one embodiment, the clutter removal module is specifically configured to:

[0126] The clutter estimation is performed on the periodically filtered signal in a least squares circle fitting manner to obtain an estimated clutter signal.

[0127] An embodiment of the present invention provides an embodiment of a computer device for implementing all or part of the above-mentioned radar vibration signal extraction method. The computer device specifically includes the following contents:

[0128] A processor, a memory, a communications interface, and a bus; wherein the processor, memory, and communications interface communicate with each other via the bus; the communications interface is used to transmit information between related devices; the computer device can be a desktop computer, a tablet computer, a mobile terminal, etc., but this embodiment is not limited thereto. In this embodiment, the computer device can be implemented with reference to the embodiment for implementing the radar vibration signal extraction method and the embodiment for implementing the radar vibration signal extraction device, the contents of which are incorporated herein and repeated parts are not repeated.

[0129] Figure 5 1 is a schematic block diagram of the system structure of the computer device 1000 according to an embodiment of the present application. Figure 5 As shown, the computer device 1000 may include a central processor 1001 and a memory 1002; the memory 1002 is coupled to the central processor 1001. Figure 5 is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0130] In one embodiment, the radar vibration signal extraction function may be integrated into the central processing unit 1001. The central processing unit 1001 may be configured to perform the following control:

[0131] Performing pulse compression processing on the radar echo signal after being reflected by the vibrating target to obtain a range compression signal of the echo signal;

[0132] performing peak detection on the range Doppler domain of the range compression signal to obtain a signal fundamental frequency of the range compression signal;

[0133] Performing periodic filtering on the range compression signal using a frequency domain periodic filter established according to the fundamental frequency of the signal to obtain a periodically filtered signal;

[0134] performing clutter estimation on the periodically filtered signal to obtain an estimated clutter signal; subtracting the estimated clutter signal from the periodically filtered signal to obtain a signal after clutter removal;

[0135] Differential interference is performed on the signal after clutter removal to obtain a differential phase of the signal after clutter removal; and the differential phase is accumulated to obtain a radar vibration target deformation inversion result; the radar vibration target deformation inversion result is low-pass filtered to obtain a radar vibration target deformation inversion result after deformation noise is filtered out.

[0136] In another embodiment, the radar vibration signal extraction device can be configured separately from the central processing unit 1001. For example, the radar vibration signal extraction device can be configured as a chip connected to the central processing unit 1001, and the radar vibration signal extraction function can be realized through the control of the central processing unit.

[0137] like Figure 5 As shown, the computer device 1000 may further include: a communication module 1003, an input unit 1004, an audio processor 1005, a display 1006, and a power supply 1007. It is worth noting that the computer device 1000 does not necessarily have to include Figure 5 In addition, the computer device 1000 may also include all components shown in Figure 5 For components not shown, reference may be made to the prior art.

[0138] like Figure 5 As shown, the central processing unit 1001 is sometimes also referred to as a controller or an operation control unit, and may include a microprocessor or other processor device and / or logic device. The central processing unit 1001 receives inputs and controls the operations of various components of the computer device 1000 .

[0139] Memory 1002 can be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It can store the aforementioned failure-related information and a program that executes the relevant information. The CPU 1001 can execute the program stored in memory 1002 to implement information storage or processing.

[0140] Input unit 1004 provides input to CPU 1001. Input unit 1004 may be, for example, a keypad or touch input device. Power supply 1007 is used to provide power to computer device 1000. Display 1006 is used to display objects such as images and text. This display may be, for example, an LCD display, but is not limited thereto.

[0141] The memory 1002 may be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), or SIM card. Alternatively, it may be a memory that retains information even when power is off, can be selectively erased, and is provided with more data. Examples of such memory are sometimes referred to as EPROMs. The memory 1002 may also be some other type of device. The memory 1002 includes a buffer memory 1021 (sometimes referred to as a buffer). The memory 1002 may include an application / function storage unit 1022 for storing application programs and function programs or processes used by the central processing unit 1001 to execute the operations of the computer device 1000.

[0142] The memory 1002 may also include a data storage unit 1023 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the computer device. The driver storage unit 1024 of the memory 1002 may include various driver programs for the computer device for communication functions and / or for executing other functions of the computer device (such as messaging applications, address book applications, etc.).

[0143] The communication module 1003 is a transmitter / receiver 1003 that sends and receives signals via the antenna 1008. The communication module (transmitter / receiver) 1003 is coupled to the central processor 1001 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0144] Based on different communication technologies, multiple communication modules 1003 can be provided in the same computer device, such as a cellular network module, a Bluetooth module, and / or a wireless local area network module. The communication module (transmitter / receiver) 1003 is also coupled to a speaker 1009 and a microphone 1010 via an audio processor 1005 to provide audio output via the speaker 1009 and receive audio input from the microphone 1010, thereby implementing common telecommunication functions. The audio processor 1005 may include any suitable buffer, decoder, amplifier, etc. Furthermore, the audio processor 1005 is coupled to the central processing unit 1001, enabling local recording via the microphone 1010 and playback of stored audio via the speaker 1009.

[0145] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned radar vibration signal extraction method is implemented.

[0146] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the above-mentioned radar vibration signal extraction method is implemented.

