Clutter simulation method and device for array antenna sensing radar

Abstract

The invention provides a clutter simulation method and device for an array antenna sensing radar. The method comprises the steps of performing single reference point ray tracing based on a 3D map of an environment where the radar is located so as to obtain clutter paths reaching the sensing radar from different directions; performing directional diagram synthesis according to the sensing radar parameters to obtain a complete antenna directional diagram data table; performing gain preprocessing on each clutter path in combination with the antenna pattern data table to generate clutter response; performing time domain convolution on the clutter response and a radar emission signal acquired in real time to generate a single-channel clutter signal; and performing one-to-many phase shifting on the single-channel clutter signal through a phase shifter, and outputting a multi-channel clutter signal. According to the invention, real-time simulation of clutter signals of a plurality of antenna units of a sensing radar antenna array is realized, and the hardware cost of clutter simulation can be significantly reduced while the clutter simulation performance is ensured.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of wireless communication, and particularly relates to a clutter simulation method and device for array antenna sensing radar. BACKGROUND

[0002] With the rapid development of wireless communication technology, 6G (the sixth generation of mobile communication system) is gradually maturing. ISAC (Integrated Sensing and Communication) is one of the key technologies of 6G, which is a new type of information processing technology that realizes the coordination of communication and sensing functions based on the sharing of software and hardware resources or information sharing. Under the influence of ISAC, the sensing capability of 6G base stations and terminals is greatly improved, such as sensing-assisted communication, which can effectively improve the communication efficiency, target detection, identification, positioning and tracking applications based on sensing, environment reconstruction based on sensing, digital twin, intelligent transportation, low-altitude monitoring, autonomous driving, robot intelligent interaction, etc.

[0003] The sensing application in ISAC is based on radar technology. Through the efforts of many researchers, the sensing radar technology has developed rapidly, and the development cycle of the sensing radar technology has been greatly shortened, resulting in higher requirements for the sensing radar simulator used for testing the performance of the sensing radar. In addition to being able to simulate targets and various jamming signals, the radar simulator can also simulate the clutter signals reflected back by the environment detected by the sensing radar, thereby realizing the testing of various sensing radar performance. The sensing radar clutter signal will directly affect the performance of the sensing radar in target detection, and therefore, it is particularly important to study the sensing radar clutter simulation technology for testing the detection capability of the sensing radar in various clutter environments.

[0004] The sensing scenarios of 6G ISAC network are very complex, such as dense urban areas, where high-rise buildings are densely distributed around the sensing antenna, the scatterers are distributed in a complex manner, and the environment clutter will change greatly with the change of sensing direction; there are often vegetation features such as trees, grasslands and water surfaces in the sensing environment, which will be affected by the wind and produce complex random characteristics; weather factors such as rainfall, cloud clusters and bird flocks passing by during the sensing process will also have a significant impact on the clutter signal, therefore, it is urgent to develop an accurate and reliable clutter model and simulation method that supports the above complex sensing scenarios.

[0005] The related researches at home and abroad mainly involve the 6G ISAC channel model research and the traditional clutter simulation technology for ground-based, airborne and spaceborne radars. However, the research on clutter simulation for 6G complex environment is not perfect, mainly in the following aspects:

[0006] (1) Traditional clutter model and 6G complex environment clutter model application scenarios have great difference. The closest model to 6G complex environment clutter model is the ground radar ground clutter model. In this model, the height of the radar base station is generally much higher than the height of the buildings in the environment, and the radar is relatively far away from the building group. The top plane of the building often forms the clutter. However, the perception radar in the 6G complex environment is relatively complex. The height may be comparable to the height of the surrounding buildings, and it may be in the middle of the building group. The incident angle range of the clutter component is larger, and the clutter influence is greater. The existing clutter model is no longer applicable.

[0007] (2) Although the ray tracing method can simulate the environment clutter of the perception radar, the existing ray tracing method is generally not used for clutter simulation of the perception system (with large scale and statistics). For 6G complex environment, the generated clutter is mainly composed of three paths, namely: reflection path, diffraction path and scattering path. The scattering path is more represented as a backscattering component. The existing ray tracing algorithm does not support the situation that the transmitter and receiver are in the same position, and the backscattering part is not supported enough.

[0008] (3) Although the 6G channel model considers the complex environment factors of 6G application scenarios, there is no systematic description of the clutter model of specific scenarios in the existing results, and the correspondence with reality is poor. Especially for digital twin and other perception and sensing integrated application demand scenarios that require specific scene support, it cannot provide effective perception ability test.

[0009] (4) Considering the universal application of antenna arrays in 6G, each antenna element in the array needs an independent radio frequency channel for clutter simulation. The larger the array size, the more the clutter simulation cost increases. SUMMARY

[0010] To solve the above technical problems, the application provides a clutter simulation method and device for array antenna perception radar.

[0011] In a first aspect, the application provides a clutter simulation method for array antenna perception radar, comprising:

[0012] Performing ray tracing based on the 3D map of the environment where the radar is located to obtain each clutter path reaching the perception radar from different directions;

[0013] Synthesizing the directional diagram according to the parameters of the perception radar to obtain a complete antenna directional diagram data table;

[0014] Combining the antenna directional diagram data table to perform gain preprocessing on each clutter path to generate a clutter response;

[0015] Performing time domain convolution on the clutter response and the real-time collected radar transmission signal to generate a single-channel clutter signal;

[0016] The single-channel clutter signal is phase-shifted by the phase shifter in one-to-many mode to output a multi-channel clutter signal.

