Nonlinear frequency-modulated wave design method for near-field MIMO radar imaging application

Abstract

The invention provides a nonlinear frequency modulation wave design method for near-field MIMO radar imaging application. Relates to the technical field of radar sensing. The method comprises the following steps: determining echo information corresponding to a near-field MIMO radar system, wherein the echo information is used for indicating response of near-field MIMO radar imaging; according to the echo information, a first position and a second position of the echo information are determined, the first position is the position of a main lobe peak value in the echo information, and the second position is the position of a side lobe peak value in the echo information; according to the first position and the second position, multiple target parameters are determined, and the multiple target parameters are used for indicating the quality of transmitting signals of the array elements; according to the plurality of target parameters, processing a signal optimization coefficient corresponding to a transmitting signal of each array element in the radar system to obtain a plurality of target coefficients; and the emission signal of each array element is adjusted according to the target coefficient, so that the imaging quality of the near-field MIMO radar is improved.

Description

Technical Field

[0001] This application relates to the field of radar sensing technology, and in particular to a nonlinear frequency-modulated wave design method for near-field MIMO radar imaging applications. Background Technology

[0002] In near-field MIMO radar imaging applications, the high coupling between the range and angle dimensions causes the imaging position to shift relative to the real target position. To achieve high-quality imaging, the radar waveform needs to have the characteristics of low sidelobes, high resolution, and high accuracy.

[0003] In related technologies, nonlinear frequency modulation waveform design methods are typically used to optimize waveform parameters. During the optimization process, a weighted summation method is usually required to transform multiple objectives into a single objective for optimization.

[0004] However, in the above process, the weighting factors of each data need to be selected manually based on experience, which makes the optimization results less stable and makes it difficult to achieve a stable balance between low sidelobes, high resolution and high accuracy, resulting in low imaging quality of near-field MIMO radar. Summary of the Invention

[0005] This application provides a nonlinear frequency-modulated wave design method for near-field MIMO radar imaging applications, in order to solve the technical problem of low imaging quality in near-field MIMO radar.

[0006] In a first aspect, embodiments of this application provide a nonlinear frequency-modulated wave design method for near-field MIMO radar imaging applications, including:

[0007] Determine the echo information corresponding to the near-field multiple-input multiple-output (MIMO) radar system. The echo information is used to indicate the response of the near-field MIMO radar imaging.

[0008] Based on the echo information, determine the first position and the second position of the echo information. The first position is the position of the main lobe peak in the echo information, and the second position is the position of the side lobe peak in the echo information.

[0009] Based on the first position and the second position, multiple target parameters are determined, which are used to indicate the quality of the transmitted signal of each array element;

[0010] Based on multiple target parameters, the signal optimization coefficients corresponding to the transmitted signals of each array element in the radar system are processed to obtain multiple target coefficients;

[0011] The transmission signals of each array element are adjusted according to the target coefficient.

[0012] In one possible implementation, based on multiple target parameters, the signal optimization coefficients corresponding to the transmitted signals of each array element in the radar system are processed to obtain multiple target coefficients, including:

[0013] Based on multiple target parameters, multiple signal optimization coefficients are processed to obtain multiple signal optimization coefficients that satisfy non-dominance.

[0014] Based on multiple signal optimization coefficients that satisfy non-dominance, each signal optimization coefficient is updated to obtain multiple target coefficients.

[0015] In one possible implementation, the signal optimization coefficients are updated based on a plurality of signal optimization coefficients that satisfy non-dominance, resulting in a plurality of target coefficients, including:

[0016] Among multiple signal optimization coefficients that satisfy non-dominance, determine the signal optimization coefficients that satisfy the second threshold;

[0017] Determine the first coefficient associated with inertia, the second coefficient associated with individual learning ability, and the third coefficient associated with global learning ability;

[0018] Based on the signal optimization coefficients, first coefficient, second coefficient, and third coefficient that satisfy the second threshold, the iteration speed of each signal optimization coefficient is updated to obtain the updated iteration speed.

[0019] Based on the updated iteration speed, the positions of the optimization coefficients of each signal are updated to obtain the target coefficients.

[0020] In one possible implementation, determining a first coefficient associated with inertia, a second coefficient associated with individual learning ability, and a third coefficient associated with global learning ability includes:

[0021] Get the current iteration number, maximum iteration number, first range, second range, and third range. The first range is the range of variation of the first coefficient, the second range is the range of variation of the second coefficient, and the third range is the range of variation of the third coefficient.

[0022] Based on the current iteration number, the maximum iteration number, the first coefficient, and the first range, determine the first coefficient associated with inertia;

[0023] Based on the current iteration number, the maximum iteration number, the second coefficient, and the second range, determine the second coefficient that is associated with individual learning ability;

[0024] Based on the current iteration count, maximum iteration count, third coefficient, and third range, determine the third coefficient associated with global learning ability.

[0025] In one possible implementation, multiple target parameters are determined based on the first position and the second position, including:

[0026] Determine the target location of the radar system;

[0027] Based on the target location, the first location, and the second location, multiple target parameters are determined.

[0028] In one possible implementation, multiple target parameters are determined based on the target position, the first position, and the second position, including:

[0029] Based on the target location and the first location, first data and second data are determined. The first data is used to indicate the degree of offset in the echo information related to distance, and the second data is used to indicate the degree of offset in the echo information related to angle.

[0030] Based on the first position, determine the first main lobe width associated with distance in the echo information and the second main lobe width associated with angle in the echo information;

[0031] Based on the second position, determine the first sidelobe value associated with distance in the echo information and the second sidelobe value associated with angle in the echo information;

[0032] Based on the first data, the second data, the first main lobe width, the second main lobe width, the first side lobe value, and the second side lobe value, multiple target parameters are determined.

[0033] In one possible implementation, determining the echo information corresponding to the near-field multiple-input multiple-output (MIMO) radar system includes:

[0034] Obtain the frequency modulation function associated with the nonlinear frequency-modulated wave, and determine the transmitted signal of each element of the near-field multi-input multi-output (MIMO) radar system based on the frequency modulation function;

[0035] Determine the first signal delay for each array element to reach the target position;

[0036] Based on the transmitted signal, target position, and first signal delay, determine the received signal of each array element of the radar system;

[0037] The echo information is determined based on the transmitted signal, the received signal, and the second signal delay of each array element at the reference position.

[0038] Secondly, embodiments of this application provide a nonlinear frequency-modulated wave design device for near-field MIMO radar imaging applications, comprising: a first determining module, a second determining module, a third determining module, a processing module, and an adjustment module, wherein...

