Ris-assisted fmcw radar non-line-of-sight target doa estimation method

Abstract

The present application relates to the technical field of non-line-of-sight target detection, and particularly relates to a RIS-assisted FMCW radar non-line-of-sight target DOA estimation method, which comprises the following steps: constructing a scene model of RIS-assisted FMCW radar non-line-of-sight target detection; performing DOA estimation on the target to calculate the angle information of the target relative to the radar; and proposing an alternating direction multiplier method based on a path gain maximization criterion to optimize the RIS phase shift matrix parameters, which can significantly enhance the received signal strength and quality by regulating the electromagnetic wave propagation environment, maximize the equivalent channel gain of the target path, effectively suppress the multipath interference and noise, make the real direction information of the target in the received signal more prominent, not only significantly improve the signal-to-noise ratio, but also ensure that the structure of the received signal covariance matrix is highly matched with the model relied on by the high-resolution estimation algorithm of the subspace class, improve the accuracy, resolution and reliability of the angle estimation, and improve the real-time performance.

Description

Technical Field

[0001] This invention relates to the field of non-line-of-sight target detection technology, specifically to a RIS-assisted method for estimating the DOA of non-line-of-sight targets using FMCW radar. Background Technology

[0002] With the rapid development of urbanization and intelligent sensing technologies, higher demands are being placed on target detection capabilities in urban environments. The dense distribution of high-rise buildings, complex street layouts, and the shading effects created by various terrains and man-made structures significantly limit traditional detection technologies when dealing with obscured targets. Problems include blocked signal propagation paths, severe attenuation of target echo intensity, and difficulty in extracting feature information, ultimately leading to decreased positioning accuracy and detection reliability. Intelligent metasurfaces, however, actively reconstruct wireless channels in a programmable manner, significantly enhancing signal strength and suppressing multipath interference, providing a stable and abundant signal source for high-precision direction-of-arrival (DOA) estimation. They can concentrate and amplify dispersed reflected signal energy and directionally project it onto the area to be detected, significantly extending the detection range and increasing echo intensity. Furthermore, by optimizing the incident wavefront structure, they improve multi-signal classification capabilities and angular resolution, thus achieving stable and accurate DOA estimation even in complex shading environments.

[0003] In non-line-of-sight (NLOS) detection, direction-of-arrival (DOA) estimation plays a crucial role. By accurately resolving and determining the spatial angles of multipath reflected signals, it effectively infers the actual location of obstructed targets, compensating for the limitations of obstructed direct beam propagation. Especially in complex scattering environments, DOA technology can analyze the signal structure from different reflection paths, achieving spatial "see-through" of the target and significantly improving positioning reliability and scene understanding under adverse perception conditions such as obstruction and multipath propagation. A proposed off-grid DOA estimation method based on RIS (Radio Routing Array) has been developed. This algorithm integrates atomic norm and Hankel-MUSIC for coarse estimation, derives a closed-form solution for angular offset through optimization, and iteratively corrects multi-source DOA using an offset estimator. Utilizing the high-precision ranging characteristics of FMCW radar, explicit DOA estimation is achieved using the absolute distance values ​​from the target to each sparse uniform linear array element. To improve the accuracy of DOA estimation, a novel strategy for sampling the optimal number and position of RIS elements is proposed. This method optimally samples RIS elements from a uniform linear RIS array while ensuring that the peak sidelobes of the sampled subarray are smaller than those of a fully filled RIS. Then, leveraging the sparsity of the target in the spatial domain, a DOA estimation problem based on the atomic norm is established to preserve the number of elements, sidelobe levels, and RIS reflection parameters under constraints. However, the above methods often suffer from high computational complexity and poor real-time performance. Summary of the Invention

[0004] To address the technical problem of poor real-time performance in DOA estimation, this invention proposes a RIS-assisted DOA estimation method for non-line-of-sight targets in FMCW radar. Specifically, after obtaining the initial DOA estimate, this invention proposes an alternating direction multiplier method to optimize the RIS phase. This algorithm successfully balances system performance and real-time performance. By dynamically adjusting the RIS phase through a lightweight computational strategy, a "sensing-control" closed loop is formed. This maintains and enhances the accuracy of the initial DOA estimate—by continuously optimizing the beam pointing to ensure echo signal strength—while significantly reducing the inherent high computational latency of the traditional two-step optimization serial architecture, meeting the real-time requirement of millisecond-level response in dynamic environments. Simultaneously, this low-complexity design significantly improves robustness to channel changes and target movement, and can flexibly coordinate communication and sensing functions, providing a key technical path for the reliable deployment of RIS in practical systems.

