A High-Precision Measurement Method for Target Distance of FMCW Radar by Fusing Difference Frequency Signal and Integral Signal

Abstract

The present invention relates to the technical field of FMCW radar ranging, and specifically discloses a high-precision measurement method for the target distance of an FMCW radar that fuses the difference frequency signal and the integral signal. The steps include: obtaining the discrete difference frequency signal; performing frequency shift processing through FFT transformation; constructing a Toeplitz square matrix and performing eigenvalue decomposition for denoising to obtain the denoised and frequency-shifted discrete difference frequency signal; solving the first integral signal of the denoised and frequency-shifted difference frequency signal; constructing a generalized eigenvalue equation and performing generalized eigenvalue solution, and then obtaining the accurate distance of the measured target. The method of the present invention can obtain the theoretical solution of the difference frequency signal frequency while solving the fence effect and spectral leakage existing in the FFT transformation, so as to achieve the precise solution of the target distance; moreover, the present invention can directly use the frequency of the difference frequency signal to measure the target displacement, thus effectively solving the phase wrapping problem existing in the phase interference method.

Description

Technical Field

[0001] The present invention relates to the technical field of FMCW radar ranging, and particularly relates to a method for high-precision measurement of the target distance of an FMCW radar by fusing a difference frequency signal and an integral signal. Background Art

[0002] FMCW radar has unique advantages of non-contact, all-weather and all-time operation, and has been widely used in remote sensing mapping, traffic infrastructure monitoring and other fields for ranging and displacement measurement. Its measurement principle is to transmit and receive microwaves with a certain modulation bandwidth and perform mixing processing to obtain a difference frequency signal, perform FFT transformation on the difference frequency signal to obtain the target distance spectrum, so as to realize target ranging; further extract the phase of the measured target spectrum, and based on the phase interference method, the measurement of the target displacement can be realized. Among them, the distance resolution of the FMCW radar is limited by the modulation bandwidth, and the traditional FFT method has problems such as the fence effect and spectrum leakage. Therefore, how to improve the ranging accuracy of the FMCW radar mainly lies in optimizing the ranging algorithm and the accurate solution of the frequency.

[0003] The methods adopted in the existing methods are as follows: Fast Fourier Transform (FFT): The FFT algorithm can convert time-domain data into the frequency domain, so as to quickly and accurately analyze the frequency difference in the received echo signal, and then calculate the distance. FFT has the characteristics of fast calculation speed and high accuracy, and is suitable for real-time signal processing. Coherent accumulation: By accumulating and averaging the echo signals received multiple times, the signal-to-noise ratio of the signal can be significantly improved, thereby reducing the influence of noise on distance measurement and improving the ranging accuracy. Doppler filtering: Filter the frequency difference of the echo signal caused by the speed of non-target objects, extract the echo signal of the target object, and reduce the interference of other stray signals. Phase difference differential method: Calculate the distance by analyzing the change in the phase difference between the received echo signal and the transmitted signal. This method has the advantages of high accuracy and real-time performance. However, although the use of advanced signal processing algorithms (such as FFT, coherent accumulation, etc.) can improve the ranging accuracy, it will also increase the computational complexity and put higher requirements on the hardware performance. In some complex environments (such as severe multipath effects, strong electromagnetic interference, etc.), the above methods may not be able to completely eliminate errors, affecting the ranging accuracy, and the ranging of long-distance target objects may not be accurate enough.

[0004] In addition, in recent years, some scholars have utilized the prior condition of the sparsity of the target distance spectrum when measuring targets with microwave radar, and conducted research on target distance super-resolution based on compressive sensing. However, it should be noted that existing methods are all frequency optimization estimation methods and do not achieve the accurate solution of frequency. Moreover, in the implementation of compressive sensing, the stability of the reconstruction algorithm is easily affected by the signal sparsity and the observation matrix. To accurately recover sparse signals, it is required that the atoms of the observation matrix have low correlation. However, in practical applications, this ideal condition is often difficult to fully meet, which may lead to unstable reconstruction results. Moreover, microwave radar detectors are highly sensitive to the environment, such as terrain, buildings, etc., which will all affect their detection performance. In complex environments, the sparsity of the target distance spectrum may be interfered, thus affecting the accuracy of super-resolution research based on compressive sensing. In particular, under the condition of severe noise pollution, the implementation and effectiveness of existing methods are greatly reduced. Summary of the Invention

