RIS-assisted FMCW radar non-line-of-sight target distance and speed measurement method

Abstract

The invention relates to the technical field of non-line-of-sight target detection, in particular to a non-line-of-sight target distance and speed measurement method for an RIS-assisted FMCW radar, and the method comprises the steps: constructing a transmitting and receiving signal model of the RIS-assisted FMCW radar, and analyzing the frequency difference between transmitting and receiving signals. And optimizing RIS phase shift matrix parameters. A radar signal is processed, a distance-Doppler spectrum of a target is obtained from radar original echoes, and then distance and speed information of a non-line-of-sight target is obtained. And performing target detection on the distance-Doppler spectrogram by using two-dimensional constant false alarm rate (2D-CFAR), and obtaining the specific position of the target according to the distance and speed information of the target. According to the invention, effective estimation of the distance and speed parameters of the non-line-of-sight target can be realized, the characteristics of radar echo signals at different distances and speeds are obtained, a powerful guarantee is provided for a target detection technology in a non-line-of-sight area, and thus the accuracy of target distance measurement and speed measurement is improved.

Description

Technical Field

[0001] The invention relates to the technical field of non-line-of-sight target detection, and in particular to a non-line-of-sight target ranging and speed measuring method assisted by a FMCW radar. Background Art

[0002] The non-line-of-sight target detection technology based on FMCW radar is mainly based on the non-line-of-sight multipath propagation characteristics of electromagnetic waves such as reflection and diffraction on the surface of media such as walls, and can effectively detect, locate and identify obscured targets in visual blind spots such as urban street corners and vehicle obstruction areas, and has great application value in the fields of intelligent driving. The geometric position relationship between the FMCW radar, obstructions and targets will have a strong correlation with the amplitude-frequency characteristics of the echo signal. Therefore, how to establish a propagation echo model of non-line-of-sight targets based on the actual detection scenario is an important prerequisite for realizing FMCW radar non-line-of-sight target detection.

[0003] Reconfigurable Intelligent Surface (RIS) is a low-cost, easy-to-deploy electromagnetic surface structure that can provide a virtual line-of-sight path and enhance beam strength in a directional manner. RIS-assisted FMCW radar can fully utilize the spatial freedom of RIS to "move" shielded targets in non-line-of-sight areas to the line-of-sight area, significantly improving the range and speed measurement capabilities of non-line-of-sight targets in non-line-of-sight scenarios.

[0004] The existing non-line-of-sight target detection method of RIS-assisted radar often requires the joint optimization of the transmitting / receiving unit and the RIS reflection unit, and does not consider FMCW, so the accuracy of target ranging and speed measurement is relatively poor. Summary of the invention

[0005] In order to solve the technical problem of poor accuracy in target ranging and speed measurement, the present invention proposes a non-line-of-sight target ranging and speed measurement method using RIS-assisted FMCW radar, which can achieve high-precision positioning of targets in non-line-of-sight areas and estimate target distance and speed parameters.

[0006] The present invention provides a non-line-of-sight target ranging and speed measuring method using a RIS-assisted FMCW radar, the method comprising:

[0007] Step S1, constructing a transmitting and receiving signal model of the RIS-assisted FMCW radar, and processing the received signal to obtain the distance and speed information of the target;

[0008] Step S2, in the non-line-of-sight scenario, combined with the known building scene layout information, based on the location information of the virtual target obtained in step S1, a path gain maximization method based on gradient ascent is proposed to optimize the RIS phase shift matrix parameters;

[0009] Step S3, based on step S2, according to the echo signal returned by the intelligent metasurface device received by the detection radar, the radar signal is processed, the range-Doppler spectrum of the target is obtained from the original radar echo, and then the distance and speed information of the non-line-of-sight target is obtained;

[0010] Step S4: Based on step S3, 2D-CFAR is used to perform target detection on the obtained range-Doppler spectrum, and the specific position detection of the target is obtained according to the distance and speed information of the target.