[0039] The first determining module is used to determine the echo information corresponding to the near-field multiple-input multiple-output (MIMO) radar system. The echo information is used to indicate the response of the near-field MIMO radar imaging.

[0040] The second determining module is used to determine the first position and the second position of the echo information based on the echo information. The first position is the position of the main lobe peak in the echo information, and the second position is the position of the side lobe peak in the echo information.

[0041] The third determining module is used to determine multiple target parameters based on the first position and the second position. These multiple target parameters are used to indicate the quality of the transmitted signal of each array element.

[0042] The processing module is used to process the signal optimization coefficients corresponding to the transmitted signals of each array element in the radar system based on multiple target parameters, and obtain multiple target coefficients.

[0043] The adjustment module is used to adjust the transmission signals of each array element according to the target coefficient.

[0044] In one possible implementation, the processing module is specifically used for:

[0045] Based on multiple target parameters, multiple signal optimization coefficients are processed to obtain multiple signal optimization coefficients that satisfy non-dominance.

[0046] Based on multiple signal optimization coefficients that satisfy non-dominance, each signal optimization coefficient is updated to obtain multiple target coefficients.

[0047] In one possible implementation, the processing module is further configured to:

[0048] Among multiple signal optimization coefficients that satisfy non-dominance, determine the signal optimization coefficients that satisfy the second threshold;

[0049] Determine the first coefficient associated with inertia, the second coefficient associated with individual learning ability, and the third coefficient associated with global learning ability;

[0050] Based on the signal optimization coefficients, first coefficient, second coefficient, and third coefficient that satisfy the second threshold, the iteration speed of each signal optimization coefficient is updated to obtain the updated iteration speed.

[0051] Based on the updated iteration speed, the positions of the optimization coefficients of each signal are updated to obtain the target coefficients.

[0052] In one possible implementation, the processing module is further configured to:

[0053] Get the current iteration number, maximum iteration number, first range, second range, and third range. The first range is the range of variation of the first coefficient, the second range is the range of variation of the second coefficient, and the third range is the range of variation of the third coefficient.

[0054] Based on the current iteration number, the maximum iteration number, the first coefficient, and the first range, determine the first coefficient associated with inertia;

[0055] Based on the current iteration number, the maximum iteration number, the second coefficient, and the second range, determine the second coefficient that is associated with individual learning ability;

[0056] Based on the current iteration count, maximum iteration count, third coefficient, and third range, determine the third coefficient associated with global learning ability.

[0057] In one possible implementation, the third determining module is specifically used for:

[0058] Determine the target location of the radar system;

[0059] Based on the target location, the first location, and the second location, multiple target parameters are determined.

[0060] In one possible implementation, the third determining module is further configured to:

[0061] Based on the target location and the first location, first data and second data are determined. The first data is used to indicate the degree of offset in the echo information related to distance, and the second data is used to indicate the degree of offset in the echo information related to angle.

[0062] Based on the first position, determine the first main lobe width associated with distance in the echo information and the second main lobe width associated with angle in the echo information;

[0063] Based on the second position, determine the first sidelobe value associated with distance in the echo information and the second sidelobe value associated with angle in the echo information;

[0064] Based on the first data, the second data, the first main lobe width, the second main lobe width, the first side lobe value, and the second side lobe value, multiple target parameters are determined.

[0065] In one possible implementation, the first determining module is used to:

[0066] Obtain the frequency modulation function associated with the nonlinear frequency-modulated wave, and determine the transmitted signal of each element of the near-field multi-input multi-output (MIMO) radar system based on the frequency modulation function;

[0067] Determine the first signal delay for each array element to reach the target position;

[0068] Based on the transmitted signal, target position, and first signal delay, determine the received signal of each array element of the radar system;

[0069] The echo information is determined based on the transmitted signal, the received signal, and the second signal delay of each array element at the reference position.

[0070] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0071] The memory stores instructions that the computer executes;

[0072] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0073] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0074] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0075] This application provides a nonlinear frequency-modulated wave design method for near-field MIMO radar imaging applications. The method involves determining the echo information corresponding to a near-field MIMO radar system, where the echo information indicates the response of the near-field MIMO radar imaging; determining a first position and a second position of the echo information based on the echo information, where the first position is the position of the main lobe peak and the second position is the position of the side lobe peak; determining multiple target parameters based on the first and second positions, where the target parameters indicate the quality of the transmitted signal of each array element; processing the signal optimization coefficients corresponding to the transmitted signals of each array element in the radar system based on the multiple target parameters to obtain multiple target coefficients; and adjusting the transmitted signals of each array element based on the target coefficients. In the above method, the electronic device constructs the optimization target by extracting key feature parameters from the echo response, without relying on manual experience to select weight factors. Furthermore, by simultaneously obtaining multiple waveform design schemes that achieve optimal balance on different performance indicators, it can obtain a transmission signal suitable for near-field MIMO radar imaging without the instability of optimization results caused by manually setting weights. This effectively solves the problems of low radar system performance and poor imaging quality caused by empirical weight selection in the prior art, and significantly improves the processing efficiency of signal optimization. Attached Figure Description

[0076] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0077] Figure 1 A schematic diagram illustrating the process of a nonlinear frequency-modulated wave design method for near-field MIMO radar imaging applications provided in this application;

[0078] Figure 2 A schematic diagram illustrating the process of determining the target coefficients provided in this application;

[0079] Figure 3 A schematic diagram illustrating the process of another nonlinear frequency-modulated wave design method for near-field MIMO radar imaging applications provided in this application embodiment;

[0080] Figure 4 A schematic diagram of a nonlinear frequency-modulated wave design device for near-field MIMO radar imaging applications provided in this application;

[0081] Figure 5 A schematic diagram of the structure of the electronic device provided in this application.

[0082] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0083] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0084] It should be noted that, in the description of this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. The terms "first," "second," etc., in this application are used to distinguish similar objects and are not used to describe a specific order or sequence.

[0085] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0086] In existing technologies, nonlinear frequency-modulated waveform design methods are typically employed, followed by waveform parameter optimization. This optimization process usually involves a weighted summation approach to transform multiple targets into a single target for optimization. However, this process requires manual selection of weighting factors based on experience, resulting in poor stability of the optimization results. It is difficult to consistently achieve a balance between low sidelobes, high resolution, and high accuracy, leading to lower imaging quality in near-field MIMO radar.