[0017] Optionally, the pattern synthesis according to the perception radar parameters comprises:

[0018] In a first case where the main lobe pointing direction, the main lobe 3dB bandwidth and the sidelobe level of the antenna pattern are known, the pattern synthesis is performed by using Gaussian fitting;

[0019] In a second case where the E-plane pattern and the H-plane pattern of the antenna are known, the sparse measurement data are supplemented by interpolation, and the E-plane pattern and the H-plane pattern components are vector-synthesized to reconstruct the three-dimensional pattern by using the projection component method;

[0020] In all other cases except the first case and the second case, the pattern synthesis is performed by using the Duffing-Chebyshev fitting method.

[0021] Optionally, the gain preprocessing of the clutter path components reaching the perception radar from different directions comprises:

[0022] The clutter path components reaching the perception radar from different directions are gain-equivalent mapped;

[0023] The clutter ray components forming the clutter path are superimposed and fused in equal time units.

[0024] Optionally, the gain-equivalent mapping of the clutter path components reaching the perception radar from different directions comprises:

[0025] The angle-of-arrival information (θ, φ) of a clutter path is extracted, and the gain value of the direction where the clutter path is located is found from the antenna pattern data table;

[0026] The found gain value is multiplied by the path loss information extracted from the clutter path to update the new path loss;

[0027] The above operation is repeated for all the clutter paths in the antenna pattern data table.

[0028] Optionally, the superimposition and fusion of the clutter ray components forming the clutter path in equal time units comprises:

[0029] The maximum action distance of the perception radar is determined according to the perception radar parameters;

[0030] The time is sampled according to the time resolution, and the maximum value of the time is twice the maximum action distance divided by the speed of light;

[0031] A clutter path is selected, delay information of the clutter path is extracted, the delay time is divided by the time resolution, and the corresponding time sample value is obtained by rounding off;

[0032] All clutter paths are isochronously superimposed according to the time sample value;

[0033] The superimposed result is arranged as a clutter response sequence, that is, the clutter response.

[0034] Optionally, the one-to-many phase shifting of the single-channel clutter signal by the phase shifter comprises:

[0035] A coordinate system is established with the normal direction of the antenna array as the Z axis, θ is the pitch angle, and φ is the azimuth angle, it is assumed that there is a signal source in the far field region, and the direction cosine of the signal source is obtained;

[0036] The wave path difference of adjacent elements in the X and Y axis directions is obtained by multiplying the X and Y axis components of the direction cosine by the preset interval threshold of the elements;

[0037] The phase difference of adjacent elements is obtained according to the wave path difference of adjacent elements;

[0038] The phase difference of the elements and the origin at any position is obtained according to the phase difference of adjacent elements in the X and Y axis components;

[0039] The steering vector of the X and Y axis linear array at any position is obtained according to the phase difference of the elements and the origin at any position;

[0040] The steering vector of the antenna array is obtained by performing Kronecker product operation on the steering vector of the X and Y axis linear array;

[0041] The array antenna perceives the clutter signal received by the radar by multiplying the single-channel clutter signal and the steering vector of the antenna array.

[0042] In a second aspect, a clutter simulation device for an array antenna perception radar is provided, comprising:

[0043] The acquisition module is configured to perform single-reference-point ray tracing based on a 3D map of an environment in which the radar is located to acquire each clutter path reaching the perception radar from different directions;

[0044] The determination module is configured to perform pattern synthesis according to the perception radar parameters to acquire a complete antenna pattern data table, perform gain preprocessing on each clutter path in combination with the antenna pattern data table to generate a clutter response, and perform time domain convolution on the clutter response and a radar transmission signal collected in real time to generate a single-channel clutter signal;

[0045] The generation module is configured to perform one-to-many phase shifting of the single-channel clutter signal by a phase shifter and output a multi-channel clutter signal.

[0046] In a third aspect, a computer storage medium is provided, and the computer storage medium stores a computer program, and the computer program is executed by a processor to implement the clutter simulation method for array antenna perception radar according to the first aspect.

[0047] In a fourth aspect, an electronic device is provided, and the electronic device includes a memory, a processor, and a computer program stored in the memory and executable by the processor, and the processor implements the clutter simulation method for array antenna perception radar according to the first aspect when executing the computer program.

[0048] In a fifth aspect, a computer program product is provided, and the computer program product includes a computer program or instructions, and the computer program or instructions are executed by a processor to implement the clutter simulation method for array antenna perception radar according to the first aspect.

