A millimeter wave radar multi-beam antenna array design method

Abstract

The application discloses a millimeter wave radar multi-beam antenna array design method, through adopting a hybrid antenna array structure, the front N1 array element center region is uniformly arranged, the rear N-N1 array element edge is non-uniformly arranged, meanwhile, a multi-beam is formed by combining an improved particle swarm optimization algorithm, through intelligent control dynamic beam adjustment mechanism, a microstrip patch or a slot antenna element is selected, hardware implementation needs to initialize particle swarm parameters, and an optimized and updated neural network model is used, so that the application improves the directivity and resolution of the beam. Compared with the traditional algorithm, the application can more accurately detect and identify the target, and improves the target detection capability of the millimeter wave radar in a complex environment.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of millimeter wave radars, and particularly relates to a millimeter wave radar multi-beam antenna array design method. BACKGROUND

[0002] Millimeter wave radars have a wide range of applications in modern transportation, security, industrial detection and many other fields. With the increasing requirements of application scenarios on radar performance, the design of millimeter wave radar multi-beam antenna arrays faces many challenges. In the prior art, there are some obvious deficiencies in millimeter wave radar multi-beam antenna arrays. The existing multi-beam antenna arrays have certain limitations in terms of beam pointing accuracy, beam width flexibility and adaptability to different target environments. In a complex target environment, the traditional multi-beam antenna array is difficult to simultaneously achieve efficient detection and tracking of multiple targets at different distances and angles, which affects the detection performance and reliability of the radar. SUMMARY

[0003] To solve the problems in the background art, the application provides a millimeter wave radar multi-beam antenna array design method, aiming to solve the problems of the existing millimeter wave radar multi-beam antenna array design.

[0004] The first aspect of the application provides a millimeter wave radar multi-beam antenna array design method, comprising: adopting a hybrid antenna array structure, the antenna array containing N elements, the first N1 elements being arranged at a uniform spacing d1 in a central region, satisfying the uniform array spatial phase difference relationship wherein, is the spatial phase difference between adjacent elements, θ is the signal incidence angle, and λ is the millimeter wave wavelength; the last N-N1 elements being arranged at a specific non-uniform spacing d2(i) in an edge region, i=N1+1, N1+2, …, N, the value of d2(i) being determined according to different direction beam performance optimization requirements, the value of d2(i) being spaced from N1+1 to N1+2, …, N.

[0005] Optionally, a multi-beam forming algorithm based on an improved particle swarm optimization algorithm is used, in which an adaptive inertia weight w(t) and a random disturbance term r(t) are introduced into the particle update speed formula, wherein the particle update speed formula is:

[0006] v i (t+1)=w(t)v i (t)+C1r1(t)(pbest i -x i (t))+c2r2(t)(gbest-x i (t)),

[0007] wherein, v i(t) is the velocity of particle i at time t, x i (t) is the position of particle i at time t, pbest i is the optimal position experienced by particle i itself, gbest is the optimal position experienced by the entire particle group, c1 and c2 are learning factors, r1(t) and r2(t) are random numbers in the interval [0, 1], and the adaptive inertia weight w(t) is calculated by where w max is the maximum inertia weight, w min is the minimum inertia weight, t is the current iteration number, and T max is the maximum iteration number.

[0008] Optionally, in the multi-beam forming process, the beam direction and amplitude are taken as the particle position parameters, and the particle position is optimized by iteration, so that the antenna array forms the desired beam in the target direction θ j , j = 1, 2, …, M, and M is the number of desired beams to be formed.

[0009] Optionally, based on the dynamic beam adjustment mechanism of intelligent control, the target echo signal feature information received by the radar is collected in real time, a pre-trained neural network model is used to identify and analyze the target environment, and the beam parameters of the antenna array are automatically adjusted according to the analysis result.

[0010] Optionally, the target echo signal feature information includes target distance, speed, and angle parameters.

[0011] Optionally, the neural network model adopts a multi-layer perceptron structure, the input layer receives target echo signal feature parameters, and the corresponding beam adjustment parameters are output in the output layer after nonlinear transformation in the hidden layer.

[0012] Optionally, according to the output beam adjustment parameters, the feed network and phase shifter of the antenna array are adjusted through the control circuit to realize dynamic beam adjustment.

[0013] Optionally, microstrip patch antenna elements or slot antenna elements are selected as millimeter wave antenna elements, and the elements are installed on a printed circuit board or other substrate to form an antenna array.

[0014] Optionally, when implementing the multi-beam forming algorithm based on the improved particle swarm optimization algorithm on a hardware platform, the particle swarm parameters are first initialized, including the number of particles, the initial position and velocity of the particles, the learning factors c1 and c2, the maximum inertia weight w max and the minimum inertia weight w min , and the maximum iteration number T max .

[0015] Optionally, during actual operation, target data under different environments is collected, and the neural network model is optimized and updated.