[0011] Optionally, the constructing of the transmitting and receiving signal model of the RIS-assisted FMCW radar and processing the received signal to obtain the distance and speed information of the target include:

[0012] The simulation and measured scenes are both set to use millimeter wave radar. The signal emitted by the radar is a linear frequency modulation signal. The initial frequency of the radar is set to f0, the frequency modulation bandwidth is set to B, and the frequency modulation period is set to T. m , then at time t(0≤t≤T m )The frequency of the transmitted signal is expressed as f t (t)=f0+μt; where μ=B / T m Indicates the frequency modulation slope, and finds the phase of the transmitted signal based on the frequency Therefore, the transmitted signal is expressed as:

[0013]

[0014] Among them, A t is the transmitted signal amplitude;

[0015] set up and They are the channels from the transmitter to the RIS and from the RIS to the receiver respectively; the RIS processes the phase offset of the incident signal, that is, At time t0, the received signal is expressed as:

[0016]

[0017] Where τ is the signal delay, is zero-mean additive Gaussian noise, diag represents the extraction of diagonal elements from the phase offset matrix, N represents the number of elements in RIS, represents each element in the phase offset matrix, θ represents the angle between the signal and the RIS normal direction; the variance of the noise The receiver noise factor N F and receiver bandwidth B, the corresponding formula is:

[0018]

[0019] Where k is the Boltzmann constant, T0 is the receiver reference temperature, T0 = 290K; K represents the loss of the signal during space propagation, expressed as:

[0020]

[0021] Where c is the speed of light, λ is the signal operating wavelength, σ is the RCS of the target, G t and G r are the radar transmitting and receiving antenna gains, L s It is the combined loss of radar transmission and reception;

[0022] The electromagnetic wave signals at the transmitting end and the receiving end are mixed into intermediate frequency signals through a mixer; for linear frequency modulation, the frequency of the difference frequency carries the distance information of the target; at time t0, the receiving signal is mixed with the transmitting signal, and the formula corresponding to the beat signal is:

[0023]

[0024] Optionally, in the non-line-of-sight scenario, combined with known building scene layout information, based on the location information of the virtual target obtained in step S1, a path gain maximization method based on gradient ascent is proposed to optimize the RIS phase shift matrix parameters, including:

[0025] To obtain θ=[θ1,…,θ N ] T The optimal value of the path gain maximization criterion is proposed, that is, maximizing the gain of the signal transmission path ψ(θ)=tr{(gΦh) H (gΦh)}, then the optimization of θ is expressed as:

[0026]

[0027] Let θ (l) Represents the value of θ obtained at the lth iteration. By iteratively searching for the specified step size in the positive direction of the gradient corresponding to the current point on the function, the following steps are performed:

[0028] Set an appropriate initial value for θ;

[0029] Given a value of θ, the corresponding gradient is calculated as The corresponding formula is:

[0030]

[0031] Among them, e n is a basis vector whose nth term is 1 and other terms are zero; Further expressed as:

[0032]

[0033] To update the parameters, the corresponding formula is:

[0034] Among them, α is the step size factor;

[0035] Repeat the above steps until convergence accuracy is reached.

[0036] Optionally, based on step S2, according to the echo signal returned by the intelligent metasurface device received by the detection radar, the radar signal is processed, and the range-Doppler spectrum of the target is obtained from the original radar echo, and then the distance and speed information of the non-line-of-sight target is obtained, including:

[0037] After obtaining the optimal θ using the path gain maximization method based on gradient ascent, the distance and speed of the target are solved; the intermediate frequency signal is a sine wave with an amplitude that changes with time, its frequency is constant, and the initial phase It is the phase difference between the transmitted signal and the echo signal corresponding to the starting time of the intermediate frequency signal. The corresponding formula is:

[0038]

[0039] When the radar detects the target at a distance of R, its intermediate frequency signal is expressed as:

[0040]

[0041] where f b =μτ=μ2R / c;

[0042] For the frequency difference f of the intermediate frequency signal b , obtained by fast Fourier transform of its intermediate frequency signal; the distance R of the detected target is expressed as:

[0043]

[0044] Distance frequency f b It is estimated by performing FFT operation on the beat signal sequence in each scanning cycle;

[0045] For a moving object, the distance FFT of each frequency modulated continuous wave will have a peak at the same position, but its phase is different, that is, the spectrum has peaks with different phases at the same position, and the phase difference is:

[0046]

[0047] The formula for the target speed is:

[0048]

[0049] When the phase difference |Δφ| is less than π, it is unambiguous. Combining the phase difference and speed, we get:

[0050]

[0051] Indirectly, the maximum speed meets V max =λ / 4T c .