[0087] This application provides a nonlinear frequency-modulated wave design method for near-field MIMO radar imaging applications. By acquiring echo information from a near-field MIMO radar system, determining the first and second positions, and then extracting multiple target parameters for evaluating waveform quality, the method processes signal optimization coefficients based on these parameters to ultimately generate an optimized transmit waveform. In this method, the electronic device constructs the optimization target by extracting key feature parameters from the echo response, eliminating the need for manual selection of weighting factors based on experience. Furthermore, by simultaneously obtaining multiple waveform design schemes that achieve optimal balance across different performance indicators, it can obtain a transmit signal suitable for near-field MIMO radar imaging without the instability caused by manually setting weights. This effectively solves the problems of low radar system performance and poor imaging quality caused by empirical weight selection in existing technologies, significantly improving the processing efficiency of signal optimization. Moreover, this method is applicable to the signal design requirements of different near-field MIMO radar imaging scenarios, reducing the operational complexity of scenario adaptation and improving the imaging quality of near-field MIMO radar while enhancing the stability of transmit signal optimization.

[0088] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0089] Figure 1 A schematic diagram illustrating the process of a nonlinear frequency-modulated wave design method for near-field MIMO radar imaging applications provided in this application is shown below. Figure 1 As shown, the method includes:

[0090] S101. Determine the echo information corresponding to the near-field multiple input multiple output (MIMO) radar system.

[0091] The execution subject of this application embodiment can be an electronic device or a nonlinear frequency-modulated wave design device installed in an electronic device. The nonlinear frequency-modulated wave design device can be implemented through software or a combination of software and hardware. The electronic device can be a terminal device or a server.

[0092] The echo information is used to indicate the response of near-field MIMO radar imaging.

[0093] In some embodiments, the echo information can be the point spread function of MIMO radar imaging, where the point spread function can represent the response of the imaging system to an ideal point target, that is, the near-field MIMO imaging quality can be evaluated based on the radar waveform.

[0094] In some embodiments, the electronic device may determine the echo information corresponding to the near-field multiple-input multiple-output (MIMO) radar system based on the following implementation: acquiring a frequency modulation function related to a nonlinear frequency-modulated wave, and determining the transmitted signal of each element of the near-field multiple-input multiple-output (MIMO) radar system according to the frequency modulation function; determining a first signal delay for each element to reach the target position; determining the received signal of each element of the radar system according to the transmitted signal, the target position, and the first signal delay; and determining the echo information according to the transmitted signal, the received signal, and a second signal delay for each element at a reference position.

[0095] The frequency modulation function is used to generate the instantaneous frequency change law of the nonlinear frequency-modulated wave, that is, the waveform shape of the transmitted signal can be determined by the frequency modulation function.

[0096] In some embodiments, the sum of the linear frequency modulation term and the nonlinear frequency modulation term, which includes a Fourier series, can be used to determine the frequency modulation function associated with the nonlinear frequency-modulated wave, specifically expressed as follows:

[0097]

[0098] Where B represents the signal bandwidth and T represents the pulse repetition period. The nth Fourier series is used to control the characteristics of the nonlinear frequency-modulated wave. The linear frequency modulation term is used to ensure basic bandwidth coverage, and the nonlinear frequency modulation term is used to provide waveform optimization degrees of freedom through Fourier series expansion. In actual solution, the first N terms are taken.

[0099] In some embodiments, the electronic device can generate the complex envelope of the transmitted signals of the virtual array elements of the MIMO radar according to the frequency modulation function, that is, the transmitted signals of each element of the near-field multiple-input multiple-output MIMO radar system. and the corresponding phase function , can be represented as:

[0100]

[0101]

[0102] The target position is used to indicate the true spatial position of the ideal point target to be detected by the near-field MIMO radar, that is, the true spatial coordinates of the target in the polar coordinate system. In some embodiments, a three-dimensional coordinate system can be established with the center of the radar array as the origin, which can be represented as follows: ,in, This represents the distance from the target location to the center of the array. Indicates the azimuth angle.

[0103] The first signal delay is used to indicate the two-way delay from each element of the near-field MIMO radar to the target position. That is, the first signal delay is the two-way propagation delay of the signal from the transmitting element to the target position and back to the receiving element.

[0104] In some embodiments, the electronic device can calculate the first signal delay of each array element reaching the target position based on a near-field spherical wave propagation model. Specifically, assuming the antenna array is a linear array arranged on the x-axis, the array element coordinates can be obtained from... A complete description, the specific process can be represented as follows:

[0105]

[0106] in, This indicates the target position in polar coordinates detected by the m-th virtual array element. The first signal delay of the point target is given by c, where c is the wave velocity.

[0107] In some embodiments, the electronic device can transmit signals. Target location and the delay of the first signal The process of determining the received signals of each array element in the radar system can be represented as follows:

[0108]

[0109] in, This is an amplitude factor that incorporates path loss and target scattering rate. The carrier frequency is represented by the exponential term, which characterizes the carrier phase change.

[0110] In some embodiments, upon receiving the received signal Subsequently, in order to compress the signal pulse width to improve range resolution and suppress received noise to enhance the signal-to-noise ratio, matched filtering needs to be performed on each receiving channel. That is, the received signal of the m-th virtual array element is matched filtered. The specific process can be expressed as follows:

[0111]

[0112] The first position is used to indicate the spatial coordinates of the near-field MIMO radar test point in the polar coordinate system. In some embodiments, it can be represented as... ,in, Represented as the distance from the reference position to the center of the array. Indicates the azimuth angle.

[0113] The second signal delay is used to indicate the compensation delay for beam focusing.

[0114] In some embodiments, since the near-field signal is a spherical wave, there is a spatial time delay difference in the echoes received by different array elements. A reference position needs to be selected to eliminate the time delay difference in order to achieve coherent signal superposition. Assuming the array focuses the beam onto the reference position... It is necessary to apply time delay compensation to the signals of each array element, i.e., the second signal time delay. The specific process can be expressed as follows:

[0115]

[0116] in, This indicates the reference position of the m-th virtual array element in the polar coordinate system. The second signal delay, where c is the wave velocity.

[0117] In some embodiments, the electronic device can determine the echo information, i.e., the point spread function, based on the transmitted signal, the received signal, and the second signal delay of each array element at the reference position. Specifically, the signals of the M virtual array elements after time delay and phase compensation are coherently superimposed. The specific process can be expressed as follows:

[0118]

[0119] in, Indicates when the target location is At that time, the radar system relative to the reference position Imaging response, The received signal of the m-th array element is subjected to matched filtering and then time delay. The compensated output The term is a complex exponential term used to compensate for carrier phase caused by path difference, and M is the total number of virtual array elements. For carrier frequency.

[0120] In the specific calculation process, it can be and A second-order Taylor expansion approximation is performed, and spatial convolution is used to represent the wave propagation and scattering process, thereby transforming the spherical wave propagation model into a spatial convolution operation. The convolution is then processed using a fast Fourier transform, thereby improving computational efficiency by reducing complexity.