[0005] To solve the problems existing in the prior art, the present invention provides a high-precision measurement method for the target distance of an FMCW radar that fuses difference frequency signals and integral signals. While solving the fence effect and spectral leakage existing in the FFT transform, the method can obtain the theoretical solution of the difference frequency signal frequency, thereby enabling the accurate solution of the target distance; in addition, the present invention can directly utilize the frequency of the difference frequency signal to measure the target displacement, thus effectively solving the phase wrapping problem existing in the phase interference method. In particular, the present invention can achieve the absolute value measurement of the distance between the target and the microwave radar, and can also solve the problem that it is difficult to connect the displacement data of the monitored target after the current microwave radar displacement monitoring system is powered off, that is, the data island effect, and solves the problems mentioned in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: A high-precision measurement method for the target distance of an FMCW radar that fuses difference frequency signals and integral signals, comprising the following steps:

[0007] S1. Obtain discrete difference frequency signals;

[0008] S2. Perform frequency shift processing by FFT transform;

[0009] S3. Construct a Toeplitz square matrix, perform eigenvalue decomposition for denoising, and obtain the denoised and frequency-shifted discrete difference frequency signals;

[0010] S4. Solve the first integral signal of the denoised and frequency-shifted difference frequency signals;

[0011] S5. Construct a generalized eigenvalue equation, perform generalized eigenvalue solution, and then obtain the accurate distance of the measured target.

[0012] Preferably, in step S1, the FMCW radar signal transmitter generates and transmits a linear frequency modulated continuous wave with a starting frequency of f 0 , a modulation period of T s , and a modulation bandwidth of B. After being reflected by a target at a distance R, it is received by the radar signal receiver. After mixing and low-pass filtering, a difference frequency signal is obtained, which is

[0013] where A IF is the amplitude of the difference frequency signal corresponding to the measured target; the subscript IF represents the difference frequency signal; K = B / T s is the modulation slope, τ = 2R / c is the time delay corresponding to the measured target, c is the speed of light, and n(t) is the noise;

[0014] The above difference frequency signal S s (t) is discretely sampled at a sampling frequency F IF , and the number of sampling points is N = T s F s , and the discretely sampled difference frequency signal is:

[0015] Preferably, in step S2, the discretely sampled difference frequency signal S IF (i) is subjected to FFT transformation to obtain the distance spectrum of the measured target. The peak point of the distance spectrum is identified as f max . With the frequency band f max -Δf on the left side of the peak point as the reference frequency, a frequency shift function is constructed for frequency shift processing, specifically: At the same time, the reference distance is obtained as

[0016] Preferably, in step S3, based on the frequency-shifted signal , a corresponding Toeplitz matrix is constructed. The Toeplitz matrix is subjected to eigenvalue decomposition The eigenvalue matrix is corrected, and its maximum eigenvalue is retained, and the remaining eigenvalues are set to zero to obtain a new eigenvalue matrix Further, according to the eigenvector matrix and the eigenvalue matrix , a denoised and frequency-shifted Toeplitz matrix is constructed. Further, the denoised and frequency-shifted discrete difference frequency signal

[0017] Preferably, in step S4, the denoised and frequency-shifted discrete difference frequency signal is integrated once to obtain an integrated signal

[0018] Preferably, in step S5, according to the above integral signal Construct a corresponding Toeplitz matrix Establish the generalized eigenvalue equation as ν is the generalized eigenvector, and I is the matrix with all elements being 1;

[0019] Solve the generalized eigenvalue equation to obtain the eigenvalue matrix λ, and select its maximum eigenvalue λ max , and perform relationship conversion to obtain the accurate distance of the measured target

[0020] The beneficial effects of the present invention are as follows: The method of the present invention is different from traditional frequency estimation methods such as FFT, CZT, MUSIC, Zoom-FFT, and MUSIC. While solving the fence effect and spectrum leakage existing in FFT transformation, this method can obtain the theoretical solution of the difference frequency signal frequency. Moreover, compared with the differential signal of the difference frequency signal, the integral signal adopted by the method of the present invention is insensitive to noise, so as to realize the accurate solution of the target distance; the present invention can realize the absolute value measurement of the distance between the measured target and the FMCW radar. Moreover, this method can also be extended to the field of bridge health monitoring, which can solve the phase wrapping problem existing in the traditional phase interference method for measuring target displacement, and synchronously solve the data island effect problem caused by system power-off in actual engineering. Description of the Drawings

[0021] Figure 1 is the flowchart of the steps of the FMCW radar target distance high-precision measurement method for fusing difference frequency signals and integral signals in the embodiment of the present invention;

[0022] Figure 2 is the schematic diagram of the FFT result of the measured target distance in the embodiment of the present invention;

[0023] Figure 3 is the schematic diagram of the result of the Zoom-FFT method for the measured target distance in the embodiment of the present invention;

[0024] Figure 4 is the schematic diagram of the result of the CZT method for the measured target distance in the embodiment of the present invention;

[0025] Figure 5 is the schematic diagram of the result of fusing the difference frequency signal and its integral signal for the measured target distance in the embodiment of the present invention;

[0026] Figure 6 is the relationship diagram of the absolute error of the measured target distance and different noise contents in the embodiment of the present invention. Detailed Embodiments