[0049] Advantages

[0050] The present application provides a clutter simulation method and device for array antenna perception radar, which realizes real-time simulation of clutter signals of multiple antenna elements of an antenna array of a perception radar through five stages of ray tracing, pattern synthesis, clutter response fusion, clutter signal generation, and single-channel-to-multi-channel phase shift mapping, and can significantly reduce the hardware cost of clutter simulation while ensuring the performance of clutter simulation. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 A flowchart of the clutter simulation method for array antenna perception radar according to the embodiment of the present application;

[0052] Figure 2 A principle block diagram of the clutter simulation method for array antenna perception radar according to the embodiment of the present application;

[0053] Figure 3 A flowchart of acquisition of all clutter paths according to the embodiment of the present application;

[0054] Figure 4 A 3D schematic diagram of effective components of a clutter path acquired by ray tracing according to the embodiment of the present application;

[0055] Figure 5 A clutter response simulation diagram before equivalent mapping of a ray gain of a clutter path according to the embodiment of the present application;

[0056] Figure 6 A clutter response simulation diagram after equivalent mapping of a ray gain of a clutter path according to the embodiment of the present application;

[0057] Figure 7 A schematic diagram of a uniform surface array used by a certain single-in-multiple-out phase shifter according to the embodiment of the present application;

[0058] Figure 8A phase change diagram after the clutter phase shifter in the embodiment of the present application faces a 4*4 planar array antenna;

[0059] Figure 9 The principle block diagram of the clutter simulation device of the array antenna perception radar in the embodiment of the present application. DETAILED DESCRIPTION

[0060] The terms "first", "second", "third", "fourth" and the like in the description and claims of the present application and above-mentioned appended drawings (if any) are used as identifiers to distinguish between similar objects, and are not necessarily intended to denote a particular order or sequence among the objects. It will be understood that the use of these terms in the description is merely intended to distinguish between the similar objects and that these terms are used interchangeably in appropriate instances to refer to the same object. It will be further understood that the data used in this description is interchangeable to allow the embodiments described herein to be implemented in other than the order in which it is illustrated or described. Furthermore, the terms "comprise" and "include" and their conjugates, as used in this description and in the appended claims, are intended to encompass the inclusion of the stated steps, elements, components, or functions, but not to preclude the inclusion of other steps, elements, components, or functions. The description of embodiments of the present application in the following detailed description and drawings are not intended to be exhaustive or to limit the present application to the precise form disclosed. While specific embodiments of, and examples for, the present application are described below, it will be apparent to those of ordinary skill in the art that numerous modifications to the described embodiments and examples can be made without departing from the spirit and scope of the application.

[0061] The present application proposes a clutter simulation method and device for array antenna perception radar, which adopts a low-cost hardware architecture of single radio frequency channel (fusion preprocessing algorithm) + phase shifter to realize real-time simulation of clutter signals of multiple antenna elements of the perception radar antenna array, efficiently simulate 6G complex scene echoes, and effectively test the detection capability of the perception radar.

[0062] The present application is applicable to the scenario that the distance between the aperture of the perception radar and the scatterer (generating perception clutter, including city buildings, trees, grasslands, lamp poles, etc.) is relatively small compared with the perception radar, that is, the perception radar echoes from the scatterer can be regarded as parallel waves, which is consistent with most array antenna cases of the perception radar. Under this condition, the scatterer echoes received by each array element of the perception radar antenna only have a fixed phase difference.

[0063] The key problem solved by the present application is: how to obtain the received clutter signal of each array element of the antenna through the phase shifter of the single-channel clutter signal, which needs to fully consider the difference of the influence of clutter in different directions. The basic idea of the problem solved by the present application is: applying the synthesized directional diagram data to perform directional gain equivalent mapping on the clutter path components obtained by ray tracing, and then realizing that the array antenna clutter signal simulated by the phase shifter is consistent with the output of the conventional multi-channel simulated clutter signal after beamforming, and then completely describing the performance influence of the environment clutter on the perception radar. The present application mainly faces the clutter simulation problem of the perception radar meeting the far-field condition, and realizes the clutter simulation framework of the radio frequency channel of each antenna element of the perception radar through five stages of ray tracing, directional diagram synthesis, clutter response fusion, clutter signal generation, and single-channel to multi-channel phase shift mapping (as shown in Figure 2 The present application is particularly suitable for the clutter simulation scene of the perception system integrated with sensing, and can significantly reduce the hardware cost of clutter simulation while ensuring the performance of clutter simulation.

[0064] Embodiment one

[0065] The present embodiment discloses a clutter simulation method for array antenna perception radar, as shown in Figure 1 The present embodiment discloses a clutter simulation method for array antenna perception radar, as shown in

[0066] Step S1: single reference point ray tracing based on the 3D map of the environment where the radar is located to obtain each clutter path reaching the perception radar from different directions;

[0067] This step mainly completes: initializing the three-dimensional electromagnetic environment parameter set, modeling the clutter response based on the ray tracing method, and obtaining the clutter impulse response or frequency response function. The detailed process is as follows:

[0068] Step S1.1: initializing the three-dimensional electromagnetic environment parameter set, including scene geographic information and radar system parameter configuration;

[0069] The scene geographic information includes building models, plant models, column models and ground models; the radar system parameter configuration includes perception range, carrier frequency, loss rate, transmit power, pulse repetition frequency, pulse width duty cycle, bandwidth, sampling frequency, coherence time interval and radar position coordinates.

[0070] Step S1.2: modeling the clutter response based on the ray tracing method on the host computer to obtain the clutter impulse response or frequency response function;

[0071] Specifically, the ray tracing-based clutter time-domain response function is denoted as h(t), and the frequency-domain response function is denoted as H(f);

[0072] The process of obtaining the clutter impulse response is to determine the geometric position relationship between the clutter unit and the radar according to the geographic information of the specific area where the ground radar is located, to determine the propagation characteristics of each clutter path according to the propagation mechanism of the clutter path and the building and ground feature data, and to finally superimpose to form the total clutter.