[0147] In an embodiment of the present invention, pulse compression processing is performed on an echo signal of a radar after being reflected by a vibrating target to obtain a range compression signal of the echo signal; peak detection is performed on a range Doppler domain of the range compression signal to obtain a signal fundamental frequency of the range compression signal; periodic filtering is performed on the range compression signal using a frequency domain periodic filter established according to the signal fundamental frequency to obtain a periodically filtered signal; clutter estimation is performed on the periodically filtered signal to obtain an estimated clutter signal; the estimated clutter signal is subtracted from the periodically filtered signal to obtain a clutter-removed signal; differential interference is performed on the clutter-removed signal to obtain a differential phase of the clutter-removed signal; and accumulation processing is performed on the differential phase to obtain an inversion result of the radar vibrating target deformation; and the radar vibrating target is subjected to the inversion of the radar vibrating target deformation. The standard shape variable inversion result is low-pass filtered to obtain the radar vibration target shape variable inversion result after filtering out the shape variable noise. By establishing a frequency domain periodic filter, differential phase accumulation processing and low-pass filtering processing, the periodic repetition characteristics of the periodic signal can be used to suppress noise, and the purpose of accumulating and denoising the signals of adjacent periods by using the periodic repetition characteristics of the periodic signal is achieved. Compared with the low-pass filtering method under the existing technology, there is no oversampling requirement for PRF, and the radar vibration signal can be extracted in a complex environment, which improves the extraction accuracy of the radar vibration signal; at the same time, after periodic filtering, the stationary clutter can be estimated more accurately, and the problem of deformation inversion jump error can be solved, which effectively solves the problem of difficult estimation of stationary clutter under low signal-to-noise ratio, and improves the accuracy of stationary clutter estimation in radar vibration signals.

[0148] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0149] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0150] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0151] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0152] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A radar vibration signal extraction method, characterized in that: include: Performing pulse compression processing on the radar echo signal after being reflected by the vibrating target to obtain a range compression signal of the echo signal; performing peak detection on the range Doppler domain of the range compression signal to obtain a signal fundamental frequency of the range compression signal; Performing periodic filtering on the range compression signal using a frequency domain periodic filter established according to the fundamental frequency of the signal to obtain a periodically filtered signal; performing clutter estimation on the periodically filtered signal to obtain an estimated clutter signal; subtracting the estimated clutter signal from the periodically filtered signal to obtain a signal after clutter removal; Performing differential interference on the signal after clutter removal to obtain a differential phase of the signal after clutter removal; and performing accumulation processing on the differential phase to obtain an inversion result of the radar vibration target deformation; The radar vibration target deformation inversion result is subjected to low-pass filtering to obtain the radar vibration target deformation inversion result after deformation noise is filtered out.

2. The method according to claim 1, wherein Also includes: A frequency domain periodic filter is established based on the fundamental frequency of the signal according to the following formula: Among them, G(f d ) represents the frequency domain expression of the frequency domain periodic filter; f d represents the Doppler frequency; v represents the slow time; g(v) is the periodic filter; δ represents the impulse function; T = 1 / f v , T is the vibration target period, f v is the signal fundamental frequency; m is a preset constant, m=1,2,...; M is the sequence number of the adjacent cycles involved in filtering, M is manually set.

3. The method according to claim 1, wherein Performing periodic filtering on the distance compression signal using a frequency domain periodic filter established according to the fundamental frequency of the signal to obtain a periodically filtered signal, including: The frequency domain periodic filter and the range Doppler domain of the range compression signal are multiplied and inverse Fourier transformed to obtain a periodically filtered signal.

4. The method according to claim 1, wherein The range compression signal is periodically filtered using a frequency domain periodic filter established according to the signal fundamental frequency according to the following formula to obtain a periodically filtered signal: Among them, s f (v) represents the frequency domain expression of the signal after periodic filtering; s d (f d ) represents the range Doppler domain of the range compression signal; G(f d ) represents the frequency domain expression of the frequency domain periodic filter; f d represents the Doppler frequency; v represents the slow time.

5. The method according to claim 1, wherein Performing clutter estimation on the periodically filtered signal to obtain an estimated clutter signal includes: The clutter estimation is performed on the periodically filtered signal in a least squares circle fitting manner to obtain an estimated clutter signal.

6. A radar vibration signal extraction device, characterized in that: include: A pulse compression processing module is used to perform pulse compression processing on the radar echo signal after it is reflected by the vibrating target to obtain a range compression signal of the echo signal; a peak detection module, configured to perform peak detection on the range Doppler domain of the range compression signal to obtain a signal fundamental frequency of the range compression signal; a periodic filtering module, configured to perform periodic filtering on the range compression signal using a frequency domain periodic filter established according to the fundamental frequency of the signal to obtain a periodically filtered signal; a clutter removal module, configured to perform clutter estimation on the periodically filtered signal to obtain an estimated clutter signal; Subtracting the estimated clutter signal from the periodically filtered signal to obtain a clutter-removed signal; A low-pass filtering module, configured to perform differential interference on the signal after clutter removal to obtain a differential phase of the signal after clutter removal; and performing accumulation processing on the differential phase to obtain an inversion result of the radar vibration target deformation; The radar vibration target deformation inversion result is subjected to low-pass filtering to obtain the radar vibration target deformation inversion result after deformation noise is filtered out.

7. The device according to claim 6, characterized in that Also includes: Frequency domain periodic filter building block for: A frequency domain periodic filter is established based on the fundamental frequency of the signal according to the following formula: Among them, G(f d ) represents the frequency domain expression of the frequency domain periodic filter; f d represents the Doppler frequency; v represents the slow time; g(v) is the periodic filter; δ represents the impulse function; T = 1 / f v , T is the vibration target period, f v is the signal fundamental frequency; m is a preset constant, m=1,2,...; M is the sequence number of the adjacent cycles involved in filtering, M is manually set.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

9. A computer-readable storage medium, characterized in that The computer-readable 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.

10. A computer program product, characterized in that The computer program product comprises 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.

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