[0027] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0028] Please refer to Figures 1-6 , the present invention provides a technical solution: a high-precision measurement method for the target distance of an FMCW radar that combines a difference frequency signal and an integral signal. As Figure 1 shown, it includes the following steps:

[0029] S1. Obtain a discrete difference frequency signal:

[0030] In step S1, the FMCW radar signal transmitter generates and transmits a linear frequency modulation continuous wave with a starting frequency of f 0 , a modulation period of T s , and a modulation bandwidth of B. After being reflected by a target at a distance of R, it is received by the radar signal receiver. After mixing and low-pass filtering, a difference frequency signal is obtained, which is

[0031] where A IF is the amplitude of the difference frequency signal corresponding to the measured target; the subscript IF represents the difference frequency signal; K = B / T s is the modulation slope, τ = 2R / c is the time delay corresponding to the measured target, c is the speed of light, and n(t) is the noise;

[0032] The above difference frequency signal S s (t) is discretely sampled at a sampling frequency F IF , and the number of sampling points is N = T s F s , and the discrete difference frequency signal after sampling is:

[0033] S2. Perform frequency shift processing by FFT transform:

[0034] In step S2, the discrete difference frequency signal S IF (i) is subjected to FFT transform to obtain the distance spectrum of the measured target. The peak point of the distance spectrum is identified as f max . Taking the frequency band f max -Δf on the left side of the peak point as the reference frequency, a frequency shift function is constructed for frequency shift processing. Specifically: At the same time, the reference distance is obtained as

[0035] S3. Construct a Toeplitz square matrix, perform eigenvalue decomposition for denoising, and obtain the denoised discrete difference frequency signal after frequency shift:

[0036] In step S3, based on the signal after frequency shift Construct the corresponding Toeplitz matrix Perform eigenvalue decomposition on the Toeplitz matrix For the eigenvalue matrix Make corrections, retain its maximum eigenvalue, and set the remaining eigenvalues to zero to obtain a new eigenvalue matrix Further, according to the eigenvector matrix and the eigenvalue matrix Construct the denoised Toeplitz matrix after frequency shift Further obtain the denoised discrete difference frequency signal after frequency shift

[0037] S4. Solve the first integral signal of the denoised difference frequency signal after frequency shift:

[0038] In step S4, for the denoised discrete difference frequency signal after frequency shift Perform the first integral to obtain the integral signal

[0039] S5. Construct a generalized eigenvalue equation, perform generalized eigenvalue solving, and then obtain the accurate distance of the measured target:

[0040] In step S5, according to the above integral signal Construct the corresponding Toeplitz matrix Establish the generalized eigenvalue equation as is the generalized eigenvector, and I is the matrix with all elements being 1;

[0041] Solve the generalized eigenvalue equation to obtain the eigenvalue matrix λ, select its maximum eigenvalue λ max , and perform relationship conversion to obtain the accurate distance of the measured target

[0042] Example verification

[0043] The high-precision FMCW radar target distance measurement method that fuses the difference frequency signal and the integral signal in the embodiment of the present invention is used for example verification, as follows:

[0044] Let the FMCW radar transmit a frequency-modulated continuous wave signal with a starting frequency of f 0 = 9.4 GHz; a modulation bandwidth of B = 200 MHz; a modulation period of T s = 0.5 ms. The measured target is located at R = 10.12345 m. The transmitted signal is reflected by the measured target and then received. Assume the sampling frequency fs = 4 MHz, the corresponding number of sampling points is N = 2000, and then the discrete difference frequency signal can be obtained.

[0045] Performing an N-point FFT transformation directly on the obtained discrete difference frequency signal can obtain the distance spectrum of the target to be measured. The result is as Figure 2 shown. The distance error of the target to be measured is relatively large; further using the Zoom-FFT method for spectrum refinement, the refined target distance spectrum is as Figure 3 shown. After refinement, there is still an error in the distance of the target to be measured; secondly, according to the peak point f of the distance spectrum of the target to be measured max , select the frequency band range as [f max -Δf f max +Δf], refine the signal within the selected frequency band, and the refinement multiple is 10. The distance spectrum of the target to be measured based on the CZT method is obtained. The result is as Figure 4 shown. There is still an error in the identified distance of the target to be measured;

[0046] Finally, perform frequency shift processing with f max -Δf as the reference frequency, perform denoising processing based on eigenvalue decomposition, perform an integration process on the denoised signal to obtain the first integration signal, construct a generalized eigenvalue equation and solve the generalized eigenvalue, and the accurate solution of the beat frequency is 2.69958664e+04, corresponding to the distance of the target to be measured is 10.1234499 m. The result is as Figure 5 shown.