[0052] Optionally, based on step S3, using 2D-CFAR to perform target detection on the obtained range-Doppler spectrum, and obtaining the specific position detection of the target according to the distance and speed information of the target, includes:

[0053] The range-Doppler spectrum of the target obtained from the original radar echo is used for constant false alarm detection, and the CA CFAR method with a two-dimensional sliding reference window is used to identify RDM;

[0054] Assume that there is a square law detector, and take the square value of the signal amplitude received in the RDM as the random variable in the random model, and use X m,w Indicated by, where m is the target distance index and w is the Doppler frequency index; the random variable in the test unit is always represented by Y, and the calculated threshold is described by parameter T; the target detection process is based on hypothesis testing, where H0 is the hypothesis in the case of noise, and H1 describes the superposition of the target and noise;

[0055] The target detection process is expressed as:

[0056]

[0057] Under the H0 hypothesis, the random variable in the test unit is assumed to be a noise signal, represented by Y0; in the square-law detector, the random variable Y0 is exponentially distributed, the probability density function is represented by pdf, the average noise power is represented by ζ, and the corresponding formula of the square-law detector is:

[0058]

[0059] The corresponding formula according to the Neyman-Pearson criterion is:

[0060] T=β·ζ

[0061] The average noise power E{X|H0}=ζ, which controls the false alarm probability P fa The threshold factor is β;

[0062] The 2D cell-averaged CFAR is the first method to estimate the time-dependent noise floor within the local environment and within a reference window; in this case, the unknown local noise expectation E{X|H0}=ζ is adaptively estimated based on the arithmetic mean of all random variables within the 2D reference window;

[0063]

[0064] In order to meet the required P fa , the decision threshold is:

[0065]

[0066] where β CA is the threshold factor, is the estimated average noise floor of the arithmetic mean; the threshold factor β CA Depends on the required P fa and the number of cells within the reference window used for noise power estimation;

[0067]

[0068] The present invention has the following beneficial effects:

[0069] The present invention discloses a non-line-of-sight target ranging and speed measurement method of RIS-assisted FMCW radar. First, a transmitting and receiving signal model of the RIS-assisted FMCW radar is constructed to analyze the frequency difference between the transmitting and receiving signals. Secondly, a path gain maximization (PGM) method based on gradient ascent is proposed to optimize the RIS phase shift matrix parameters. Then, the radar signal is processed, and the range-Doppler spectrum of the target is obtained from the original radar echo, thereby obtaining the distance and speed information of the non-line-of-sight target. Finally, 2D-CFAR is used to detect the obtained range-Doppler spectrum, and the specific position of the target is obtained according to the distance and speed information of the target. The method of the present invention can realize the effective estimation of the distance and speed parameters of the non-line-of-sight target, obtain the characteristics of the radar echo signal at different distances and speeds, and provide a strong guarantee for the target detection technology in the non-line-of-sight area, thereby improving the accuracy of the target ranging and speed measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0071] Figure 1 It is a flow chart of a non-line-of-sight target ranging and speed measurement method of a RIS-assisted FMCW radar of the present invention;

[0072] Figure 2 The overall flow chart of the algorithm provided by the present invention;

[0073] Figure 3 A schematic diagram of the signal processing steps for target detection provided by the present invention;

[0074] Figure 4 A schematic diagram of a one-dimensional distance FFT provided by the present invention;

[0075] Figure 5 This is a schematic diagram of the distance dimension FFT provided by the present invention;

[0076] Figure 6 The range Doppler spectrum provided by the present invention;

[0077] Figure 7 The time domain diagram of the intermediate frequency signal provided by the present invention;

