Millimeter wave radar multi-beam antenna array design method

Abstract

The invention discloses a millimeter wave radar multi-beam antenna array design method, which comprises the following steps of: adopting a hybrid antenna array structure, uniformly arranging central areas of first N1 array elements, non-uniformly arranging edges of rear N-N1 array elements, forming multiple beams in combination with an improved particle swarm optimization algorithm, and intelligently controlling a dynamic beam adjustment mechanism, so as to realize the multi-beam antenna array design of a millimeter wave radar. Microstrip patches or slot antenna array elements are selected, particle swarm parameters need to be initialized for hardware implementation, and a neural network model is optimized and updated, so that the directivity and resolution of beams are improved. Compared with a traditional algorithm, the method 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 present invention belongs to the technical field of millimeter wave radar, and in particular relates to a method for designing a millimeter wave radar multi-beam antenna array. Background Art

[0002] Millimeter-wave radar is widely used in many fields, including modern transportation, security, and industrial inspection. As radar performance requirements for various applications continue to increase, the design of millimeter-wave radar multi-beam antenna arrays faces numerous challenges. Existing multi-beam antenna arrays for millimeter-wave radars have several significant shortcomings. Existing multi-beam antenna arrays have limitations in beam pointing accuracy, beamwidth flexibility, and adaptability to diverse target environments. In complex target environments, traditional multi-beam antenna arrays struggle to simultaneously and efficiently detect and track multiple targets at varying distances and angles, compromising radar detection performance and reliability. Summary of the Invention

[0003] In order to solve the problems existing in the background technology, the present invention proposes a millimeter-wave radar multi-beam antenna array design method, which aims to solve the problems of insufficient design of existing millimeter-wave radar multi-beam antenna arrays.

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

[0005] Optionally, based on the multi-beam forming algorithm of the improved particle swarm optimization algorithm, an adaptive inertia weight w(t) and a random perturbation term r(t) are introduced into the particle update rate formula, where the particle update rate 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] Among them, 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 swarm, 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 as follows: Among them, w max is the maximum inertia weight, w min is the minimum inertia weight, t is the current iteration number, T max is the maximum number of iterations.

[0008] Optionally, in the multi-beam forming process, the beam direction and amplitude are used as particle position parameters, and the particle position is iteratively optimized to make the antenna array in the target direction θ j The desired beam is formed on j = 1, 2, ..., M, where M is the number of beams desired to be formed.

[0009] Optionally, a dynamic beam adjustment mechanism based on intelligent control can collect characteristic information of the target echo signal received by the radar in real time, use a pre-trained neural network model to identify and analyze the target environment, and automatically adjust the beam parameters of the antenna array according to the analysis results.

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

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

[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 achieve dynamic beam adjustment.

[0013] Optionally, a microstrip patch antenna element or a slot antenna element is selected as the millimeter wave antenna element, and the element is mounted 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 the hardware platform, the particle swarm parameters are initialized first, including the number of particles, the initial position and velocity of the particles, the learning factors c1 and c2, and the maximum inertia weight w max and the minimum value w min And the maximum number of iterations T max .

[0015] Optionally, during actual operation, target data in different environments are collected to optimize and update the neural network model.

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

[0017] 1. Reduced system cost and complexity: The hybrid antenna array structure reduces the need for numerous complex phase shifters and feed network components, simplifying system design and thus reducing system cost and complexity. This structure also improves system integration and reduces the size of the antenna array, making it more suitable for space-constrained applications.