[0016] Compared with the prior art, the present application has the following beneficial effects:

[0017] 1. Reduce system cost and complexity: By adopting the hybrid antenna array structure, the need for a large number of complex phase shifters and feed network components is reduced, simplifying the system design and reducing the cost and complexity of the system. At the same time, this structure is conducive to improving the integration of the system and reducing the size of the antenna array, making it more suitable for use in space-limited application scenarios.

[0018] 2. Improve beam performance: The multi-beam forming algorithm based on the improved PSO algorithm can more accurately form the desired beam in the target direction, effectively suppress the sidelobes, and improve the directivity and resolution of the beam. Compared with traditional algorithms, it can more accurately detect and identify targets, improving the target detection capability of the millimeter wave radar in complex environments.

[0019] 3. Enhance environmental adaptability: The dynamic beam adjustment mechanism based on intelligent control enables the multi-beam antenna array to automatically adjust beam parameters in real time according to changes in the target environment, improving the adaptability of the radar to different target environments. Whether in a sparse environment with sparse targets or in a complex environment with dense targets, it can maintain good detection performance, improving the reliability and practicality of the millimeter wave radar. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 is a structural diagram of the antenna array structure in the millimeter wave radar multi-beam antenna array design method in the embodiment of the present application;

[0021] Figure 2 is a flowchart of the dynamic beam adjustment mechanism in the millimeter wave radar multi-beam antenna array design method in the embodiment of the present application;

[0022] Figure 3 is a flowchart of forming the desired multi-beam based on the PSO algorithm in the millimeter wave radar multi-beam antenna array design method in the embodiment of the present application. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0024] For the convenience of understanding, the terms appearing in the present application are explained as follows:

[0025] Millimeter wave radar: millimeter wave radar is a radar working in the millimeter wave band for detection. Usually, millimeter wave refers to the frequency domain of 30-300GHz (wavelength of 1-10mm). The wavelength of millimeter wave is between microwave and centimeter wave, so the millimeter wave radar has some advantages of microwave radar and photoelectric radar.

[0026] Beam: the shape formed by the electromagnetic waves emitted by the satellite antenna on the earth's surface (such as a flashlight beam into the darkness), by controlling the phase and amplitude of each antenna in the antenna array, the electromagnetic waves emitted form a strong beam in a certain direction, thereby improving the transmission quality of the signal, increasing the transmission distance or reducing interference.

[0027] Antenna array: the directivity of a single antenna is limited, in order to adapt to various applications, two or more single antennas working at the same frequency are fed and spatially arranged according to certain requirements to form an antenna array, wherein the antenna radiation unit constituting the antenna array is called an array element.

[0028] In an embodiment, as Figure 1 The structure diagram of the antenna array structure in the millimeter wave radar multi-beam antenna array design method in the embodiment of the present application is shown.

[0029] The present application adopts a new type of hybrid antenna array structure, which combines the advantages of uniform array and non-uniform array. Specifically, a uniform array layout is adopted in the central region of the array, and a non-uniform array layout is adopted in the edge region of the array. In the present application, it is assumed that the total number of array elements of the antenna array is N, the first N1 array elements (N1 wherein, for the spatial phase difference between adjacent elements, θ is the signal incidence angle, and λ is the millimeter wave wavelength; the latter N-N1 elements are arranged in the edge region according to a specific non-uniform interval d2(i), i=N1+1, N1+2, …, N, and the value of d2(i) is determined according to the performance optimization requirements of different direction beams, for example, by analyzing and calculating the beamforming weights of different directions to achieve the enhancement or suppression of the beams in specific directions. Through this hybrid structure, while ensuring the accuracy and efficiency of beamforming in the central region, the non-uniform array in the edge region is used to effectively suppress the sidelobes, thereby improving the directivity and resolution of the beams. The beamforming weight refers to a parameter matrix for adjusting the excitation amplitude and phase of each element in the millimeter wave radar multi-beam antenna array to achieve the enhancement or suppression of the beams in specific directions. The beamforming weight optimizes the signal weighting coefficients of each element to focus the electromagnetic wave energy radiated by the antenna array in the target direction while suppressing the energy radiation in the sidelobe direction.

[0030] Notably, in the present application, the hybrid antenna array adopts a "central uniform + edge non-uniform" layout, the former N1 elements are arranged in the central region according to a uniform interval d1, which satisfies the spatial phase difference relationship of the uniform array to ensure the accuracy of beamforming, and the latter N-N1 elements are arranged in the edge region according to a non-uniform interval d2(i). The value of d2(i) is determined according to the performance optimization requirements of different direction beams, and the beamforming weight is analyzed and calculated to achieve the enhancement or suppression of the beams in specific directions, for example, by adjusting d2(i) in the target direction to make the phase of the edge elements consistent with that of the central elements to enhance the main beam, or by destroying the phase consistency through the non-uniform interval in the non-target direction to suppress the sidelobes.