[0078] Figure 8 This is the distance FTF result diagram provided by the present invention;

[0079] Fig. 9 The velocity dimension FFT range Doppler spectrum provided by the present invention;

[0080] Fig.10 This is the CFAR result diagram provided by the present invention. DETAILED DESCRIPTION

[0081] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the technical solutions proposed by the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0082] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0083] The embodiment of the present invention is a MATLAB simulation example, referring to Figure 1 , shows the process of some embodiments of a non-line-of-sight target ranging and speed measurement method of a RIS-assisted FMCW radar according to the present invention. The non-line-of-sight target ranging and speed measurement method of the RIS-assisted FMCW radar comprises the following steps:

[0084] Step S1, constructing a transmitting and receiving signal model of the RIS-assisted FMCW radar, and processing the received signal to obtain the distance and speed information of the target.

[0085] In some embodiments, a transmission and reception signal model of a RIS-assisted FMCW radar is constructed, and then information is processed through the received signal to obtain the distance and speed information of the target.

[0086] It should be noted that the distance and speed information of the target are updated in sequence. The distance and speed information of the target in step S1 only generally describes that the echo signal model can obtain the speed and distance information of the target; the distance and speed of the target can be specifically calculated through step S3, and the position of the target can be simulated through FFT, but it may not be clear enough at this time and may contain clutter signals; the clear position of the target can be obtained through step S4.

[0087] As an example, step S1 may include the following steps:

[0088] The simulation and measured scenes are both set to use millimeter wave radar. The signal emitted by the radar is a linear frequency modulation signal. The initial frequency of the radar is set to f0, the frequency modulation bandwidth is set to B, and the frequency modulation period is set to T. m , then at time t(0≤t≤T m )The frequency of the transmitted signal can be expressed as f t (t)=f0+μt. Among them, μ=B / T m Indicates the frequency modulation slope, and finds the phase of the transmitted signal based on the frequency Therefore, the transmitted signal can be expressed as:

[0089]

[0090] Among them, A t is the transmit signal amplitude.

[0091] set up and They are the channels from the transmitter to the RIS and from the receiver to the RIS. The RIS processes the phase offset of the incident signal, namely Therefore, at time t0, the received signal can be expressed as:

[0092]

[0093] Where τ is the signal delay, is zero-mean additive Gaussian noise, diag represents the extraction of diagonal elements from the phase offset matrix, N represents the number of elements in RIS, represents each element in the phase offset matrix, and θ represents the angle between the signal and the RIS normal direction. The receiver noise factor N F and receiver bandwidth B, the corresponding formula is:

[0094]

[0095] Where k is the Boltzmann constant, T0 is the receiver reference temperature, T0 = 290 K. K represents the loss of the signal during space propagation, which can be expressed as:

[0096]

[0097] Where c is the speed of light, λ is the signal operating wavelength, σ is the RCS of the target, G t and G r are the radar transmitting and receiving antenna gains respectively. s is the radar transmission and reception comprehensive loss, which includes the transmission loss L t , receiving loss L r , two-way atmospheric loss L dq wait.

[0098] The electromagnetic wave signals at the transmitting end and the receiving end are mixed into intermediate frequency signals through the mixer. For linear frequency modulation, the frequency of the difference frequency carries the distance information of the target. At time t0, the receiving signal is mixed with the transmitting signal, and the beat signal is:

[0099]

[0100] After obtaining the intermediate frequency signal, the intermediate frequency signal is processed by fast Fourier transform to obtain the frequency f of the intermediate frequency signal. b , and then in the subsequent steps, the distance R from the target to the radar and the speed v of the target are calculated. In actual measurement, the original echo signal collected by the radar is the intermediate frequency signal after mixing, for example Figure 7 What is represented is the intermediate frequency time domain diagram. Therefore, by directly performing FFT processing on the original echo signal of each cycle of the radar, the distance information of the target can be obtained. This step is called distance FFT, such as Figure 4 The one-dimensional distance FFT is shown as Figure 5 The distance dimension FFT is shown in Figure 1. The spectrum matrix of the distance FFT result is as follows: Figure 8 In order to optimize the RIS phase shift matrix parameters during the calculation process, a path gain maximization method based on gradient ascent is proposed. The overall algorithm flow chart is shown in Figure 2 shown.