[0121] S102. Based on the echo information, determine the first and second positions of the echo information.

[0122] The first position refers to the location of the main lobe peak in the echo information, which is the position of the MIMO radar system relative to the location of the main lobe peak. The location of the target imaging result can be represented as:

[0123]

[0124] The second position is the location of the sidelobe peak in the echo information, which can be... .

[0125] S103. Determine multiple target parameters based on the first position and the second position.

[0126] Among them, multiple target parameters are used to indicate the quality of the transmitted signal of each array element.

[0127] In some embodiments, the electronic device may determine multiple target parameters based on a first position and a second position in the following manner: determining the target position emitted by the radar system; and determining multiple target parameters based on the target position, the first position, and the second position.

[0128] The target location emitted by the radar system can be .

[0129] In some embodiments, the electronic device may determine multiple target parameters based on a target location, a first location, and a second location, according to the following implementation: determining first data and second data based on the target location and the first location; determining a first main lobe width associated with distance and a second main lobe width associated with angle in the echo information based on the first location; determining a first side lobe value associated with distance and a second side lobe value associated with angle in the echo information based on the second location; and determining multiple target parameters based on the first data, the second data, the first main lobe width, the second main lobe width, the first side lobe value, and the second side lobe value.

[0130] The first data is used to indicate the degree of offset in the echo information related to distance, and the second data is used to indicate the degree of offset in the echo information related to angle.

[0131] In some embodiments, due to the distortion of the main lobe under approach conditions, the position of the imaging result is offset from the target position, which allows for the calculation of the target distance. Distance from the main lobe peak of the point spread function The absolute difference between them (first data), and the target angle Angle of main lobe peak of point spread function The absolute difference between them (second data) is the offset of the distance dimension. , can be represented as:

[0132]

[0133]

[0134] In some embodiments, since the main lobe width determines the imaging resolution of the MIMO radar system, the main lobe width in the range dimension... (First main lobe width) and main lobe width in the angular dimension (Second main lobe width), can be expressed as:

[0135]

[0136]

[0137] in, and The difference between the two distances corresponding to the point where the PSF amplitude drops to half the peak value of the main lobe, i.e., -3dB. Defined as the 3dB bandwidth of the main lobe in the distance dimension, i.e., the width of the first main lobe. and The difference between the two angle values ​​corresponding to the PSF amplitude decreasing to half of the main lobe peak value. This is the 3dB bandwidth of the main lobe in the angular dimension, i.e., the width of the second main lobe.

[0138] In some embodiments, since the peak-to-side-lobe ratio determines the performance of imaging weak targets and edge sharpness, the range-dimensional peak-to-side-lobe ratio (first sidelobe value) can be expressed as:

[0139]

[0140]

[0141] in, Indicates when the real target is At that time, the point spread function is at the peak position of the main lobe. The response value at that location, Indicates when the real target is At that time, the point spread function is at the sidelobe peak position The response value at that location, express Distance from the main lobe peak The position is more than half the width of the main lobe.

[0142] The angular peak-to-side-lobe ratio (first sidelobe value) can be expressed as:

[0143]

[0144]

[0145] in, Indicates when the real target is located At that time, the point spread function is at the peak position of the main lobe. The response value at that location, Indicates when the real target is At that time, the point spread function is at the sidelobe peak position The response value at that location, express Distance from the main lobe peak The position is more than half the width of the main lobe.

[0146] In some embodiments, the objective parameters can be used to construct an optimization problem, specifically including: offsetting the distance dimension. , distance dimension main lobe width Angular dimension main lobe width Distance to peak sidelobe ratio And the peak sidelobe ratio in the angular dimension In the form of unconstrained multi-objective optimization, it can be expressed as:

[0147]

[0148] Among them, optimization variables Given a vector consisting of the first N Fourier coefficients, minimize the distance dimension offset. and angular offset This helps ensure the accuracy of the imaging position and improve the stability of the imaging; it also minimizes the main lobe width. and It helps improve imaging resolution and maximize the peak-to-side-lobe ratio. and It helps to suppress sidelobes, prevent strong targets from obscuring weak targets, and ensure clear imaging edges. The various optimization objectives (target parameters) are mutually constrained, making it difficult to solve this multi-objective optimization problem using conventional methods.

[0149] S104. Based on multiple target parameters, process the signal optimization coefficients corresponding to the transmitted signals of each array element in the radar system to obtain multiple target coefficients.

[0150] The signal optimization coefficients are the optimization variables corresponding to the optimization problem. In this embodiment, the signal optimization coefficients are the Fourier series of the first N terms. These coefficients directly determine the time-frequency characteristics of the transmitted waveform, which in turn affects the shape of the final point target spread function (PSF).

[0151] The target coefficients are used to indicate the optimized global optimal solution, that is, the coefficients that meet the requirements of low sidelobes, high resolution and high accuracy of radar imaging.

[0152] In some embodiments, the electronic device may employ a multi-objective optimization algorithm to process the signal optimization coefficients corresponding to the transmitted signals of each array element in the radar system, obtaining multiple target coefficients. Specifically, the electronic device employs a multi-objective optimization algorithm to construct and solve a problem aimed at simultaneously optimizing multiple target parameters, thereby processing the signal optimization coefficients. This processing outputs a set of Pareto optimal solutions as multiple target coefficients.

[0153] S105. Adjust the transmission signals of each array element according to the target coefficient.

[0154] In some embodiments, the electronic device can convert the optimized target coefficients into the actual transmitted physical waveform. Specifically, from the Pareto optimal solution set, a set of optimal target Fourier coefficients is selected based on the specific requirements of the current near-field imaging task (low sidelobes, high resolution, and high accuracy). These coefficients are then used to reconstruct the spectrum of the transmitted waveform, and the final time-domain nonlinear frequency-modulated signal is generated through inverse Fourier transform. The optimized waveform is then loaded and uniformly applied to each transmitting element of the radar system, thereby achieving the expected high-performance near-field imaging effect with low sidelobes, high resolution, high accuracy, and low main lobe distortion in the MIMO radar system.