[0073] The process of obtaining the clutter impulse response is as shown in Figure 3 The process of obtaining the clutter impulse response is as shown in

[0074] Step S1.2.1: converting the 3D map in the geodetic coordinate system into the 3D map in the radar coordinate system;

[0075] Specifically, the three-dimensional digital Figure 1 Generally, the geodetic coordinate system is used, and the response function of the building clutter and the ray tracing in the clutter simulation are generally completed in the radar coordinate system. Therefore, the conversion between the two coordinate systems needs to be completed.

[0076] Step S1.2.2: three-dimensional scene modeling;

[0077] Specifically, the scene modeling is the triangulation processing of the building surface, that is, accurately describing the geometric data of the building with a suitable data structure; in the three-dimensional digital map, the building is often described as the bottom edge projection of the building in the plane and the corresponding elevation; the bottom edge projection is generally a polygon composed of feature points of the bottom edge contour;

[0078] Step S1.2.3: smoothing the building contour to determine the smoothed building point set and height;

[0079] In a high-precision map, the number of bottom edge contour feature points is large, and the more the bottom edge contour points, the more the building side surfaces, and there are many adjacent points with close distances, which will cause a large number of very narrow building side surfaces after modeling, thereby increasing the time cost in the process of ray tracing and detecting the path, and therefore, the redundant points need to be smoothed to determine the smoothed building point set and height.

[0080] Step S1.2.4: triangulation of the building surface;

[0081] The bottom edge projection point set of the building is extended by using the corresponding height information (the bottom edge adjacent two points and the height can jointly determine a side surface of the building, and the bottom edge contour combined with the height information can obtain the top surface of the building), to form each side surface and top surface of the building; the side surface is directly connected to the diagonal line to realize triangulation, and for the top surface, the Delaunay triangulation algorithm is used for triangulation; the building side surface and top surface are thus modeled into many irregular triangular surfaces, and each triangular surface is represented by the vertex coordinates and the vertex serial number constituting the triangular surface.

[0082] Step S1.2.5: Visibility judgment for triangular faces and wedges

[0083] The radar emits rays with fixed solid angle intervals, and the direction of the ray is determined by the solid angle. For each ray, the intersection between the ray and all triangular faces is determined. If there is an intersection point, it indicates that the triangular face may block the ray. Track the ray to obtain all the planes intersecting the ray and the intersection point information. Compare these intersection points to determine the closest one to the radar, and the plane corresponding to the intersection point is the visible triangular face of the radar, and all other triangular faces are discarded. Poll the solid angle corresponding to all rays to obtain all visible triangular faces at the current radar position. Similarly, the visibility of the wedge is determined.

[0084] Step S1.2.6: Path search

[0085] The purpose of path search is to determine the ray path from the radar pointing in various directions, passing through the action of the building plane (possibly multiple times), and finally returning to the radar. Because of the complex building environment in the 6G sensing application scenario, in the clutter composition, the multi-path component ray mainly considers the ray path that returns to the radar receiving antenna after two reflections, one reflection + one diffraction; the backscattering component is the path that returns to the radar receiving antenna after one scattering (it needs to be noted that when the ray is perpendicular to the plane, backscattering is equivalent to one reflection). Considering the above assumptions, the main path search algorithm is the reverse ray tracing algorithm.

[0086] Step S1.2.7: Calculate the path loss of the ray

[0087] Apply the action mechanism model (reflection, diffraction, and backscattering) in step S1.2.6, combined with the spatial path loss, to determine the total loss of each ray path. Then, according to the radar parameters, including the radar pattern, radar action distance, maximum path loss setting, etc., to judge the effectiveness of the calculated ray path, if effective, save the path into the clutter effective path set.

[0088] The clutter path components obtained by ray tracing are shown in Figure 4 The angle, delay, and path loss of each path searched are stored respectively to form a clutter path ray data table.

[0089] Step S2: Synthesize the pattern according to the radar parameters to obtain the complete antenna pattern data table;

[0090] The main purpose of pattern synthesis is to obtain the complete pattern data of the radar antenna. In view of the antenna directivity parameters that can be obtained in engineering practice, there are three cases as follows: (1) the main lobe pointing direction, the main lobe 3dB beam width, the first side lobe pointing direction and the side lobe level, the second side lobe pointing direction and the side lobe level are known; (2) the E-plane pattern and the H-plane pattern of the antenna are known; (3) only part of the pattern parameters are known, such as only the main lobe pointing direction and the main lobe width, or only the side lobe level. The commonality of the above three ways is that they all describe part of the pattern information, and the antenna gain in any direction cannot be determined, therefore, the pattern synthesis method is needed to obtain the most detailed pattern gain data as possible.

[0091] Firstly, the pattern generated by the antenna array contains an element factor and an array factor. The element factor is the pattern of each antenna element itself, and the array factor is the pattern formed by beamforming on the antenna array. Assuming that each element pattern (element factor) is F e (θ,φ), which represents the radiation characteristics of a single antenna element. The array pattern (array factor) is S(θ,φ), which represents the influence of the array arrangement and the feeding phase on the radiation directivity. According to the pattern multiplication theorem, the total pattern function (hereinafter referred to as the array pattern) of the array antenna can be expressed as the product of the element factor and the array factor, where θ represents the elevation angle and φ represents the azimuth angle, i.e.