[0005] This invention provides a RIS-assisted method for estimating the DOA of non-line-of-sight targets in FMCW radar, the method comprising: Step S1: Construct a scenario model for non-line-of-sight target detection using RIS-assisted FMCW radar, describing the transmitted and received signals; Step S2: Based on step S1, perform DOA estimation on the target according to the transmitted and received signals, calculate the angle information of the target relative to the radar, and obtain the position information of the target in three-dimensional space. Step S3, based on step S2, proposes an alternating direction multiplier method based on the path gain maximization criterion to optimize the RIS phase shift matrix parameters according to the target's position information in three-dimensional space.

[0006] Optionally, the scenario model for constructing RIS-assisted FMCW radar non-line-of-sight target detection, describing the transmitted and received signals, includes: RIS constructs indirect reflection paths through intelligent configuration of its reflective elements, enabling the FMCW radar to detect non-line-of-sight targets based on these reflection paths; the signal emitted by the FMCW radar is a continuous wave signal, the frequency of which changes linearly with time; the radar is equipped with... One launch and One receiving antenna, passive RIS It consists of several reflective elements; it is assumed that there is a loop connection between the FMCW radar and the RIS to complete the exchange of control information; Within one signal pulse period, the transmitted signal is represented as: in, The starting frequency, Represents the imaginary unit. Indicates time, Indicates the transmission of a signal. For frequency modulation period, The duration is The frequency modulation slope, This indicates the amplitude of the transmitted signal at time 0. This indicates the initial phase of the transmitted signal at time 0; The received signal reflected back to the target by the RIS is represented as follows: in, This represents the channel matrix from the source node to the RIS. Represents the set of complex numbers. This represents the channel matrix from RIS to the target. Represents the RIS phase shift matrix (diagonal matrix, unit modulus constrained). ), Represents the precoding matrix of the source node; Indicates the transmission of a signal, satisfying , Represents mathematical expectation, This represents additive white Gaussian noise. It is the received noise power; The intermediate frequency beat signal obtained after the received signal is mixed and filtered is: in, This represents the phase difference in the target echo signal caused by target reflection.

[0007] Optionally, based on step S1, the step of estimating the target's DOA according to the transmitted and received signals, calculating the target's angle information relative to the radar, and obtaining the target's position information in three-dimensional space includes: To obtain the angle information of the target, the deterministic maximum likelihood method is used to estimate the angle, thereby achieving super-resolution angle measurement. The idea of ​​the deterministic maximum likelihood method is: under any unknown parameter, the existing data is used to estimate the parameter. Specifically, the joint probability density function is used to establish the maximum likelihood function of the signal data, and the target angle is estimated by finding the maximum value of the function. The joint probability density function of the array received signals is expressed as: in, From the perspective of the target, For the number of receiving array elements, For the number of snapshots, The steering vector matrix of the signal. For the received signal vector, The signal is a complex baseband signal; Logarithmizing the above equation and taking the logarithm of both sides, we obtain the log-likelihood function as follows: First assume and Given the noise variance, calculate the noise variance. The maximum likelihood estimate is: Substituting the maximum likelihood estimate of the noise variance obtained above into the log-likelihood function and performing a maximization process, we obtain the following equation: Further maximization yields: Continue to assume Given, find for: The obtained Substituting the simplified formula into the maximization formula, we get: make ,say For the guiding vector matrix The projection matrix of the above equation is transformed into: in, Let the trace of the matrix be denoted by . This is called the covariance matrix of the received signal data, and the above formula can be further written as: angle The maximum likelihood estimate is: .