[0031] This structure cooperates the central region and the edge region to effectively suppress the sidelobes through the non-uniform array in the edge region while ensuring the accuracy and efficiency of beamforming, thereby improving the directivity and resolution of the beams. For example, if a main beam needs to be formed at γ=30° and the sidelobes at θ=15° and 45° need to be suppressed, the interval between the edge elements can be designed according to the phase requirements of the target direction, such as setting d2(i) to to enhance the main beam, or setting d2(i) to to suppress the sidelobes, and the non-uniform interval adjustment expands the equivalent aperture of the array, reduces the half-power beam width, and effectively reduces the sidelobe level compared with the traditional uniform array, while the angular resolution is greatly improved.

[0032] In the embodiment, a multi-beam forming algorithm based on an improved particle swarm optimization (PSO) algorithm is provided. The conventional PSO algorithm is prone to fall into a local optimal solution in a search process, which affects the performance of the beam forming. The application sets a physical layout of an array by d2(i), and the PSO algorithm needs to optimize beam parameters under the set structure. In the process of improving the PSO algorithm, an adaptive inertia weight w(t) and a random disturbance term r(t) are introduced into a particle update speed formula.

[0033] The particle update speed formula is:

[0034] v i (t+1)=w(t)v i (t)+c1r1(t)(pbest i -x i (t))+c2r2(t)(gbest-x i (t)),

[0035] wherein v i (t) is a speed of the particle i at the time t, x i (t) is a position of the particle i at the time t, pbest i is an optimal position of the particle i, gbest is an optimal position of the entire particle group, c1 and c2 are learning factors, r1(t) and r2(t) are random numbers in the interval [0, 1]. The calculation formula of the adaptive inertia weight w(t) is wherein w max is a maximum value of the inertia weight, w min is a minimum value of the inertia weight, t is a current iteration number, and T max is a maximum iteration number. In this way, the algorithm has strong global search ability in the early iteration and high local search precision in the late iteration. In the multi-beam forming process, the direction and amplitude of the beam are taken as the position parameters of the particle, and the position of the particle is continuously optimized, so that the antenna array forms the expected beam in the target direction and suppresses the sidelobe. For example, on the target direction θ j (j=1, 2, …, M, M is the number of expected beams), the amplitude of the array output signal in the direction is maximized by the optimization algorithm, and the amplitude in other non-target directions is as small as possible, so as to realize the accurate formation of the multi-beam and the suppression of the interference.

[0036] Specifically, as Figure 2A flowchart of a dynamic beam adjustment mechanism in the millimeter wave radar multi-beam antenna array design method of the embodiment of the application is shown. In order to enable the multi-beam antenna array to adapt to different target environments in real time, the application designs a dynamic beam adjustment mechanism based on intelligent control. In the working process of the millimeter wave radar, the characteristic information of the target echo signal received by the radar is collected in real time, such as the distance, speed, angle and other parameters of the target. Using this information, the target environment is identified and analyzed through a pre-trained neural network model to determine the distribution and motion state of the target in the current environment. According to the analysis result, the beam parameters of the antenna array are automatically adjusted, including the pointing, width and amplitude of the beam. For example, when it is detected that the target is moving quickly in a certain direction, the pointing and width of the beam are adjusted to enable it to closely track the motion trajectory of the target, thereby improving the detection and tracking accuracy of the target; when it is detected that multiple targets are distributed relatively densely in different directions, the amplitude and phase of the beam are adjusted to enhance the resolution of different targets and avoid mutual interference between the beams. In the specific implementation process, the neural network model adopts a multi-layer perceptron (MLP) structure, the characteristic parameters of the target echo signal are received by the input layer, the beam adjustment parameters are output by the output layer after nonlinear transformation of the hidden layer, and then the feeding network and phase shifter components of the antenna array are adjusted through the control circuit to realize dynamic beam adjustment.

[0037] In this embodiment, according to the designed hybrid antenna array structure, appropriate millimeter wave antenna elements are selected, such as microstrip patch antenna elements or slot antenna elements, etc. In the manufacturing process, the size and precision of the elements are strictly controlled to ensure that their performance meets the design requirements. According to the designed spacing and layout, the elements are installed on a printed circuit board or other suitable substrate to form an antenna array. For the uniform array part in the central region, the element spacing is strictly maintained as d1; for the non-uniform array part in the edge region, accurate installation is performed according to the pre-calculated d2(i). After installation is completed, preliminary performance testing is performed on the antenna array, including testing of parameters such as VSWR and gain, to ensure that the basic performance of the antenna array is normal.