[0092] G(θ,φ)=F e (θ,φ)·S(θ,φ) (1)

[0093] In order to adapt to each given way of antenna parameters, three different methods are adopted respectively to synthesize the patterns of the three cases, which are described in detail as follows:

[0094] Case 1: The main lobe pointing direction, the main lobe 3dB beam width and the side lobe level are known

[0095] This case adopts the Gaussian fitting method for pattern synthesis; the Gaussian method is a mathematical modeling method for pattern synthesis, which is particularly suitable for the scene where the angles and levels of the main lobe and the first side lobe are relatively symmetrical and easy to express.

[0096] Let the main lobe 3dB beam width be χ 0.5 , the first side lobe level be g1, and the rest of the side lobe level be g2. The Gaussian function expression of the pattern is as follows:

[0097]

[0098] Wherein, χ χ is the angle difference between the radar beam pointing direction and the line connecting the radar and the detection area;

[0099] μ1 is the zero side lobe width of the main lobe and the first side lobe;

[0100] χ is the angle between the line connecting the radar position and the detection area and the transmission beam, and its expression is as follows:

[0101]

[0102] Where α is the angular difference between the detection area and the radar main lobe beam in azimuth, and β is the angular difference between the detection area and the radar main lobe beam in elevation.

[0103] The half-power angle of the first side lobe is χ s1 It is expressed as follows:

[0104]

[0105] Case 2: The antenna E-plane and H-plane patterns are known

[0106] In such cases, this embodiment supplements the sparse measurement data by interpolation, and uses the projection component method to perform vector synthesis on the E-plane directional pattern and the H-plane directional pattern components to reconstruct the three-dimensional directional pattern;

[0107] The specific implementation content includes the specific implementation of the interpolation method and the projection component. This method is divided into two steps:

[0108] 1) Third-order Hermite interpolation

[0109] In actual measurements, E-plane and H-plane pattern data are often sparse, meaning field strength values ​​are measured only at certain angles (such as elevation and azimuth), while data for other angles is missing. In such cases, interpolation methods are used to supplement the data.

[0110] The third-order Hermite interpolation is a commonly used interpolation method that can better handle the smoothness and accuracy of sparse data. This interpolation method converts the data x0,x1,x2,...x n Divide into multiple small intervals [x i ,x i+1 ], using a cubic polynomial P in each small interval i (x) to approximate the function and constrain the slope of each data point To ensure smooth curves. For non-uniformly spaced data, the derivative is calculated using the split difference formula.

[0111] 2) Pattern synthesis

[0112] After the interpolation, the field strength distribution of E-plane and H-plane pattern can be reconstructed by the method of projecting the components. E-plane and H-plane depend on the structure and the essential characteristics of the antenna, and have nothing to do with the placement and rotation of the antenna. For the convenience of modeling, E-plane and H-plane are usually placed on a certain φ plane (azimuth angle), and here E-plane is placed on XOZ plane (φ = 0°) and H-plane is placed on YOZ plane (φ = 90°). Thus, the E-plane and H-plane patterns are both functions of the elevation angle θ.

[0113] Suppose the pattern functions of E-plane and H-plane are represented as E(θ) and H(θ) respectively, then the gain amplitude at azimuth angle φ is composed of the vector addition of the projections of the two planes: the contribution of E-plane component varies with cosφ (projection to x-axis direction); the contribution of H-plane component varies with sinφ (projection to y-axis direction). The gain amplitude at different elevation angle θ and different azimuth angle φ is expressed as:

[0114]

[0115] Case three: only part of the pattern parameters

[0116] This case usually occurs when the device under test cannot provide detailed pattern parameters. In this case, the Doherty-Chebyshev fitting method is used for pattern synthesis. The basic idea is to optimize another index (the sidelobe level is as low as possible) as much as possible while ensuring the index of part of the pattern parameters (such as the main lobe width is constant).

[0117] Chebyshev polynomials are a class of weighted orthogonal polynomials, represented by T P (x), where P is the order. The recursive formula of Chebyshev polynomials is:

[0118] T P+1 (x) = 2xT P (x) - T P-1 (x) (6)

[0119] The explicit expression is:

[0120]

[0121] Dolph (C.L. Dolph) uses the characteristics of Chebyshev polynomials that |x| < 1 is constant, instead of the sidelobe of the pattern; the characteristics of x > 1 are sharply rising, instead of the main lobe of the pattern. Taking the symmetrically excited half-wavelength interval even line array as an example, its pattern function can be simplified as:

[0122]

[0123] Where the number of array elements is N = 2M, and M is the number of half array elements of the array;

[0124] n is the element number of the array half side, taking values 1, 2,..., M;

[0125] ω n is the weighting weight of the nth element of the array half side.

[0126] From the double angle formula, a(θ) is an N-1 order polynomial, which can correspond to a P=N-1 order Chebyshev polynomial, and we have:

[0127]

[0128] where x0 is the value position of the main lobe in the Chebyshev polynomial, and the main lobe maximum level is R0, i.e.:

[0129] T N-1 (x0) = R0 (10)

[0130] In the Chebyshev polynomial, the side lobe level is 1 relative to the main lobe level R0. The main lobe level is normalized and expressed in logarithmic form, the main lobe is 0 dB, and the side lobe level is:

[0131] P SLL = -20log 10 R0(dB) (11)

[0132] Therefore, the half-power lobe width θ 0.5 at R0 satisfies:

[0133]

[0134] By applying the above Chebyshev fitting relationship, the following two sub-situations can be fitted.