[0008] Optionally, based on step S2, according to the target's position information in three-dimensional space, an alternating direction multiplier method based on the path gain maximization criterion is proposed to optimize the RIS phase shift matrix parameters, including: In non-line-of-sight scenarios, after estimating the target angle information using the DOA algorithm, the beam-focusing performance of the RIS is further optimized. Based on the initial angle estimation provided by DOA, an alternating direction multiplier method based on the path gain maximization criterion is adopted to optimize the phase in the RIS phase shift matrix. The optimization is performed, and the problem to be optimized is represented as follows: Among them, constraint C1 indicates that the transmission power cannot exceed , This represents the total transmit power. Constraint C2 indicates that the phase unit of the RIS is constant-modulus. It is noise power. Represents the RIS reflection coefficient. Represents the precoding matrix, Represents the reflection matrix of RIS. This represents the phase shift vector of RIS; since the objective function is related to... and The non-concave nature of makes the optimization problem a high-dimensional non-convex optimization problem. A joint optimization is proposed. and The iterative algorithm decomposes the original high-dimensional non-convex optimization problem into two low-complexity subproblems and iteratively optimizes them until the convergence condition is met. ;in, Indicates the convergence tolerance. Indicates the iteration count index; designed using the zero-forcing algorithm. And an ADMM algorithm based on the path gain maximization criterion was designed. ; Step 101, precoding matrix design; Step 1: Calculate the zero-forcing precoding matrix; The goal of zero-forcing precoding is: Right now: in, , yes The false reversal; Step 2, power normalization; right Apply power constraints, total power constraints : Step 102, RIS phase shift matrix design; Since is a constant, the square function is monotonically increasing. Therefore, the optimization problem is equivalent to maximizing path gain, i.e.: The objective function of the above formula can be equivalently rewritten as: ,in ,set up Then verify: use This fact leads to the following related matrices and vectors: The formula for the optimization problem, which is equivalent to the path gain maximization problem, can be rewritten in a form suitable for ADMM, namely: express The smallest eigenvalue; for the above problem, its augmented Lagrangian function is: in, It is a constraint The corresponding Lagrange multipliers, penalty parameters Assume it is positive; The initial primary and dual variables are used; the standard ADMM includes the following iterative procedure: question The closed-form solution is expressed as: in, The phase vector representing its parameters; the problem This is an unconstrained least squares problem; by taking the first derivative and setting it to zero, we obtain: Rearranging the above formulas produces: .

[0009] The present invention has the following beneficial effects: This invention discloses a RIS-assisted method for non-line-of-sight (DOA) target estimation in FMCW radar. First, a scenario model for RIS-assisted FMCW radar non-line-of-sight target detection is constructed, describing the transmitted and received signals. Second, DOA estimation is performed on the target, calculating the target's angle relative to the radar to obtain its position in three-dimensional space. Next, to maintain high-precision and high-reliability target detection performance, an alternating direction multiplier method based on the path gain maximization criterion is proposed to optimize the RIS phase shift matrix parameters. This provides strong support for target detection technology in non-line-of-sight regions. Specifically, it overcomes the detection blind zone by constructing a virtual line-of-sight path using RIS, transforming previously invisible obstructing targets into detectable signals and expanding the radar's sensing range. It improves detection accuracy by using the ADMM algorithm based on the path gain maximization criterion to achieve precise phase control of the RIS, significantly enhancing the echo signal-to-noise ratio and making the angle estimation resolution higher and more reliable. It balances real-time performance by decomposing the high-dimensional non-convex optimization problem into low-complexity sub-problems for iterative solving, reducing computational overhead and meeting the millisecond-level real-time response requirements in non-line-of-sight scenarios. It exhibits strong robustness, meaning that sensing and control form a closed loop. By continuously optimizing the RIS phase shift matrix, it effectively suppresses multipath fading and noise interference in complex environments. Attached Figure Description

[0010] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. 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 effort.

[0011] Figure 1 This is a flowchart of a RIS-assisted FMCW radar non-line-of-sight target DOA estimation method according to the present invention; Figure 2 This is a beam profile diagram for spatial spectrum estimation according to the present invention; Figure 3 This is a three-dimensional distance-angle diagram of the DML of the present invention; Figure 4 This is a top view of the DML distance-angle 3D graph of the present invention; Figure 5 This is a graph showing the FFT and DML angle estimation methods of the present invention. Figure 6 This is a distance Doppler image of the present invention; Figure 7 This is a target location marking diagram for the present invention. Detailed Implementation

[0012] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the specific implementation methods, structures, features, and effects of the technical solution proposed according to the present invention are described in detail below with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0013] Unless otherwise defined, 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 pertains.