[0101] Step S2: In the non-line-of-sight scenario, based on the known building scene layout information and the location information of the virtual target obtained in step S1, a path gain maximization method based on gradient ascent is proposed to optimize the RIS phase shift matrix parameters.

[0102] In some embodiments, in a non-line-of-sight scenario, combined with known building scene layout information, based on the location information of the virtual target obtained in step S1, a path gain maximization method based on gradient ascent is proposed to optimize the RIS phase shift matrix parameters.

[0103] It should be noted that, through the construction of the scene model in step S1, the approximate position of the target can be obtained based on the obtained echo signal, and then the parameters can be further optimized to make the transmitted signal more concentrated and the position of the target can be observed more clearly.

[0104] As an example, Φ represents a phase shift matrix parameter, and θ in the phase shift matrix is ​​mainly optimized, where θ represents an angle between the signal and the RIS normal. Specifically, step S2 may include the following steps:

[0105] To obtain θ=[θ1,…,θ N ] T The optimal value of the path gain maximization criterion is proposed, that is, maximizing the gain of the signal transmission path ψ(θ)=tr{(gΦh) H (gΦh)}, then the optimization of θ can be expressed as:

[0106]

[0107] Let θ (l) The value of θ obtained in the lth iteration is close to the local maximum of the function by iteratively searching the specified step size in the positive direction of the gradient corresponding to the current point on the function. The execution steps are as follows:

[0108] The first step is to set an appropriate initial value for θ.

[0109] The second step is to calculate the corresponding gradient given the value of θ: The corresponding formula is:

[0110]

[0111] Among them, e n is a basis vector with the nth term being 1 and the rest being zero. Since the trace of matrix multiplication is invariant under cyclic permutations, It can be further expressed as:

[0112]

[0113] The third step is to update the parameters, and the corresponding formula is:

[0114] Among them, α is the step size factor.

[0115] In the fourth step, the second and third steps in the example of step S2 are repeated until the convergence accuracy is reached. It is necessary to select a suitable α: if α is too small, the convergence speed is too slow and the time cost increases. If α is large, the convergence speed will increase, but it may oscillate around the optimal value and fail to converge.

[0116] The optimization of the RIS phase shift matrix parameters in this embodiment ends here. The overall algorithm flow chart is as follows: Figure 3 Next, the target's range-Doppler spectrum is obtained from the radar's original echo, and then the range and speed information of the non-line-of-sight target is obtained, so that the target's range and speed are calculated.

[0117] Step S3, based on step S2, processes the radar signal according to the echo signal sent back by the intelligent metasurface device received by the detection radar, obtains the range-Doppler spectrum of the target from the original radar echo, and then obtains the distance and speed information of the non-line-of-sight target.

[0118] In some embodiments, based on the RIS phase shift matrix parameters optimized in step S2, the radar signal is processed according to the echo signal transmitted back by the intelligent metasurface device received by the detection radar, and the range-Doppler spectrum of the target is obtained from the original radar echo, thereby obtaining the distance and speed information of the non-line-of-sight target, wherein the range-Doppler spectrum is a spectrum obtained by simulation after FFT of the echo signal.

[0119] It should be noted that after optimizing the RIS phase shift matrix parameters, the signal strength of the echo signal can be increased.

[0120] As an example, step S3 may include the following steps:

[0121] After obtaining the optimal θ using the path gain maximization method based on gradient ascent, the distance and speed of the target are solved. The intermediate frequency signal is a sine wave whose amplitude varies with time, its frequency is constant, and the initial phase It is the phase difference between the transmitted signal and the echo signal corresponding to the starting time of the intermediate frequency signal. The corresponding formula is:

[0122]

[0123] When the radar detects the target at a distance of R, its intermediate frequency signal can be expressed as:

[0124]

[0125] where f b =μτ=μ2R / c.