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

[0019] 3. Enhanced Environmental Adaptability: A dynamic beam adjustment mechanism based on intelligent control enables the multi-beam antenna array to automatically adjust beam parameters in real time based on changes in the target environment, improving the radar's adaptability to diverse target environments. Whether in open environments with sparse targets or in complex environments with dense targets, it maintains excellent detection performance, enhancing the reliability and practicality of the millimeter-wave radar. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0021] Figure 2 This is a flow chart of a dynamic beam adjustment mechanism in a millimeter-wave radar multi-beam antenna array design method according to an embodiment of the present invention;

[0022] Figure 3 This is a flow chart of forming desired multi-beams based on the PSO algorithm in the millimeter wave radar multi-beam antenna array design method in an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0024] For ease of understanding, the terms used in this application are explained below:

[0025] Millimeter-wave radar: Millimeter-wave radar operates in the millimeter-wave band. Millimeter waves typically fall within the 30-300 GHz frequency range (wavelengths of 1-10 mm). Millimeter-wave wavelengths lie between microwaves and centimeter waves, so millimeter-wave radar combines the advantages of both microwave and photoelectric radar.

[0026] Beam: The shape formed on the Earth's surface by electromagnetic waves emitted by a satellite antenna (for example, like the beam of light emitted by a flashlight into a dark place). By controlling the phase and amplitude of each antenna in the antenna array, the emitted electromagnetic waves form a stronger beam in a specific direction, thereby improving signal transmission quality, increasing transmission distance or reducing interference.

[0027] Antenna array: The directivity of a single antenna is limited. To suit various applications, two or more single antennas operating at the same frequency are fed and spatially arranged according to certain requirements to form an antenna array. The antenna radiating units that constitute the antenna array are called array elements.

[0028] In one embodiment, if Figure 1 The figure shows a structural diagram of the antenna array structure in the millimeter wave radar multi-beam antenna array design method according to an embodiment of the present invention.

[0029] The present invention adopts a new hybrid antenna array structure that combines the advantages of uniform array and non-uniform array. Specifically, a uniform array layout is adopted in the center area of ​​the array, while a non-uniform array layout is adopted in the edge area of ​​the array. In the present invention, it is assumed that the antenna array has a total of N array elements. The first N1 array elements (N1 < N) are set in the center area and arranged according to the uniform spacing d1 to meet the spatial phase difference relationship of the uniform array. in, is the spatial phase difference between adjacent array elements, θ is the signal incident angle, and λ is the millimeter wave wavelength; the last N-N1 array elements are set in the edge area and arranged at a specific non-uniform spacing d2(i), i = N1+1, N1+2, ..., N, and the value of d2(i) is determined according to the optimization requirements of the beam performance in different directions. For example, it is obtained by analyzing and calculating the beamforming weights in different directions to achieve the enhancement or suppression of the beam in a specific direction. Through this hybrid structure, while ensuring the accuracy and efficiency of beamforming in the central area, the non-uniform array in the edge area is used to effectively suppress the sidelobes, thereby improving the directivity and resolution of the beam. The beamforming weight refers to the parameter matrix that adjusts the excitation amplitude and phase of each array element in the millimeter wave radar multi-beam antenna array to achieve the enhancement or suppression of the beam in a specific direction. The beamforming weight optimizes the signal weighting coefficient of each array 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] It is worth noting that in the present invention, the hybrid antenna array adopts a "center uniform + edge non-uniform" layout. The first N1 array elements are arranged at a uniform spacing d1 in the center area, satisfying the uniform array spatial phase difference relationship to ensure beamforming accuracy. The last N-N1 array elements are arranged at a non-uniform spacing d2(i) in the edge area. The value of d2(i) is determined based on the requirements for optimizing beam performance in different directions. By analyzing and calculating the beamforming weights, the enhancement or suppression of the beam in a specific direction is achieved. For example, by adjusting d2(i) in the target direction, the phase of the edge array elements and the center array elements can be coordinated to enhance the main beam, or in the non-target direction, the phase consistency is destroyed by non-uniform spacing to suppress the sidelobes.