[0014] refer to Figure 1 This paper illustrates the flowchart of some embodiments of a RIS-assisted FMCW radar non-line-of-sight target DOA estimation method according to the present invention. The RIS-assisted FMCW radar non-line-of-sight target DOA estimation method includes the following steps: Step S1: Construct a scenario model for RIS-assisted FMCW radar non-line-of-sight target detection, describing the transmitted and received signals.

[0015] In some embodiments, a scenario model for RIS-assisted FMCW radar non-line-of-sight target detection can be constructed to describe the transmitted and received signals.

[0016] Consider the problem of non-line-of-sight target detection aided by a reconfigurable intelligent surface (RIS). The RIS, through intelligent configuration of its reflective elements, can construct effective indirect reflection paths, enabling FMCW radar to detect non-line-of-sight targets using these paths. The signal emitted by the FMCW radar is a continuous wave signal, the frequency of which changes linearly with time. The radar is equipped with... One launch and One receiving antenna, passive RIS It consists of several reflective elements. Furthermore, it is assumed that there is a loop connection between the FMCW radar and the RIS to facilitate the exchange of control information.

[0017] Within one signal pulse period, the transmitted signal can be represented as: in, The starting frequency, Represents the imaginary unit. Indicates time, Indicates the transmission of a signal. For frequency modulation period, The duration is The frequency modulation slope, This indicates the amplitude of the transmitted signal at time 0. This indicates the initial phase of the transmitted signal at time 0.

[0018] The received signal reflected back to the target by the RIS can be represented as: in, This represents the channel matrix from the source node to the RIS. Represents the set of complex numbers. This represents the channel matrix from RIS to the target. Represents the RIS phase shift matrix (diagonal matrix, unit modulus constrained). ), Represents the precoding matrix of the source node; Indicates the transmission of a signal, satisfying , Represents mathematical expectation, This represents additive white Gaussian noise. It is the received noise power.

[0019] The intermediate frequency beat signal obtained after the received signal is mixed and filtered is: in, This represents the phase difference in the target echo signal caused by target reflection.

[0020] Step S2, based on step S1, performs DOA estimation on the target based on the transmitted and received signals, calculates the target's angle information relative to the radar, and obtains the target's position information in three-dimensional space.

[0021] In some embodiments, based on step S1, DOA estimation of the target can be performed according to the transmitted and received signals to calculate the target's angle information relative to the radar and obtain the target's position information in three-dimensional space.

[0022] To accurately perceive the surrounding environment and obtain the target's angle information, a super-resolution direction-of-arrival (DOA) estimation algorithm is used to obtain a more refined angle estimate, thus achieving super-resolution angle measurement. In this embodiment of the invention, the deterministic maximum likelihood (DML) method is employed. The basic idea of ​​this algorithm is: when any parameter to be estimated is unknown, only the existing data is used to estimate the parameter. Specifically, the maximum likelihood function of the signal data is established using the joint probability density function, and the target angle estimate is obtained by finding the maximum value of this function.

[0023] The joint probability density function of the array received signals can be expressed as: in, From the perspective of the target, For the number of receiving array elements, For the number of snapshots, The steering vector matrix of the signal. For the received signal vector, It is the complex baseband signal of the signal.

[0024] To simplify calculations, the above equation is logarithmized. Taking the logarithm of both sides yields the log-likelihood function: To obtain the maximum likelihood estimate of the estimated parameter, we should find the maximum value of the log-likelihood function in the parameter space. For ease of solution, we first assume... and Given that the noise variance can be calculated, The maximum likelihood estimate is: Substituting the maximum likelihood estimate of the noise variance obtained above into the log-likelihood function, ignoring the influence of the constant term, and performing a maximization process, we obtain the following equation: It can be observed that the above equation is a monotonic function. For simplified calculation, it can be further maximized to obtain: By observing the above formula, we can continue to assume... Given that, we can obtain for: The obtained Substituting the simplified formula into the maximization result, we get: make ,say For the guiding vector matrix The projection matrix is ​​such that the above equation can be written as: in, Let the trace of the matrix be denoted by . This is called the covariance matrix of the received signal data, and the above formula can be further written as: Therefore, angle The maximum likelihood estimate is: .