[0126] For the frequency difference f of the intermediate frequency signal b, which is generally obtained by performing Fast Fourier Transform (FFT) on the intermediate frequency signal. Therefore, the distance R of the detected target can be expressed as:

[0127]

[0128] Distance frequency f b It can be estimated by performing FFT operation on the beat signal sequence in each scanning cycle.

[0129] For a moving object, the distance FFT of each frequency modulated continuous wave will have a peak at the same position, but its phase is different, that is, the spectrum has peaks with different phases at the same position. The phase difference is:

[0130]

[0131] Therefore, the speed of the target can be derived as:

[0132]

[0133] Since this speed measurement method is based on phase difference, there will be ambiguity. It is usually unambiguous only when the phase difference |Δφ| is less than π. Combining the phase difference and speed, we can get:

[0134]

[0135] Indirectly, the maximum speed meets V max =λ / 4T c .

[0136] Step S4: Based on step S3, 2D-CFAR is used to perform target detection on the obtained range-Doppler spectrum, and the specific position detection of the target is obtained according to the distance and speed information of the target.

[0137] In some embodiments, based on step S3, 2D-CFAR is used to perform target detection on the obtained range-Doppler spectrum, and the specific position detection of the target is obtained according to the distance and speed information of the target.

[0138] As an example, step S4 may include the following steps:

[0139] The range-Doppler spectrum of the target obtained from the original radar echo is as follows: Figure 6 The constant false alarm detection is performed as shown in the embodiment of the present invention. The CA CFAR method of the two-dimensional sliding reference window is used to identify the RDM. The specific signal processing steps of the target detection are as follows Figure 3 shown.

[0140] Assume that there is a square law detector, and take the square value of the signal amplitude received in the RDM as the random variable in the random model, and use X m,w , where m is the target distance index and w is the Doppler frequency index. The random variable in the test unit is always denoted by Y, and the calculated threshold is described by the parameter T. The target detection process is based on hypothesis testing, where H0 is the hypothesis in the case of noise, and H1 describes the situation where the target is superimposed with noise.

[0141] The general target detection process can be expressed as:

[0142]

[0143] Under the H0 hypothesis, the random variable in the test unit is assumed to be a noise signal, represented by Y0. In the square-law detector, the random variable Y0 is exponentially distributed, the probability density function is represented by pdf, the average noise power is represented by ζ, and the corresponding formula of the square-law detector is:

[0144]

[0145] The corresponding formula according to the Neyman-Pearson criterion is:

[0146] T=β·ζ

[0147] The average noise power E{X|H0}=ζ, which controls the false alarm probability P fa The threshold factor is β.

[0148] The 2D cell-averaged CFAR is the first method to estimate the time-dependent noise floor within the local environment and within a reference window. In this case, the unknown local noise expectation E{X|H0}=ζ is adaptively estimated based on the arithmetic mean of all random variables within the 2D reference window.

[0149] therefore,

[0150] In order to meet the required P fa , the decision threshold can be:

[0151]

[0152] where β CA is the threshold factor, is the estimated average noise floor of the arithmetic mean. Threshold factor β CA Depends on the required P fa and the number of cells within the reference window used for noise power estimation;

[0153]

[0154] In the embodiment of the present invention, a simulation analysis is carried out in the CFAR detection algorithm part, and the FFT range Doppler spectrum of the velocity dimension can be obtained through the simulation experiment, such as Fig. 9 The CFAR result is shown in Fig.10 It integrates range information (range Doppler matrix, RDM) and velocity information. Such a chart usually consists of two two-dimensional planes, one plane showing the range information and the other plane showing the velocity information. In a three-dimensional representation, the two planes are stacked vertically to form a stereo image, where changes in color or brightness indicate signal strength or the presence of a target.

[0155] In the embodiment of the present invention, the main parameters of the radar are as follows: operating frequency f c =24GHz, c=3×10 8 m / s; the peak power in the FMCW waveform is 100W, the number of transmitting antennas and receiving antennas is 1, and the maximum detection distance of the radar target is R max =200, distance resolution r res =1, the maximum speed v of the radar to detect the target max =20, number of signal chirps N d =128, ADC sampling points N r =256, radar cross section RCS = 1, noise factor n f =3dB, radar loss is 6dB.