[0031] This structure uses the coordination between the central area and the edge area to ensure the accuracy and efficiency of beamforming while effectively suppressing the side lobes and improving the beam directionality and resolution. For example, if it is necessary to form the main beam at γ = 30° and suppress the side lobes at θ = 15° and 45°, the edge array element spacing can be designed according to the phase requirements in the target direction, such as setting d2(i) to to enhance the main beam, or set To suppress side lobes, the array equivalent aperture is expanded by adjusting the non-uniform spacing, and the half-power beamwidth is reduced. Compared with the traditional uniform array, the sidelobe level can be effectively reduced, and the angular resolution is greatly improved.

[0032] In this embodiment, a multi-beamforming algorithm based on an improved particle swarm optimization (PSO) algorithm is proposed. Traditional PSO algorithms are prone to falling into local optimal solutions during the search process, affecting beamforming performance. This invention sets the physical layout of the array using d2(i). The PSO algorithm optimizes the beam parameters within this set structure. In this improvement to the PSO algorithm, an adaptive inertia weight w(t) and a random perturbation term r(t) are introduced into the particle update rate 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] Among them, 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]. The calculation formula of the adaptive inertia weight w(t) is Among them, w max is the maximum inertia weight, w min is the minimum inertia weight, t is the current iteration number, T max is the maximum number of iterations. In this way, the algorithm has a strong global search capability in the early iteration and a high local search accuracy in the later iteration. In the multi-beam forming process, the direction and amplitude of the beam are used as the position parameters of the particle. By continuously iteratively optimizing the position of the particle, the antenna array forms the desired beam in the target direction while suppressing the side lobes. For example, in the target direction θ j (j=1, 2, ..., M, where M is the number of beams to be formed), the optimization algorithm is used to maximize the amplitude of the array output signal in that direction, while minimizing the amplitude in other non-target directions, so as to achieve accurate multi-beam formation and interference suppression.

[0036] Specifically, if Figure 2The figure shows a flow chart of the dynamic beam adjustment mechanism in the millimeter-wave radar multi-beam antenna array design method according to an embodiment of the present invention. To enable the multi-beam antenna array to adapt to different target environments in real time, the present invention designs a dynamic beam adjustment mechanism based on intelligent control. During millimeter-wave radar operation, the radar collects in real time characteristic information from target echo signals, such as target distance, speed, angle, and other parameters. Using this information, a pre-trained neural network model identifies and analyzes the target environment to determine the distribution and motion state of the targets within the current environment. Based on the analysis results, the antenna array's beam parameters, including beam direction, width, and amplitude, are automatically adjusted. For example, when a target is detected moving rapidly in a certain direction, the beam direction and width are adjusted to closely track the target's motion trajectory, improving target detection and tracking accuracy. When multiple targets are detected densely distributed in different directions, the beam amplitude and phase are adjusted to enhance target resolution and prevent interference between beams. In the specific implementation process, a multi-layer perceptron (MLP) structure is adopted through the neural network model. The input layer receives the characteristic parameters of the target echo signal. After the nonlinear transformation of the hidden layer, the corresponding beam adjustment parameters are output at the output layer. Then, the feed network and phase shifter components of the antenna array are adjusted through the control circuit to realize dynamic beam adjustment.

[0037] In this embodiment, appropriate millimeter-wave antenna elements, such as microstrip patch antenna elements or slot antenna elements, are selected based on the designed hybrid antenna array structure. During the manufacturing process, the size and precision of the elements are strictly controlled to ensure that their performance meets the design requirements. The elements are mounted on a printed circuit board or other suitable substrate according to the designed spacing and layout to form an antenna array. For the uniform array portion in the center area, the element spacing is ensured to be strictly maintained at d1; for the non-uniform array portion in the edge area, the elements are precisely installed according to the pre-calculated d2(i). After installation, the antenna array is subjected to preliminary performance testing, including testing of parameters such as standing wave ratio and gain, to ensure that the basic performance of the antenna array is normal.