[0025] In DOA angle estimation Figure 2 The spatial spectrum estimation beam profile depicts the relationship between received signal power and azimuth angle. DOA estimation assumes that the direction of the signal source is the direction that maximizes the received signal power. Therefore, by finding the location of the peak on the beam profile, the corresponding x-coordinate represents the estimated direction of the target. Figure 3 The DML range-angle 3D plot visually demonstrates the radar system's ability to jointly estimate two-dimensional parameters when sensing multi-target scenes. It shows that the deterministic maximum likelihood estimation algorithm can accurately distinguish the unique positions of multiple targets in the range and angle dimensions. Each sharp spectral peak in the figure corresponds to a successfully detected and separated target, verifying the algorithm's high-resolution performance and the effectiveness of the entire radar system design. Figure 4 The top-down view projects the three-dimensional spatial spectrum onto the plane from a top perspective, and uses color depth to intuitively represent signal strength. This display method eliminates the visual distortion of three-dimensional perspective and allows observation of the specific distance and angular coordinates of each target.

[0026] exist Figure 5 In the FFT and DML angle estimation diagrams, by comparing the azimuth spectrum of traditional FFT beamforming and deterministic maximum likelihood estimation, the super-resolution capability of the DML algorithm compared to conventional methods is intuitively verified. In three closely adjacent target scenes, the DML spectral peaks are sharper, the sidelobe suppression is better, and the targets at -15°, 0°, and 15° can be separated more clearly, demonstrating the significant advantages of the maximum likelihood estimation method in terms of angle resolution accuracy and noise immunity. Figure 6 In the range-Doppler image, by performing FFT processing on the radar echo in the snapshot dimension, the target information is transformed from the time domain to the frequency domain, thus simultaneously displaying the distribution of multiple targets on a range-velocity two-dimensional plane. Each bright peak in the image corresponds to a detected target, and its horizontal and vertical coordinates accurately reflect the target's range and radial velocity, respectively.

[0027] exist Figure 7 In the target location marking diagram, the actual positions of the three preset targets are clearly marked with striking red circles and text labels on the range Doppler image (50m, 30m / s), (100m, -20m / s), and (150m, 10m / s). This marking directly verifies the correctness of the radar signal processing link by intuitively comparing the theoretical true values ​​with the actual measurement results. That is, the range calculation, velocity estimation, and angle measurement are all accurate, providing a reliable benchmark reference for system performance evaluation.

[0028] Step S3, based on step S2, proposes an alternating direction multiplier method based on the path gain maximization criterion to optimize the RIS phase shift matrix parameters according to the target's position information in three-dimensional space.

[0029] In some embodiments, based on step S2, in order to maintain high-precision and high-reliability target detection performance, an alternating direction multiplier method based on the path gain maximization criterion can be proposed to optimize the RIS phase shift matrix parameters.

[0030] In non-line-of-sight scenarios, after estimating the target angle using the DOA algorithm, the angle may be inaccurate due to multipath propagation and signal attenuation. To improve sensing accuracy, the beam-focusing performance of the RIS needs further optimization. Based on the initial angle estimation provided by DOA, an Alternating Direction Method of Multipliers (ADMM) based on the path gain maximization criterion is used to optimize the phase in the RIS phase shift matrix. The optimization problem can be represented as follows: Among them, constraint C1 indicates that the transmission power cannot exceed , This represents the total transmit power. Constraint C2 indicates that the phase unit of the RIS is constant-modulus. It is noise power. Represents the RIS reflection coefficient. Represents the precoding matrix, Represents the reflection matrix of RIS. This represents the phase shift vector of RIS. Furthermore, since the objective function has... and The non-concave nature of the problem makes the optimization problem a high-dimensional non-convex optimization problem.

[0031] This invention proposes a joint optimization and The iterative algorithm decomposes the original high-dimensional non-convex optimization problem into two low-complexity subproblems and iteratively optimizes them until the convergence condition is met (e.g., ...). ).in, Indicates the convergence tolerance. This represents the iteration count index. The zero-forcing algorithm is used in the design. And an ADMM algorithm based on the path gain maximization criterion was designed. .

[0032] Step 101, precoding matrix design.

[0033] Step 1: Calculate the zero-forcing precoding matrix.

[0034] The goal of zero-forcing precoding is: Right now: in, , yes The pseudo-inverse (Moore-Penrose inverse).

[0035] Step 2, power normalization.