[0135] Sub-situation one: for a given main lobe width, the side lobe is as low as possible;

[0136] 1) According to the number of elements N, determine the order of Chebyshev polynomial N-1;

[0137] 2) According to the side lobe level P SLL , solve R0, and solve x0 by the explicit Chebyshev polynomial expression;

[0138]

[0139] Solve the excitation amplitude distribution by the Bartlett formula:

[0140]

[0141] where n = 1, 2,..., M;

[0142] 4) Normalization, solve the weight w n;

[0143]

[0144] The corresponding directional diagram data is obtained by using the calculated weight to perform beamforming on the symmetric excitation half-wavelength interval even linear array.

[0145] Sub-case two: for a given sidelobe level P SLL , the main lobe is as narrow as possible.

[0146] 1) According to the number of array elements N, determine the order of Chebyshev polynomial N-1;

[0147] 2) According to the given main lobe width θ 0.5 , put it into formula (12), replace R0 with the expression of x0, set the initial value, and solve x0 using numerical iteration method;

[0148] 3) The excitation amplitude distribution is solved by the Barbel formula:

[0149]

[0150] Where n=1, 2,..., M;

[0151] 4) Normalization, solve the weight w n :

[0152]

[0153] According to the weight of each array element, the weight vector W of the uniform linear array can be obtained:

[0154] W=(w1,w2,...,w n ) T (18)

[0155] The corresponding directional diagram data is obtained by using the calculated weight to perform beamforming on the symmetric excitation half-wavelength interval even linear array.

[0156] At this point, the complete antenna directional diagram data table formed after the beamforming of the perception radar is obtained, and the corresponding antenna gain value can be found from the data table by giving the azimuth angle and the elevation angle.

[0157] Step S3: Gain preprocessing is performed on each clutter path of the perception radar arriving from different directions to generate a clutter response in combination with the antenna directional diagram data table;

[0158] Specifically, the gain preprocessing is mainly performed on the clutter path components arriving at the perception radar from different directions, and then the clutter components arriving at the perception radar from different directions are equivalent to the clutter entering the perception radar array from the beam pointing direction, and the clutter characteristics of the two are the same after beamforming of the perception radar. It includes the following steps:

[0159] Step S3.1: Gain equivalent mapping is performed on the clutter path components arriving at the perception radar from different directions;

[0160] In the specific implementation process, the steps include the following:

[0161] Step S3.1.1: Select a clutter path, extract its angle of arrival information (θ, φ), and find the gain value of the direction information from the directional diagram data table obtained in step S2 according to the information;

[0162] Step S3.1.2: Multiply the found gain value with the path loss information extracted in the clutter path to update the new path loss.

[0163] Step S3.1.3: Perform S3.1.1 and S3.1.2 operations on all paths in the clutter path (i.e. the clutter path ray data table in the above) obtained in step S1.

[0164] Step S3.2: The clutter ray components forming the clutter path are superimposed and fused in the same time unit.

[0165] This step mainly realizes the generation of the clutter response function, and the specific process is as follows:

[0166] Step S3.2.1: Determine the maximum action distance of the perception radar according to the perception radar parameters;

[0167] Step S3.2.2: Sample the time according to the time resolution, and the maximum value of the time is twice the maximum action distance divided by the speed of light;

[0168] Step S3.2.3: Select a clutter path, extract its delay information, divide the delay by the time resolution, and round it to get the corresponding time sampling value;

[0169] Step S3.2.4: All paths are superimposed (vector sum) according to the calculated time sampling value;

[0170] Step S3.2.5: The superposition result is arranged as a clutter response sequence, which is the time domain response of the clutter.

[0171] In the implementation process, the generated clutter time domain response data is stored in the convolution module for the next calculation. In a feasible implementation, the data is stored in the storage unit of a digital signal processing hardware platform. In this implementation, the steps S1-S3 are executed in the host computer. The clutter response formed before and after the gain equivalent mapping of the clutter path ray is shown in FIGS. Figure 5 、 Figure 6

[0172] Step S4: Perform time domain convolution of the clutter response and the real-time collected radar transmission signal to generate a single-channel clutter signal.

[0173] In a feasible implementation, this step realizes real-time output of the clutter signal through a digital signal processing hardware platform (such as FPGA+DSP+RF). The specific implementation process is as follows:

[0174] Collect the radar excitation signal (i.e., the radar transmission signal) and locally cache the radar excitation signal.

[0175] Read the clutter response data stored in step S3 from the storage unit (corresponding to different wave positions and stored separately, and the storage medium is, for example, DDR), and perform time domain convolution with the collected radar excitation signal.

[0176] The convolution output digital signal is output to the RF unit through a DA converter for mixing output.

[0177] At this point, a single-channel clutter signal r(t) is generated.

[0178] Step S5: Perform one-to-many phase shift on the single-channel clutter signal through a phase shifter to output a multi-channel clutter signal.

[0179] In the implementation process, this embodiment uses an input-output phase shifter to realize mapping and transformation from a single-channel clutter signal to a clutter signal of each antenna element of an antenna array.