[0155] This application provides a nonlinear frequency-modulated wave design method for near-field MIMO radar imaging applications. It automatically extracts a first and second position from the echo information and determines multiple target parameters for evaluating waveform quality based on these parameters. Then, it performs multi-objective optimization processing on the signal optimization coefficients to ultimately obtain an optimized transmitted waveform. In this method, the electronic device automatically extracts key position information to construct optimization targets, eliminating the need for manual selection of weighting factors based on experience, effectively avoiding the instability of optimization results caused by subjective human factors. Simultaneously, the multi-objective optimization algorithm can simultaneously minimize the main lobe width, side lobe level, and main lobe distortion in both the range and angle dimensions, resulting in a nonlinear frequency-modulated wave with narrow main lobe, low sidelobe, and low distortion characteristics. Furthermore, this method not only significantly improves the efficiency and reliability of waveform design but also provides waveform schemes that achieve optimal balance among multiple performance indicators for different imaging scenarios. It effectively solves the problems of low radar system performance and poor imaging quality caused by empirical weight selection in traditional methods, greatly improving the overall performance of near-field MIMO radar imaging.

[0156] Figure 2 A schematic diagram illustrating the process of determining the target coefficients provided in this application, as shown below. Figure 2As shown, in this embodiment... Figure 1 Based on the examples, the method for determining the target coefficients is described in detail, and the method includes:

[0157] S201. Based on multiple target parameters, process multiple signal optimization coefficients to obtain multiple signal optimization coefficients that satisfy non-dominance.

[0158] In some embodiments, the electronic device may, in each generation of optimization iteration, select all Pareto optimal solutions (signal optimization coefficients) from the current population through non-dominated sorting and use a hypercube density estimation strategy to store these signal optimization coefficients.

[0159] In some embodiments, the electronic device may be initialized by determining the population size, initializing particle positions (signal optimization coefficients), initializing particle update rates, and initializing the non-dominated solution storage pool.

[0160] Specifically, the electronic device can be given the number of particles Np in the swarm, which can be set according to the specific needs of the actual application. Np determines the breadth of the initial search of the algorithm; and the Fourier coefficients of each particle in the swarm are initialized. Let N be a uniformly distributed N-dimensional random number vector on the interval [0, 1], and let the current optimal solution of each particle be... In this way, the initial solution is set to a uniform random number, which aims to maximize the diversity of the initial population, and the individual's historical best solution is initialized to itself, indicating that at the beginning of the iteration, each particle is in the optimal position.

[0161] The electronic device initializes the update rate (step size) for each particle. Let N be the zero vector, where velocity is... This represents the direction and distance a particle moves in each iteration. By initializing it to zero, it indicates that all particles are in a "stationary" state, and their subsequent movements will be driven entirely by individual and social cognition during the optimization process; furthermore, the storage pool REP, which stores all current non-dominated solutions (i.e., Pareto optimal solutions), is initialized to empty.

[0162] In some embodiments, after the electronic device initializes the population, it can evaluate all the initial solutions of the population, that is, through systematic performance evaluation and screening, identify the most competitive candidate solutions from the current population, and provide a data foundation for constructing the Pareto front.

[0163] Specifically, the electronic device needs to calculate the objective function value, which is done by traversing every particle in the population (i.e., every Fourier coefficient vector). Substituting this into the near-field MIMO radar system model, we calculate its specific values ​​for the multiple target parameters defined in S103, forming the target function vector, which can be expressed as:

[0164]

[0165] Electronic devices can be based on the calculated target vector set of all particles { The fast non-dominated sorting method is used to filter out all solutions that are not dominated by any other solution by comparing the dominance relationships between solutions pairwise.

[0166] In some embodiments, after the electronic device finds all non-dominated solutions in the current population, the electronic device can store or update all non-dominated solutions in the storage pool REP. At this time, all solutions in the storage pool REP together constitute the Pareto front of the optimization problem in the current iteration. That is, the storage pool has completely recorded all the best waveform schemes found up to the current iteration.

[0167] In some embodiments, after the electronic device has deposited all non-dominated solutions into the storage pool REP, the electronic device can partition the storage non-dominated solutions into a hypercube, that is, establish a structured spatial index and management framework for the non-dominated solutions in the storage pool REP. By discretizing the continuous target space, the electronic device lays the foundation for subsequent archive maintenance operations based on the diversity of spatial distribution.

[0168] Specifically, electronic devices establish a six-dimensional coordinate system, where each dimension represents an optimization objective. By equally dividing each dimension of the six-dimensional coordinate system, several six-dimensional hypercubes can be created. The division should ensure that the six-dimensional objective function value corresponding to each non-dominated solution in the storage pool is located in a hypercube, thereby improving storage efficiency while increasing storage utilization.

[0169] S202. Based on the multiple signal optimization coefficients that satisfy non-dominance, update each signal optimization coefficient to obtain multiple target coefficients.

[0170] In some embodiments, the electronic device may update each signal optimization coefficient according to a plurality of signal optimization coefficients that satisfy non-dominance to obtain a plurality of target coefficients: among the plurality of signal optimization coefficients that satisfy non-dominance, determine the signal optimization coefficients that satisfy a second threshold; determine a first coefficient associated with inertia, a second coefficient associated with individual learning ability, and a third coefficient associated with global learning ability; update the iteration speed of each signal optimization coefficient according to the signal optimization coefficients that satisfy the second threshold, the first coefficient, the second coefficient, and the third coefficient to obtain an updated iteration speed; update the position of each signal optimization coefficient according to the updated iteration speed to obtain the target coefficients.

[0171] The signal optimization coefficients that satisfy the second threshold can be the global optimal solution for the population. .

[0172] In some embodiments, the global optimal solution needs to be selected from the Pareto front. The specific process is as follows: For each non-empty hypercube, if the hypercube contains x non-dominated solutions, it is divided into 1 / x, thus obtaining a fractional vector X, whose dimension is the same as the number of non-empty hypercubes. This vector is then normalized. This yields the probability distribution for selecting each hypercube. It represents the 1 norm of a vector.

[0173] Sampling is performed based on this probability distribution. The hypercube in which the solution is located is then randomly sampled from x non-dominated solutions with equal probability within the hypercube. Such The selection method tends to explore the sparse region of non-dominated solutions to prevent over-exploitation of known optimal solutions. That is, the sparsity of the Pareto front in multi-objective optimization may lead to over-exploitation of local regions and affect the diversity of solutions. This method is beneficial to ensure the integrity of the Pareto front in multi-objective optimization problems.

[0174] The first coefficient can be the inertia coefficient, used to indicate the strength of the particle's tendency to maintain its previous search direction and velocity.

[0175] The second coefficient can be an individual learning factor, used to indicate the degree to which a particle tends to learn from its own historical best position, reflecting the particle's individual learning ability.

[0176] The third coefficient can be a global learning factor, used to indicate the degree to which a particle tends to learn from the global optimal solution of the population, reflecting the particle's social learning ability.