[0156] In summary, in order to optimize the RIS phase shift parameters, the present invention proposes a path gain maximization method based on gradient ascent. According to a series of non-line-of-sight target estimation performance curves obtained by simulation, the range-Doppler spectrum of non-line-of-sight targets under different conditions is analyzed to verify the performance of the algorithm. In the entire target detection process, the effective estimation of the distance and speed parameters of the non-line-of-sight target can be achieved, and the characteristics of the radar echo signal at different distances and speeds can be intuitively observed from a series of simulation diagrams. The range Doppler diagram in the simulation diagram can evaluate the performance of the radar system, including target detection, speed estimation and range resolution, and provide a strong guarantee for target detection technology in non-line-of-sight areas.

[0157] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features can be replaced by equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A non-line-of-sight target ranging and speed measurement method using RIS-assisted FMCW radar, characterized in that: The following steps are involved: Step S1, constructing a transmitting and receiving signal model of the RIS-assisted FMCW radar, and processing the received signal to obtain the distance and speed information of the target; Step S2, in the non-line-of-sight scenario, combined with the known building scene layout information, based on the location information of the virtual target obtained in step S1, a path gain maximization method based on gradient ascent is proposed to optimize the RIS phase shift matrix parameters; Step S3, based on step S2, according to the echo signal returned by the intelligent metasurface device received by the detection radar, the radar signal is processed, the range-Doppler spectrum of the target is obtained from the original radar echo, and then the distance and speed information of the non-line-of-sight target is obtained; Step S4: Based on step S3, 2D-CFAR is used to perform target detection on the obtained range-Doppler spectrum, and the specific position detection of the target is obtained according to the distance and speed information of the target.

2. The non-line-of-sight target ranging and speed measurement method of a RIS-assisted FMCW radar according to claim 1 is characterized in that: The construction of the transmission and reception signal model of the RIS-assisted FMCW radar and processing of information through the received signal to obtain the distance and speed information of the target include: The simulation and measured scenes are both set to use millimeter wave radar. The signal emitted by the radar is a linear frequency modulation signal. The initial frequency of the radar is set to f0, the frequency modulation bandwidth is set to B, and the frequency modulation period is set to T. m , then at time t(0≤t≤T m )The frequency of the transmitted signal is expressed as f t (t)=f0+μt; where μ=B / T m Indicates the frequency modulation slope, and finds the phase of the transmitted signal based on the frequency Therefore, the transmitted signal is expressed as: Among them, A t is the transmitted signal amplitude; set up and They are the channels from the transmitter to the RIS and from the RIS to the receiver respectively; the RIS processes the phase offset of the incident signal, that is, At time t0, the received signal is expressed as: Where τ is the signal delay, is zero-mean additive Gaussian noise, diag represents the extraction of diagonal elements from the phase offset matrix, N represents the number of elements in RIS, represents each element in the phase offset matrix, θ represents the angle between the signal and the RIS normal direction; the variance of the noise The receiver noise factor N F and receiver bandwidth B, the corresponding formula is: Where k is the Boltzmann constant, T0 is the receiver reference temperature, T0 = 290K; K represents the loss of the signal during space propagation, expressed as: Where c is the speed of light, λ is the signal operating wavelength, σ is the RCS of the target, G t and G r are the radar transmitting and receiving antenna gains, L s It is the combined loss of radar transmission and reception; The electromagnetic wave signals at the transmitting end and the receiving end are mixed into intermediate frequency signals through a mixer; for linear frequency modulation, the frequency of the difference frequency carries the distance information of the target; at time t0, the receiving signal is mixed with the transmitting signal, and the formula corresponding to the beat signal is:

3. The non-line-of-sight target ranging and speed measurement method of a RIS-assisted FMCW radar according to claim 1, characterized in that: In the non-line-of-sight scenario, combined with the known building scene layout information, based on the location information of the virtual target obtained in step S1, a path gain maximization method based on gradient ascent is proposed to optimize the RIS phase shift matrix parameters, including: To obtain θ=[θ1,…,θ N ] T The optimal value of the path gain maximization criterion is proposed, that is, maximizing the gain of the signal transmission path ψ(θ)=tr{(gΦh) H (gΦh)}, then the optimization of θ is expressed as: Let θ (l) Represents the value of θ obtained at the θth iteration, by iteratively searching for the specified step size in the positive direction of the gradient corresponding to the current point on the function, and performing the following steps: Set an appropriate initial value for θ; Given a value of θ, the corresponding gradient is calculated as The corresponding formula is: Among them, e n is a basis vector whose nth term is 1 and other terms are zero; Further expressed as: Update the parameters, and the corresponding formula is: Among them, α is the step size factor; Repeat the above steps until convergence accuracy is reached.

4. The non-line-of-sight target ranging and speed measurement method of a RIS-assisted FMCW radar according to claim 1, characterized in that: Based on step S2, according to the echo signal returned by the intelligent metasurface device received by the detection radar, the radar signal is processed, and the range-Doppler spectrum of the target is obtained from the original radar echo, and then the distance and speed information of the non-line-of-sight target is obtained, including: After obtaining the optimal θ using the path gain maximization method based on gradient ascent, the distance and speed of the target are solved; the intermediate frequency signal is a sine wave with an amplitude that changes with time, its frequency is constant, and the initial phase It is the phase difference between the transmitted signal and the echo signal corresponding to the starting time of the intermediate frequency signal. The corresponding formula is: When the radar detects the target at a distance of R, its intermediate frequency signal is expressed as: among themf b =μτ=μ2R / c; For the frequency difference f of the intermediate frequency signal b , obtained by fast Fourier transform of its intermediate frequency signal; the distance R of the detected target is expressed as: Distance frequency f b It is estimated by performing FFT operation on the beat signal sequence in each scanning cycle; For a moving object, the distance FFT of each frequency modulated continuous wave will have a peak at the same position, but its phase is different, that is, the spectrum has peaks with different phases at the same position, and the phase difference is: The formula for the target speed is: When the phase difference |Δφ| is less than π, it is unambiguous. Combining the phase difference and speed, we get: Indirectly, the maximum speed meets V max =λ / 4T c .

5. The non-line-of-sight target ranging and speed measurement method of a RIS-assisted FMCW radar according to claim 1, characterized in that: Based on step S3, the obtained range-Doppler spectrum is subjected to target detection using 2D-CFAR, and the specific position detection of the target is obtained according to the distance and speed information of the target, including: The range-Doppler spectrum of the target obtained from the original radar echo is used for constant false alarm detection, and the CA CFAR method with a two-dimensional sliding reference window is used to identify RDM; Assume that there is a square law detector, and take the square value of the signal amplitude received in the RDM as the random variable in the random model, and use X m,w Indicated by, where m is the target distance index and w is the Doppler frequency index; the random variable in the test unit is always represented by Y, and the calculated threshold is described by parameter T; the target detection process is based on hypothesis testing, where H0 is the hypothesis in the case of noise, and H1 describes the superposition of the target and noise; The target detection process is expressed as: Under the H0 hypothesis, the random variable in the test unit is assumed to be a noise signal, represented by Y0; in the square-law detector, the random variable Y0 is exponentially distributed, the probability density function is represented by pdf, the average noise power is represented by ζ, and the corresponding formula of the square-law detector is: The corresponding formula according to the Neyman-Pearson criterion is: T=β·ζ The average noise power E{X|H0}=ζ, which controls the false alarm probability P fa The threshold factor is β; The 2D cell-averaged CFAR is the first method to estimate the time-dependent noise floor within the local environment and within a reference window; in this case, the unknown local noise expectation E{X|H0}=ζ is adaptively estimated based on the arithmetic mean of all random variables within the 2D reference window; In order to meet the required P fa , the decision threshold is: where β CA is the threshold factor, is the estimated average noise floor of the arithmetic mean; the threshold factor β CA Depends on the required P fa and the number of cells within the reference window used for noise power estimation;

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