[0180] The working process of the input-output phase shifter is described below by taking a uniform surface array as an example:

[0181] Suppose the uniform surface array is as shown in FIG. Figure 7 The inter-element spacing is fixed at d = λ / 2, the array is in the spatial far field, and the in-phase wave front can be regarded as a plane wave, that is, the time delay reaching each array element is determined only by the array structure and the incoming wave direction. A coordinate system is established as shown in the figure with the array normal as the Z axis, and θ is the pitch angle and φ is the azimuth angle. Suppose there is a signal source in the far field region, and the direction cosine is

[0182]

[0183] Then:

[0184] ​cosα x = sinφ (20)

[0185] cosα y =cosφsinθ (21)

[0186] cosα z =cosφcosθ (22) The path difference Δr between adjacent array elements in the X-axis direction x :

[0187] Δr x =dcosα x =dsinφ (23) The path difference Δr between adjacent array elements in the Y-axis direction y :

[0188] Δr y =dcosα y =dcosφsinθ (24)

[0189] From this, we can get the phase difference Δφ of adjacent array elements on the X and Y axes: x , Δφ y :

[0190]

[0191] nth x row, nth y Direction vector of the array element at the column

[0192]

[0193] n x is the array element number along the x-axis, n x =1,2,3…,N;

[0194] n y is the array element number along the y-axis, n y =1,2,3…,N;

[0195] N is the number of array elements in the x-axis direction or the y-axis direction.

[0196] Then the nth x row, nth y The path difference between the array element at the column and the array element at the origin

[0197]

[0198] nth x row, nth y Phase difference between the array element at the column and the origin:

[0199]

[0200] The steering vector of the X-axis linear array is:

[0201]

[0202] The steering vector of the Y-axis linear array is:

[0203]

[0204] Therefore, the steering vector of the uniform rectangular array can be written as the Kronecker product of the two linear arrays or the vectorization of this matrix:

[0205]

[0206] The working principle of the phase shifter with single-channel input and multi-channel output is equivalent to left multiplying the input signal by a steering vector, that is:

[0207]

[0208] where a(θ main ,φ main ) represents the steering vector pointing in the direction (θ main ,φ main ) according to the antenna arrangement; r(t) is the single-channel clutter signal formed by step S4, and X i,j (t) is the clutter signal received by the array element corresponding to the i-th row and j-th column.

[0209] Figure 8 is a schematic diagram of the phase variation of the 16 antenna elements (4*4 planar array) over time after the phase shifter.

[0210] Up to now, the simulation of the environmental clutter signal received by each array element antenna of the perception radar antenna array has been realized.

[0211] The method of the present application realizes real-time simulation of the clutter signals of multiple antenna elements of the perception radar antenna array, and can significantly reduce the hardware cost of clutter simulation while ensuring the performance of clutter simulation.

[0212] Embodiment Two

[0213] The present embodiment proposes a clutter simulation device for array antenna perception radar, as shown in Figure 9 , comprising:

[0214] The acquisition module is configured to perform ray tracing based on a 3D map of the environment in which the radar is located to obtain various clutter paths reaching the perception radar from different directions.

[0215] determining module, configured to synthesize a directional diagram according to the sensing radar parameter to obtain a complete antenna directional diagram data table; to combine the antenna directional diagram data table to perform gain preprocessing on each clutter path to generate a clutter response; and to perform time domain convolution on the clutter response and a real-time collected radar transmitting signal to generate a single-channel clutter signal;

[0216] generating module, configured to perform one-to-many phase shift on the single-channel clutter signal through a phase shifter to output a multi-channel clutter signal.

[0217] The clutter simulation device for the array antenna sensing radar provided in this embodiment has the same technical features as the clutter simulation method for the array antenna sensing radar provided in the first embodiment, and can solve the same technical problems and achieve the same technical effects.

[0218] Embodiment three

[0219] The application provides a computer storage medium, which has a computer program stored thereon, and the program is executed by a processor to implement the clutter simulation method for the array antenna sensing radar in the first embodiment.

[0220] Those skilled in the art should understand that the embodiments of the application can be provided as a method, a system or a computer program product. Therefore, the application can be in the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Moreover, the application can be in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0221] Therefore, the application also provides a computer-readable storage medium, which has a computer program stored thereon, and the program is executed by a processor to implement the method described in any embodiment of the application. The computer-readable storage medium can be configured in any device of the application.

[0222] Embodiment four

[0223] The application provides an electronic device, which includes a memory, a processor and a computer program stored in the memory and executable by the processor, and the processor implements the clutter simulation method for the array antenna sensing radar as described in the first embodiment when executing the computer program.

[0224] For example, it includes one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, so that the one or more processors implement the method provided by the embodiments of the application. The method is contained in the functional description above, and will not be repeated here.

[0225] The electronic device also includes an input device and an output device; the processor, the storage device, the input device and the output device in the electronic device can be connected through a bus or other means.

[0226] The storage device, as a computer-readable storage medium, can be used to store software programs, computer-executable programs and module units, such as program instructions corresponding to the method in the embodiments of the present application. The storage device can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system and at least one application required by a function; the data storage area can store data created according to the use of the terminal, etc. In addition, the storage device can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some examples, the storage device can further include a memory remotely arranged with respect to the processor, and these remote memories can be connected through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. Each of the embodiments in the present application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment mainly describes the differences from other embodiments.