[0177] In some embodiments, the electronic device may determine a first coefficient associated with inertia, a second coefficient associated with individual learning ability, and a third coefficient associated with global learning ability based on the following implementation: obtaining the current iteration number, the maximum iteration number, a first range, a second range, and a third range, wherein the first range is the range of variation of the first coefficient, the second range is the range of variation of the second coefficient, and the third range is the range of variation of the third coefficient; determining the first coefficient associated with inertia based on the current iteration number, the maximum iteration number, the first coefficient, and the first range; determining the second coefficient associated with individual learning ability based on the current iteration number, the maximum iteration number, the second coefficient, and the second range; and determining the third coefficient associated with global learning ability based on the current iteration number, the maximum iteration number, the third coefficient, and the third range.

[0178] The current iteration number is the number of iteration rounds that have been executed, which can be represented as t.

[0179] The maximum number of iterations is the set total number of iteration rounds, which can be expressed as: .

[0180] The first range is the range of variation of the first coefficient, that is, the first range can be expressed as [ ].

[0181] The second range is the range of variation of the second coefficient, that is, the second range can be expressed as [ ].

[0182] The third range is the range of variation of the third coefficient, that is, the third range can be expressed as [ ].

[0183] The iteration rate, used to indicate the adjustment step size and direction of the signal optimization coefficients during the iteration process, can be expressed as: .

[0184] In some embodiments, the electronic device can be based on a first range [ The inertia coefficient, i.e., the first coefficient w, can be determined as follows:

[0185]

[0186] Where k takes a value greater than or equal to 1.

[0187] In this way, the inertia coefficient w can be designed to change as a tangent function within a first range as the number of iterations increases. That is, in the initial stage of optimization, the value of w is small, indicating that the current iteration speed is not significantly affected by the speed of the previous iteration, and it tends to explore the global scope, that is, to expand the search range and avoid getting trapped in local optima. In the middle and later stages of optimization, the value of w is large, indicating that the influence of historical trends is stronger, and it focuses more on local convergence, that is, to finely adjust near the high-quality solution and improve accuracy.

[0188] In some embodiments, in order to speed up the execution efficiency of the algorithm and prevent it from getting trapped in local optima, two learning factors, namely the second coefficient and the third coefficient, are introduced, which take values ​​in the second range and the third range, respectively.

[0189] Specifically, electronic devices can be configured according to the second range [ Determine the learning factors And can be based on the second range [ Determine the learning factors , can be represented as:

[0190]

[0191]

[0192] Thus, by establishing an adaptive update mechanism based on the inverse cosine function, the dynamic changes of the individual learning factor c1 and the global learning factor c2 are strictly limited to a preset boundary interval. ]and[ This design ensures that the learning factor does not diverge out of control throughout the optimization process, and follows a specific nonlinear trajectory. That is, it remains relatively stable in the early and late stages of iteration, and achieves a rapid and smooth transition in the middle stage. This avoids the search instability caused by drastic parameter fluctuations, and also achieves precise allocation of exploration and development capabilities. Thus, while ensuring the convergence of the algorithm, it effectively improves the distribution quality and diversity of the Pareto solution set.

[0193] In some embodiments, the electronic device can optimize coefficients based on a signal that satisfies a second threshold. First coefficient Second coefficient and the third coefficient The iteration speed of optimizing the coefficients of each signal The update process is performed to obtain the updated iteration velocity. Specifically, the particle velocity is updated by comparing the individual extreme value and the global extreme value with the historical best value in each iteration, ultimately obtaining the optimal solution to the objective function. The specific process can be represented as follows:

[0194]

[0195] in, This represents the velocity of a particle in multidimensional space. and It is a random number that follows a standard uniform distribution [0, 1]. This represents the optimal position of each particle obtained through searching in space. This represents the globally optimal position of the entire population.

[0196] In some embodiments, after the electronic device updates the individual particle velocities, it updates all individual particle solutions and local and global optimal solutions. The process of updating individual solutions can be expressed as follows:

[0197]

[0198] in, This represents the updated particle velocity.

[0199] In this way, the corresponding objective function value can be calculated separately using the updated individual solutions. ,like Dominate Then update the local optimum as Otherwise, no update will be performed; if It is not mutually dependent with any of the globally optimal solutions in the storage pool, or If a solution dominates one or more solutions in the storage pool, then... Place the solutions into the storage pool and the corresponding hypercube, and remove the dominated solutions from the storage pool and the hypercube; conversely, if... If a solution is dominated by one or more solutions in the storage pool, no operation will be performed.

[0200] In some embodiments, the electronic device updates the individual particle velocities and the individual solutions and local and global optimal solutions of all particles until the algorithm's iteration count reaches a preset maximum number of iterations. Then, the final solution set accumulated in the storage pool REP is the output of the algorithm, which yields the optimal nonlinear frequency modulated wave that balances main lobe offset, main lobe width, and peak sidelobe ratio, and has the characteristics of low main lobe offset, low main lobe width, and high peak sidelobe ratio.

[0201] Specifically, the storage pool REP contains a set of fully optimized nonlinear frequency-modulated Fourier coefficients. The waveform schemes corresponding to these coefficients together constitute the Pareto front of this multi-objective optimization problem. They are a series of design results that achieve optimal trade-offs in multiple key performance indicators such as main lobe offset, main lobe width, and peak-to-side lobe ratio, which can be flexibly selected by the system according to specific near-field imaging requirements.

[0202] This application provides a nonlinear frequency-modulated wave design method for near-field MIMO radar imaging applications. It employs a multi-objective particle swarm optimization algorithm to iteratively update the signal optimization coefficients that satisfy non-dominance. Specifically, it achieves intelligent updates of the position and velocity of the signal optimization coefficients by dynamically adjusting a first coefficient related to inertia, a second coefficient related to individual learning ability, and a third coefficient related to global learning ability. In this method, the electronic device effectively balances global exploration and local exploitation capabilities by adaptively adjusting learning parameters, significantly improving the convergence speed and stability of the optimization process. Furthermore, through a screening mechanism based on non-dominance relationships, it can simultaneously obtain multiple waveform design schemes that achieve optimal balance among different performance indicators, avoiding the limitation of traditional single-objective optimization that can only obtain a single compromise result. Moreover, this method not only ensures that the final nonlinear frequency-modulated wave possesses excellent characteristics such as narrow main lobe, low sidelobe, and low distortion, but also significantly improves the efficiency and reliability of waveform optimization, providing a high-performance and highly adaptable waveform solution for near-field MIMO radar imaging systems.

[0203] Figure 3 This is a schematic diagram illustrating another nonlinear frequency-modulated wave design method for near-field MIMO radar imaging applications provided in this application embodiment. Please refer to... Figure 3 The method may include:

[0204] S301. Establish a nonlinear frequency-modulated wave transceiver signal model for MIMO radar.