[0036] Because zero-forcing precoding can amplify noise, it is usually necessary to... Apply power constraints (such as total power constraints) ): Step 102, RIS phase shift matrix design.

[0037] because Since is a constant and the square function is monotonically increasing, the optimization problem can be equivalent to maximizing the path gain, i.e.: The objective function of the above formula can be equivalently rewritten as: ,in ,set up Then it can be verified that: use This fact leads to the following related matrices and vectors: The formula for the optimization problem, which is equivalent to maximizing path gain, can be rewritten in the following form applicable to ADMM: express The smallest eigenvalue. For the above problem, its augmented Lagrangian function is: in, It is a constraint The corresponding Lagrange multipliers, penalty parameters It is positive. Let it be positive. These are the initial primary and dual variables. The standard ADMM includes the following iterative procedure: We can get the problem. The closed-form solution can be expressed as: in, The phase vector representing its parameters. (Question) This is an unconstrained least squares problem. By taking the first derivative and setting it to zero, we can obtain: Rearranging the above formulas produces: .

[0038] In this embodiment of the invention, the main parameters are as follows: total number of array elements. There are 16 array elements on the y-axis and 16 array elements on the z-axis. The azimuth angle of target 1 is -15°. 0 Target 2 azimuth 0 0 Target 3 azimuth 15 0 Signal-to-noise ratio 20dB, maximum detection range Distance resolution .

[0039] In summary, for the detection method of DOA estimation of non-line-of-sight targets using RIS-assisted FMCW radar, this invention studies an alternating direction multiplier method based on the path gain maximization criterion to optimize the RIS phase shift matrix parameters. The high-dimensional joint optimization problem of the RIS phase shift matrix is ​​decomposed into a series of univariate closed-form updates. By efficiently optimizing the RIS phase shift matrix, the chaotic non-line-of-sight scattering paths are reconstructed into a stable virtual line-of-sight link, enabling the receiver to regain clear spatial path difference information. This significantly reduces computational complexity and achieves accurate angle estimation in non-line-of-sight environments with strong multipath interference and low signal-to-noise ratio. The dimensional sine maximization algorithm studied in this invention has good convergence speed, strong adaptability, and scalability. The above research provides strong support for target detection technology in non-line-of-sight regions.

[0040] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A RIS-assisted method for estimating the DOA of non-line-of-sight targets in FMCW radar, characterized in that, Includes the following steps: Step S1: Construct a scenario model for non-line-of-sight target detection using RIS-assisted FMCW radar, describing the transmitted and received signals; Step S2: Based on step S1, perform DOA estimation on the target according to the transmitted and received signals, calculate the angle information of the target relative to the radar, and obtain the position information of the target in three-dimensional space. Step S3, based on step S2, proposes an alternating direction multiplier method based on the path gain maximization criterion to optimize the RIS phase shift matrix parameters according to the target's position information in three-dimensional space.

2. The RIS-assisted FMCW radar non-line-of-sight target DOA estimation method according to claim 1, characterized in that, The scenario model for constructing RIS-assisted FMCW radar for non-line-of-sight target detection describes the transmitted and received signals, including: RIS constructs indirect reflection paths through intelligent configuration of its reflective elements, enabling the FMCW radar to detect non-line-of-sight targets based on these reflection paths; the signal emitted by the FMCW radar is a continuous wave signal, the frequency of which changes linearly with time; the radar is equipped with... One launch and One receiving antenna, passive RIS It consists of several reflective elements; it is assumed that there is a loop connection between the FMCW radar and the RIS to complete the exchange of control information; Within one signal pulse period, the transmitted signal is represented as: in, The starting frequency, Represents the imaginary unit. Indicates time, Indicates the transmission of a signal. For frequency modulation period, The duration is The frequency modulation slope, This indicates the amplitude of the transmitted signal at time 0. This indicates the initial phase of the transmitted signal at time 0; The received signal reflected back to the target by the RIS is represented as follows: in, This represents the channel matrix from the source node to the RIS. Represents the set of complex numbers. This represents the channel matrix from the RIS to the target; This represents the RIS phase shift matrix, which is a diagonal matrix with unit modulus constraint. ; Represents the precoding matrix of the source node; Indicates the transmission of a signal, satisfying , Represents mathematical expectation, This represents additive white Gaussian noise. It is the received noise power; The intermediate frequency beat signal obtained after the received signal is mixed and filtered is: in, This represents the phase difference in the target echo signal caused by target reflection.