[0227] Embodiment five

[0228] The embodiment provides a computer program product, which includes a computer program or instructions, and the computer program or instructions are executed by a processor to realize the clutter simulation method for array antenna perception radar in the embodiment one.

[0229] Based on such understanding, the technical solution of the present application or the part of the technical solution which is essential or makes a contribution to the prior art can be embodied in the form of a computer program product.

[0230] Each of the embodiments in the present application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment mainly describes the differences from other embodiments.

[0231] The protection scope of the present application is not limited to the above-mentioned embodiments. Obviously, those skilled in the art can make various modifications and changes to the present disclosure without departing from the scope and spirit of the present disclosure. If these modifications and changes belong to the scope of the claims of the present disclosure and its equivalent technologies, the present disclosure also includes these modifications and changes.

Claims

1. A clutter simulation method for array antenna sensing radar, characterized in that: include: Ray tracing is performed based on a 3D map of the radar environment to obtain the paths of clutter reaching the perception radar from different directions; Perform pattern synthesis based on the perceived radar parameters to obtain a complete antenna pattern data table; Combined with the antenna pattern data table, gain preprocessing is performed on each clutter path to generate clutter response; Perform time domain convolution of the clutter response with the real-time collected radar transmission signal to generate a single-channel clutter signal; The single-channel clutter signal is subjected to one-to-many phase shifting by a phase shifter to output a multi-channel clutter signal.

2. The clutter simulation method for array antenna sensing radar according to claim 1, characterized in that: The performing pattern synthesis according to the perception radar parameters includes: In the first case where the main lobe direction, main lobe 3dB bandwidth and side lobe level of the directivity pattern are known, the directivity pattern is synthesized using the Gaussian fitting method; In the second case where the E-plane and H-plane patterns of the antenna are known, the sparse measurement data are supplemented by interpolation, and the E-plane and H-plane pattern components are vector-synthesized using the projection component method to reconstruct the three-dimensional pattern. In all other cases except the first and second cases, the Dolph-Chebyshev fitting method is used for pattern synthesis.

3. The clutter simulation method for array antenna sensing radar according to claim 1, characterized in that: The gain preprocessing of each clutter path arriving at the perception radar from different directions includes: Perform gain equivalent mapping on the clutter path components reaching the perception radar from different directions; The time units such as clutter ray components forming the clutter path are superimposed and fused.

4. The clutter simulation method for array antenna sensing radar according to claim 3, characterized in that: The performing gain equivalent mapping on the clutter path components arriving at the perception radar from different directions includes: Extracting the arrival angle information (θ, φ) of a clutter path, and finding the gain value in the direction of the clutter path from the antenna pattern data table; Multiply the found gain value by the path loss information extracted from the clutter path to update it into a new path loss; Repeat the above operation for all clutter paths in the antenna pattern data table.

5. The clutter simulation method for array antenna sensing radar according to claim 3, characterized in that: The superposition and fusion of time units such as clutter ray components forming the clutter path includes: Determine the maximum operating range of the perception radar based on the perception radar parameters; The time is sampled according to the time resolution, and the maximum time is twice the maximum action distance divided by the speed of light; Select a clutter path, extract the delay information of the clutter path, divide the delay time by the time resolution, and round it up to get the corresponding time sampling value; Performing equal delay superposition on all clutter paths according to the time sampling value; The superposition results are organized into a clutter response sequence, which is the clutter response.

6. The clutter simulation method for array antenna sensing radar according to claim 1, characterized in that: The one-to-many phase shifting of the single-channel clutter signal by the phase shifter includes: Establish a coordinate system with the antenna array normal as the Z axis, let θ be the elevation angle, φ be the azimuth angle, and assume there is a signal source in the far field. Calculate the direction cosines of this signal source. Multiply the X and Y axis components of the direction cosine by the preset array element spacing threshold to obtain the path difference between adjacent array elements in the X and Y axis directions; The phase difference between adjacent array elements is obtained according to the path difference between adjacent array elements; The phase difference between the array element at any position and the origin is obtained based on the phase difference of adjacent array elements in the X and Y axis components; The steering vectors of the linear array in the X and Y axis directions are obtained based on the phase difference between the array element at any position and the origin; Performing Kronecker product calculation on the steering vectors of the linear array in the X and Y axis directions can obtain the steering vector of the antenna array; The clutter signal received by the array antenna sensing radar is obtained by multiplying the single-channel clutter signal with the steering vector of the antenna array.

7. A clutter simulation device for array antenna sensing radar, characterized in that: include: An acquisition module is used to perform ray tracing based on a 3D map of the radar environment to obtain the clutter paths reaching the perception radar from different directions; The determination module is used to synthesize the pattern according to the perception radar parameters and obtain a complete antenna pattern data table; Combined with the antenna pattern data table, gain preprocessing is performed on each clutter path to generate clutter response; Perform time domain convolution of the clutter response with the real-time collected radar transmission signal to generate a single-channel clutter signal; The generation module is used to perform one-to-many phase shifting on the single-channel clutter signal through a phase shifter and output a multi-channel clutter signal.

8. A computer storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the clutter simulation method for array antenna sensing radar described in any one of claims 1 to 6 is implemented.

9. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable by the processor, wherein when the processor executes the computer program, the clutter simulation method for array antenna sensing radar according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the clutter simulation method for array antenna sensing radar described in any one of claims 1 to 6 is implemented.

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