[0205] Electronic equipment constructs a nonlinear frequency-modulated wave signal model for a near-field MIMO radar system. Specifically, this includes: defining a method for characterizing the time-frequency characteristics of the nonlinear frequency-modulated wave, representing the frequency modulation function as the sum of the basic linear frequency-modulated component and the nonlinear component obtained from the Fourier series expansion; establishing the complex envelope model of the transmitted signal for each array element and its phase function expression; deriving the two-way delay (first signal delay) under near-field propagation conditions to obtain the received signal including path loss and phase change; and obtaining the baseband signal expression by performing matched filtering on each receiving channel. This model comprehensively describes the entire process from signal transmission and propagation to reception and processing, laying the foundation for the analysis of the system's imaging quality.

[0206] S302. Calculate the near-field MIMO radar point target spread function based on the received signal model.

[0207] The electronic device derives the point target spread function of the near-field MIMO radar system based on the established signal model. Specifically, this includes: calculating the required delay compensation (second signal delay) for each array element when the beam is focused at a specific test point; and obtaining the system's point target spread function (echo information) by performing phase compensation and coherent superposition on the matched filter outputs of each channel. This reflects key performance indicators such as the system's range and angle resolution and sidelobe characteristics, providing an accurate performance evaluation basis for subsequent optimization problems.

[0208] S303, Construct the optimization problem of Fourier coefficients for nonlinear frequency modulated waves.

[0209] Electronic devices are constructed using a point-target spread function to address a multi-objective optimization problem. Specifically, this involves extracting six key performance indicators from the point spread function: range offset (first data), angle offset (second data), range main lobe width (first main lobe width), angle main lobe width (second main lobe width), range peak-to-sidelobe ratio (first sidelobe value), and angle peak-to-sidelobe ratio (second sidelobe value). A mathematical optimization problem (with multiple objective parameters) is established, using the Fourier coefficient vector as the optimization variable (signal optimization coefficients) and aiming to simultaneously optimize the aforementioned six performance indicators. This transforms the waveform design problem into a standard unconstrained multi-objective optimization problem, laying the foundation for solving it using multi-objective optimization algorithms.

[0210] S304. Solve the nonlinear frequency modulated wave Fourier coefficient optimization problem using an improved multi-objective particle swarm optimization algorithm.

[0211] Electronic devices solve established optimization problems using an improved multi-objective particle swarm optimization algorithm. Specifically, this includes: designing a global optimal solution selection mechanism based on hypercube partitioning and density estimation to ensure the diversity of Pareto front distribution; establishing a parameter adaptive update strategy based on tangent and inverse cosine functions to achieve nonlinear adjustment of the inertia coefficient (first coefficient) and learning factors (second and third coefficients); and outputting a set of Pareto optimal solutions (target coefficients) that achieve optimal trade-offs among various performance indicators through non-dominated sorting and optimal solution retention in an external storage pool, providing multiple optimized waveform selections for different imaging scenarios.

[0212] Figure 4 A schematic diagram of a nonlinear frequency-modulated wave design device for near-field MIMO radar imaging applications provided in this application is shown below. Figure 4 As shown, the indicator data processing device 40 provided in this embodiment includes: a first determining module 41, a second determining module 42, a third determining module 43, a processing module 44, and an adjustment module 45, wherein,

[0213] The first determining module 41 is used to determine the echo information corresponding to the near-field multiple input multiple output MIMO radar system. The echo information is used to indicate the response of the near-field MIMO radar imaging.

[0214] The second determining module 42 is used to determine the first position and the second position of the echo information based on the echo information. The first position is the position of the main lobe peak in the echo information, and the second position is the position of the side lobe peak in the echo information.

[0215] The third determining module 43 is used to determine multiple target parameters based on the first position and the second position. The multiple target parameters are used to indicate the quality of the transmitted signal of each array element.

[0216] The processing module 44 is used to process the signal optimization coefficients corresponding to the transmitted signals of each array element in the radar system based on multiple target parameters to obtain multiple target coefficients.

[0217] The adjustment module 45 is used to adjust the transmission signals of each array element according to the target coefficient.

[0218] The nonlinear frequency-modulated wave design device for near-field MIMO radar imaging applications provided in this application embodiment can execute the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar, and will not be repeated here.

[0219] In one possible implementation, the processing module 44 is specifically used for:

[0220] Based on multiple target parameters, multiple signal optimization coefficients are processed to obtain multiple signal optimization coefficients that satisfy non-dominance.

[0221] Based on multiple signal optimization coefficients that satisfy non-dominance, each signal optimization coefficient is updated to obtain multiple target coefficients.

[0222] In one possible implementation, the processing module 44 is further configured to:

[0223] Among multiple signal optimization coefficients that satisfy non-dominance, determine the signal optimization coefficients that satisfy the second threshold;

[0224] Determine the first coefficient associated with inertia, the second coefficient associated with individual learning ability, and the third coefficient associated with global learning ability;

[0225] Based on the signal optimization coefficients, first coefficient, second coefficient, and third coefficient that satisfy the second threshold, the iteration speed of each signal optimization coefficient is updated to obtain the updated iteration speed.

[0226] Based on the updated iteration speed, the positions of the optimization coefficients of each signal are updated to obtain the target coefficients.

[0227] In one possible implementation, the processing module 44 is further configured to:

[0228] Get the current iteration number, maximum iteration number, first range, second range, and third range. The first range is the range of variation of the first coefficient, the second range is the range of variation of the second coefficient, and the third range is the range of variation of the third coefficient.

[0229] Based on the current iteration number, the maximum iteration number, the first coefficient, and the first range, determine the first coefficient associated with inertia;

[0230] Based on the current iteration number, the maximum iteration number, the second coefficient, and the second range, determine the second coefficient that is associated with individual learning ability;

[0231] Based on the current iteration count, maximum iteration count, third coefficient, and third range, determine the third coefficient associated with global learning ability.

[0232] In one possible implementation, the third determining module 43 is specifically used for:

[0233] Determine the target location of the radar system;

[0234] Based on the target location, the first location, and the second location, multiple target parameters are determined.

[0235] In one possible implementation, the third determining module 43 is further configured to:

[0236] Based on the target location and the first location, first data and second data are determined. The first data is used to indicate the degree of offset in the echo information related to distance, and the second data is used to indicate the degree of offset in the echo information related to angle.

[0237] Based on the first position, determine the first main lobe width associated with distance in the echo information and the second main lobe width associated with angle in the echo information;

[0238] Based on the second position, determine the first sidelobe value associated with distance in the echo information and the second sidelobe value associated with angle in the echo information;

[0239] Based on the first data, the second data, the first main lobe width, the second main lobe width, the first side lobe value, and the second side lobe value, multiple target parameters are determined.