[0038] As Figure 3 A flowchart of forming an expected multi-beam based on a PSO algorithm in the millimeter wave radar multi-beam antenna array design method of the embodiment of the application is shown. The multi-beam forming algorithm based on the improved PSO algorithm is implemented on a hardware platform. First, the parameters of the particle swarm are initialized, including the number of particles, the initial position and velocity of the particles, the learning factors c1 and c2, the maximum value w max and the minimum value w min of the inertia weight, and the maximum number of iterations T maxThen, the target direction and the expected beam amplitude and other parameters are taken as the input of the algorithm, and the required excitation phase and amplitude of each array element are calculated by continuously iterating and optimizing the position of the particle. During debugging, the performance of the algorithm is tested and optimized by changing different target scenarios and parameter settings, ensuring that the algorithm can accurately form the expected multi-beam in the target direction.

[0039] A target echo signal feature acquisition system is established to collect target echo signals received by the millimeter wave radar in real time and extract the distance, speed, angle and other characteristic parameters of the target. These parameters are input into the pre-trained neural network model, and the model outputs the corresponding beam adjustment parameters. According to the output adjustment parameters, the feed network and the phase shifter and other components of the antenna array are adjusted through the control circuit to realize dynamic beam adjustment. In the actual operation process, the target data in different environments are continuously collected to optimize and update the neural network model, improve the model's ability to identify and analyze the target environment, and further optimize the performance of the dynamic beam adjustment mechanism.

[0040] Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can modify the technical solutions described in the foregoing embodiments or make equivalent replacements to some of the technical features, and any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A design method for a millimeter-wave radar multi-beam antenna array, characterized in that, A hybrid antenna array structure is adopted, which contains N array elements. The first N1 array elements are arranged in the central region at a uniform spacing d1 to satisfy the spatial phase difference relationship of the uniform array. ,in, The spatial phase difference between adjacent array elements. The angle of signal incidence. The wavelength is millimeter wave; the last N-N1 array elements are arranged in the edge region at a specific non-uniform spacing d2(i), i=N1+1,N1+2,…,N. The value of d2(i) is determined according to the beam performance optimization requirements in different directions. The spacing of d2(i) is from N1+1 to N1+2,…,N. The d2(i) sets the physical layout of the array to optimize the beam parameters. The process of optimizing the beam parameters is as follows: A multi-beamforming algorithm based on an improved particle swarm optimization algorithm introduces adaptive inertial weights into the particle update velocity formula. and random disturbance term The formula for particle renewal rate is: ， in, Let be the velocity of particle i at time t. Let be the position of particle i at time t. Let i be the optimal position that particle i experiences. This is the optimal position for the entire particle swarm. and As a learning factor, and For random numbers in the interval [0,1], adaptive inertia weights The calculation formula is ,in, This represents the maximum value of the inertia weight. Let t be the minimum inertia weight, and t be the current iteration number. This represents the maximum number of iterations. In the multi-beamforming process, the beam direction and amplitude are used as particle position parameters. By iteratively optimizing the particle position, the antenna array is aligned with the target direction. The desired beam is formed on the surface, j=1,2,…,M, where M is the number of beams to be formed.

2. The millimeter-wave radar multi-beam antenna array design method according to claim 1, characterized in that, It includes a dynamic beam adjustment mechanism based on intelligent control, which collects the characteristic information of target echo signals received by radar in real time, uses a pre-trained neural network model to identify and analyze the target environment, and automatically adjusts the beam parameters of the antenna array based on the analysis results.

3. The millimeter-wave radar multi-beam antenna array design method according to claim 2, characterized in that, The target echo signal feature information includes target distance, velocity, and angle parameters.

4. The millimeter-wave radar multi-beam antenna array design method according to claim 2, characterized in that, The neural network model adopts a multilayer perceptron structure. The input layer receives the characteristic parameters of the target echo signal, and after nonlinear transformation by the hidden layer, the corresponding beam adjustment parameters are output in the output layer.

5. The millimeter-wave radar multi-beam antenna array design method according to claim 4, characterized in that, The antenna array's feed network and phase shifter are adjusted according to the output beam adjustment parameters and control circuit to achieve dynamic beam adjustment.

6. The millimeter-wave radar multi-beam antenna array design method according to claim 1, characterized in that, Microstrip patch antenna elements or slot antenna elements are selected as millimeter-wave antenna elements, and the elements are mounted on printed circuit boards or other substrates to form an antenna array.

7. The millimeter-wave radar multi-beam antenna array design method according to claim 1, characterized in that, When implementing a multi-beamforming algorithm based on an improved particle swarm optimization algorithm on a hardware platform, the particle swarm parameters are first initialized, including the number of particles, initial particle positions and velocities, and learning factors. and Maximum value of inertia weight and minimum value and the maximum number of iterations .

8. The millimeter-wave radar multi-beam antenna array design method according to claim 2, characterized in that, In actual operation, target data under different environments are collected to optimize and update the neural network model.

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