[0047] Assume that the signal-to-noise ratio is Gaussian white noise with a mean of 0 and a variance of σ 2 . Analyze the influence of the signal-to-noise ratio SNR on the frequency solution. At each signal-to-noise ratio, 100 independent Monte Carlo experiments are carried out. Add Gaussian white noise with different degrees (1% - 30%), and obtain the absolute error of the target distance recognition at different signal-to-noise ratios. The result is as Figure 6 shown. When adding 25% Gaussian white noise, due to the influence of noise, the error of target distance recognition is greater than the mm level; as the noise content decreases, the distance recognition error gradually decreases, and the performance of the FMCW radar target distance accurate solution method that fuses the difference frequency signal and its integration signal is excellent.

[0048] In the embodiments provided by the present invention, it should be noted that in this text, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such a process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or device including the said element.

[0049] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms "a", "the" and "said" used in the embodiments of the present invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0050] It should be understood that the term "and / or" used herein is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this text generally represents an "or" relationship between the associated objects before and after.

[0051] Depending on the context, the word "if" as used herein can be interpreted as "when", "while" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detecting (stated condition or event)" can be interpreted as "when determined", "in response to determining", "when detecting (stated condition or event)", or "in response to detecting (stated condition or event)".

[0052] The "first / second" mentioned in the embodiments is only to distinguish similar objects and does not represent a specific order for the objects. It can be understood that the "first / second" can be interchanged in a specific order or sequence when permitted. It should be understood that the objects distinguished by "first / second" can be interchanged under appropriate circumstances so that the embodiments described herein can be implemented in an order other than those illustrated or described herein.

[0053] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent substitution on some of the technical features. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A high-precision FMCW radar target distance measurement method that combines a difference frequency signal and an integral signal, characterized in that: The steps include: S1, obtaining discrete difference frequency signal; S2, FFT transformation for frequency shift processing; S3, construct a Toeplitz matrix, perform eigenvalue decomposition and denoising, and obtain a discrete difference frequency signal after denoising and frequency shifting; S4, solving the primary integral signal of the difference frequency signal after denoising and frequency shifting; S5. Construct a generalized characteristic equation, solve the generalized eigenvalue, and then obtain the accurate distance of the measured target.

2. The method for high-precision measurement of FMCW radar target distance by fusing difference frequency signal and integral signal according to claim 1 is characterized in that: In step S1, the FMCW radar signal transmitter generates and transmits a signal with a starting frequency of f0 and a modulation period of T. s , the linear frequency modulated continuous wave with modulation bandwidth B is reflected by the target at distance R and received by the radar signal receiver. After mixing and low-pass filtering, the difference frequency signal is obtained, which is t∈(0,T s ); Among them, A IF is the amplitude of the difference frequency signal corresponding to the target under test; the subscript IF represents the difference frequency signal; K = B / T s is the modulation slope, τ = 2R / c is the time delay corresponding to the target under test, c is the speed of light, and n(t) is the noise; With sampling frequency F s For the above difference frequency signal S IF (t) Discrete sampling is performed, and the number of sampling points is N = T s F s , the difference frequency signal after discrete sampling is obtained as: i∈(1,N).

3. The method for high-precision measurement of FMCW radar target distance by fusing difference frequency signal and integral signal according to claim 1, characterized in that: In step S2, the discretely sampled difference frequency signal S IF (i) Perform FFT transformation to obtain the range spectrum of the target being measured, and identify the peak point of the range spectrum as f max , with the frequency band f on the left side of the peak point max -Δf is the reference frequency, construct the frequency shift function Perform frequency shift processing, specifically: At the same time, the reference distance is 4. The method for high-precision measurement of FMCW radar target distance by fusing difference frequency signal and integral signal according to claim 1, characterized in that: In step S3, based on the frequency-shifted signal Construct the corresponding Toeplitz matrix Perform eigenvalue decomposition on a Toeplitz matrix For the eigenvalue matrix Make corrections, retain its maximum eigenvalue, set the remaining eigenvalues ​​to zero, and obtain a new eigenvalue matrix Further according to the eigenvector matrix and the eigenvalue matrix Construct the Toeplitz matrix after denoising and frequency shifting Further obtain the discrete difference frequency signal after denoising and frequency shifting 5. The method for high-precision measurement of FMCW radar target distance by fusing difference frequency signal and integral signal according to claim 1, characterized in that: In step S4, the discrete difference frequency signal after denoising and frequency shifting is Perform an integration to get the integral signal 6. The FMCW radar target distance high-precision measurement method by fusing the difference frequency signal and the integral signal according to claim 1 is characterized in that: In step S5, according to the above integrated signal Construct the corresponding Toeplitz matrix The generalized characteristic equation is established as ν is a generalized eigenvector, and I is a matrix whose elements are all 1; Solve the generalized characteristic equation to obtain the eigenvalue matrix λ, and select its maximum eigenvalue λ max , and perform relationship conversion to obtain the precise distance of the measured target

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