[0240] In one possible implementation, the first determining module 41 is used to:

[0241] Obtain the frequency modulation function associated with the nonlinear frequency-modulated wave, and determine the transmitted signal of each element of the near-field multi-input multi-output (MIMO) radar system based on the frequency modulation function;

[0242] Determine the first signal delay for each array element to reach the target position;

[0243] Based on the transmitted signal, target position, and first signal delay, determine the received signal of each array element of the radar system;

[0244] The echo information is determined based on the transmitted signal, the received signal, and the second signal delay of each array element at the reference position.

[0245] This embodiment provides a nonlinear frequency-modulated wave design device for near-field MIMO radar imaging applications. It can execute the method provided in the above-described method embodiment, and its implementation principle and technical effect are similar. This embodiment will not elaborate further here.

[0246] Figure 5 A schematic diagram of the structure of the electronic device provided in this application. Figure 5 As shown, the electronic device 50 provided in this embodiment includes at least one processor 51 and a memory 52. ​​Optionally, the electronic device 50 further includes a communication component 53. The processor 51, memory 52, and communication component 53 are connected via a bus.

[0247] In a specific implementation, at least one processor 51 executes computer execution instructions stored in memory 52, causing at least one processor 51 to perform the above-described method.

[0248] The specific implementation process of processor 51 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0249] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0250] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0251] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0252] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0253] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0254] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0255] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0256] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0257] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0258] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0259] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0260] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0261] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for designing nonlinear frequency-modulated wave for near-field MIMO radar imaging applications, characterized in that, include: Determine the echo information corresponding to the near-field multiple-input multiple-output (MIMO) radar system, the echo information being used to indicate the response of the near-field MIMO radar imaging; Based on the echo information, a first position and a second position of the echo information are determined. The first position is the position of the main lobe peak in the echo information, and the second position is the position of the side lobe peak in the echo information. Based on the first position and the second position, a plurality of target parameters are determined, which are used to indicate the quality of the transmitted signal of each array element; Based on the multiple target parameters, the signal optimization coefficients corresponding to the transmitted signals of each array element in the radar system are processed to obtain multiple target coefficients; The transmission signals of each array element are adjusted according to the target coefficient.

2. The method of claim 1, wherein, Based on the multiple target parameters, the signal optimization coefficients corresponding to the transmitted signals of each array element in the radar system are processed to obtain multiple target coefficients, including: Based on the multiple target parameters, multiple signal optimization coefficients are processed to obtain multiple signal optimization coefficients that satisfy non-dominance. Based on the multiple signal optimization coefficients that satisfy non-dominance, each signal optimization coefficient is updated to obtain multiple target coefficients.

3. The method of claim 2, wherein, Based on the multiple signal optimization coefficients that satisfy non-dominance, each signal optimization coefficient is updated to obtain multiple target coefficients, including: Among the multiple signal optimization coefficients that satisfy non-dominance, the signal optimization coefficients that satisfy the second threshold are determined; Determine the first coefficient associated with inertia, the second coefficient associated with individual learning ability, and the third coefficient associated with global learning ability; Based on the signal optimization coefficients that satisfy the second threshold, the first coefficient, the second coefficient, and the third coefficient, the iteration speed of each signal optimization coefficient is updated to obtain the updated iteration speed; Based on the updated iteration speed, the positions of the optimized coefficients of each signal are updated to obtain the target coefficients.

4. The method of claim 3, wherein, Determine the first coefficient associated with inertia, the second coefficient associated with individual learning ability, and the third coefficient associated with global learning ability, including: Obtain the current iteration number, the maximum iteration number, the first range, the second range, and the third range, where the first range is the variation range of the first coefficient, the second range is the variation range of the second coefficient, and the third range is the variation range of the third coefficient; Based on the current iteration number, the maximum iteration number, the first coefficient, and the first range, determine the first coefficient associated with inertia; Based on the current iteration number, the maximum iteration number, the second coefficient, and the second range, a second coefficient associated with individual learning ability is determined; The third coefficient associated with global learning ability is determined based on the current iteration number, the maximum iteration number, the third coefficient, and the third range.

5. The method according to claim 1, characterized in that, Based on the first position and the second position, multiple target parameters are determined, including: Determine the target location emitted by the radar system; Based on the target location, the first location, and the second location, multiple target parameters are determined.

6. The method according to claim 5, characterized in that, Based on the target location, the first location, and the second location, multiple target parameters are determined, including: Based on the target location and the first location, first data and second data are determined, wherein the first data is used to indicate the degree of offset associated with distance in the echo information, and the second data is used to indicate the degree of offset associated with angle in the echo information; Based on the first position, determine the first main lobe width associated with distance in the echo information and the second main lobe width associated with angle in the echo information; Based on the second position, determine the first sidelobe value associated with distance in the echo information and the second sidelobe value associated with angle in the echo information; The plurality of target parameters are determined based on the first data, the second data, the first main lobe width, the second main lobe width, the first side lobe value, and the second side lobe value.

7. The method according to claim 5 or 6, characterized in that, Determine the echo information corresponding to the near-field multiple-input multiple-output (MIMO) radar system, including: Obtain the frequency modulation function associated with the nonlinear frequency-modulated wave, and determine the transmitted signal of each element of the near-field multi-input multi-output (MIMO) radar system based on the frequency modulation function; Determine the first signal delay for each array element to reach the target position; The received signals of each array element of the radar system are determined based on the transmitted signal, the target position, and the delay of the first signal. The echo information is determined based on the transmitted signal, the received signal, and the second signal delay of each array element at the reference position.

8. A nonlinear frequency-modulated wave design device for near-field MIMO radar imaging applications, characterized in that, include: The system comprises a first determining module, a second determining module, a third determining module, a processing module, and an adjustment module, wherein... The first determining module is used to determine the echo information corresponding to the near-field multiple-input multiple-output (MIMO) radar system, and the echo information is used to indicate the response of the near-field MIMO radar imaging; The second determining module is used to determine a first position and a second position of the echo information based on the echo information, wherein the first position is the position of the main lobe peak in the echo information and the second position is the position of the side lobe peak in the echo information; The third determining module is used to determine multiple target parameters based on the first position and the second position, the multiple target parameters being used to indicate the quality of the transmitted signal of each array element; The processing module is used to process the signal optimization coefficients corresponding to the transmitted signals of each array element in the radar system according to the multiple target parameters, so as to obtain multiple target coefficients; The adjustment module is used to adjust the transmission signal of each array element according to the target coefficient.

9. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

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