[0038] like Figure 3 The figure shows a flow chart of forming the desired multi-beam based on the PSO algorithm in the millimeter wave radar multi-beam antenna array design method according to an embodiment of the present invention. The multi-beam forming algorithm based on the improved PSO algorithm is implemented on the 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, and the maximum value of the inertia weight w. max and the minimum value w min And the maximum number of iterations T maxThen, the algorithm uses parameters such as the target direction and desired beam amplitude as inputs. By continuously iteratively optimizing the particle positions, it calculates the required excitation phase and amplitude for each array element. During the debugging process, the algorithm's performance is tested and optimized by varying target scenarios and parameter settings to ensure that it can accurately form the desired multi-beams in the target direction.

[0039] A target echo signal feature acquisition system was established to collect target echo signals received by the millimeter-wave radar in real time and extract characteristic parameters such as target range, velocity, and angle. These parameters were input into a pre-trained neural network model, which then output the corresponding beam-steering parameters. Based on the output adjustment parameters, the control circuit adjusted components such as the antenna array's feed network and phase shifters to achieve dynamic beam-steering. During actual operation, target data from different environments was continuously collected, and the neural network model was optimized and updated to improve the model's ability to identify and analyze target environments, thereby further optimizing the performance of the dynamic beam-steering mechanism.

[0040] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A millimeter wave radar multi-beam antenna array design method, characterized in that: A hybrid antenna array structure is adopted. The antenna array contains N array elements. The first N1 array elements are arranged in the center area with a uniform spacing of d1 to meet the uniform array spatial phase difference relationship. in, is the spatial phase difference between adjacent array elements, θ is the signal incident angle, and λ is the millimeter wave wavelength; the last N-N1 array elements are arranged in the edge area according to a specific non-uniform spacing d2(i), i = N1+1, N1+2, ..., N, and the value of d2(i) is determined according to the optimization requirements of beam performance in different directions, and the spacing of d2(i) is N1+1 to N1+2, ..., N.

2. The millimeter wave radar multi-beam antenna array design method according to claim 1, characterized in that: Also includes: The multi-beam forming algorithm based on the improved particle swarm optimization algorithm introduces the adaptive inertia weight w(t) and the random perturbation term r(t) into the particle update rate formula, where the particle update rate formula is: v i (t+1)=w(t)v i (t)+C1r1(t)(pbest i -x i (t))+c2r2(t)(gbest-x i (t)), Among them, 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 swarm, 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 as follows: Among them, w max is the maximum inertia weight, w min is the minimum inertia weight, t is the current iteration number, T max is the maximum number of iterations.

3. The millimeter wave radar multi-beam antenna array design method according to claim 2, characterized in that: In the multi-beam forming process, the beam direction and amplitude are used as particle position parameters, and the particle position is optimized iteratively to make the antenna array in the target direction θ j The desired beam is formed on j = 1, 2, ..., M, where M is the number of beams desired to be formed.

4. 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 the target echo signal received by the 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 according to the analysis results.

5. The millimeter wave radar multi-beam antenna array design method according to claim 4, characterized in that: The target echo signal characteristic information includes target distance, speed, and angle parameters.

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

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

8. The millimeter wave radar multi-beam antenna array design method according to claim 1, characterized in that: A microstrip patch antenna array element or a slot antenna array element is selected as a millimeter wave antenna array element, and the array element is mounted on a printed circuit board or other substrate to form an antenna array.

9. The millimeter wave radar multi-beam antenna array design method according to claim 2, characterized in that: When implementing the multi-beam forming algorithm based on the improved particle swarm optimization algorithm on the hardware platform, the particle swarm parameters are initialized first, including the number of particles, the initial position and speed of the particles, the learning factors c1 and c2, and the maximum inertia weight w max and the minimum value w min And the maximum number of iterations T max .

10. The millimeter wave radar multi-beam antenna array design method according to claim 4, characterized in that: During the actual operation, target data in different environments are collected to optimize and update the neural network model.

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