3. The RIS-assisted FMCW radar non-line-of-sight target DOA estimation method according to claim 1, characterized in that, Based on step S1, DOA estimation of the target is performed according to the transmitted and received signals, and the angle information of the target relative to the radar is calculated to obtain the target's position information in three-dimensional space, including: To obtain the angle information of the target, the deterministic maximum likelihood method is used to estimate the angle, thereby achieving super-resolution angle measurement. The idea of ​​the deterministic maximum likelihood method is: under any unknown parameter, the existing data is used to estimate the parameter. Specifically, the joint probability density function is used to establish the maximum likelihood function of the signal data, and the target angle is estimated by finding the maximum value of the function. The joint probability density function of the array received signals is expressed as: in, From the perspective of the target, For the number of receiving array elements, For the number of snapshots, The steering vector matrix of the signal. For the received signal vector, The signal is a complex baseband signal; Logarithmizing the above equation and taking the logarithm of both sides, we obtain the log-likelihood function as follows: First assume and Given the noise variance, calculate the noise variance. The maximum likelihood estimate is: Substituting the maximum likelihood estimate of the noise variance obtained above into the log-likelihood function and performing a maximization process, we obtain the following equation: Further maximization yields: Continue to assume Given, find for: The obtained Substituting the simplified formula into the maximization formula, we get: make ,say For the guiding vector matrix The projection matrix of the above equation is transformed into: in, Let the trace of the matrix be denoted by . This is called the covariance matrix of the received signal data, and the above formula can be further written as: angle The maximum likelihood estimate is: 。 4. The RIS-assisted FMCW radar non-line-of-sight target DOA estimation method according to claim 1, characterized in that, Based on step S2, and according to the target's position information in three-dimensional space, an alternating direction multiplier method based on the path gain maximization criterion is proposed to optimize the RIS phase shift matrix parameters, including: In non-line-of-sight scenarios, after estimating the target angle information using the DOA algorithm, the beam-focusing performance of the RIS is further optimized. Based on the initial angle estimation provided by DOA, an alternating direction multiplier method based on the path gain maximization criterion is adopted to optimize the phase in the RIS phase shift matrix. The optimization is performed, and the problem to be optimized is represented as follows: Among them, constraint C1 indicates that the transmission power cannot exceed , This represents the total transmit power. Constraint C2 indicates that the phase unit of the RIS is constant-modulus. It is noise power. Represents the RIS reflection coefficient. Represents the precoding matrix, Represents the reflection matrix of RIS. This represents the phase shift vector of RIS; since the objective function is related to... and The non-concave nature of makes the optimization problem a high-dimensional non-convex optimization problem. A joint optimization is proposed. and The iterative algorithm decomposes the original high-dimensional non-convex optimization problem into two low-complexity subproblems and iteratively optimizes them until the convergence condition is met. ;in, Indicates the convergence tolerance. Indicates the iteration count index; designed using the zero-forcing algorithm. And an ADMM algorithm based on the path gain maximization criterion was designed. ; Step 101, precoding matrix design; Step 1: Calculate the zero-forcing precoding matrix; The goal of zero-forcing precoding is: Right now: in, , yes The false reversal; Step 2, power normalization; right Apply power constraints, total power constraints : Step 102, RIS phase shift matrix design; Since is a constant, the square function is monotonically increasing. Therefore, the optimization problem is equivalent to maximizing path gain, i.e.: The objective function of the above formula can be equivalently rewritten as: ,in ,set up Then verify: use This fact leads to the following related matrices and vectors: The formula for the optimization problem, which is equivalent to the path gain maximization problem, can be rewritten in a form suitable for ADMM, namely: express The smallest eigenvalue; for the above problem, its augmented Lagrangian function is: in, It is a constraint The corresponding Lagrange multipliers, penalty parameters Assume it is positive; The initial primary and dual variables are used; the standard ADMM includes the following iterative procedure: question The closed-form solution is expressed as: in, The phase vector representing its parameters; the problem This is an unconstrained least squares problem; by taking the first derivative and setting it to zero, we obtain: Rearranging the above formulas produces: 。

Images