A radar test system and method based on multi-mode dynamic reconstruction

Abstract

The present application relates to the technical field of radar testing, and specifically relates to a radar testing system and method based on multi-mode dynamic reconstruction. The system comprises a multi-mode radio frequency link dynamic reconstruction module, a baseband signal processing module, a spatial positioning module, an intelligent diagnosis and visualization module, and a closed-loop cross-linking control module. The present application realizes the integration of three working modes of target simulation, power measurement and T / R component testing through multi-mode radio frequency link dynamic reconstruction, realizes the automatic identification and diagnosis of T / R component faults through the intelligent diagnosis and visualization module, ensures the testing accuracy through high-precision spatial positioning, realizes the seamless switching between modes through closed-loop cross-linking control, and significantly improves the efficiency and accuracy of radar testing.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of radar testing, in particular to a radar testing system and method based on multi-mode dynamic reconstruction. BACKGROUND

[0002] With the continuous improvement of the complexity of modern radar systems, radar testing technology has become a key link to ensure the performance of radar systems. Traditional radar testing methods are mainly designed for single functions. In different testing needs such as target simulation, power measurement and T / R component testing, independent test systems usually need to be built.

[0003] In the prior art, Chinese patent CN115712097A discloses a radar active module testing system, which connects a switch matrix and a power division network through a host computer to realize amplitude and phase parameter measurement of multi-band, multi-polarization and multi-channel radar active modules, thereby reducing the complexity of manual operation to a certain extent. Chinese patent CN118393437A discloses a digital component compensation testing system based on partial signal correlation, which realizes the switching and distribution of transmit and receive signals between the tested component and the reference component through a signal hub, thereby improving the testing efficiency of digital T / R components. Chinese patent CN118151110B proposes a fast testing system for full-polarization radar antennas, which uses a radio frequency routing unit to select a radio frequency signal path, thereby solving the problem of frequent line changing in the testing process of phased array antennas. In addition, Chinese patent CN115664548A discloses a multi-channel digital radar antenna testing system, which realizes automatic testing of multi-channel digital radar antennas through the coordination of a scanning rack subsystem and a radio frequency subsystem.

[0004] However, the above prior art solutions still have the following shortcomings: First, the existing testing system has relatively single function, and cannot integrate multiple working modes such as target simulation, power measurement and T / R component testing in the same set of equipment, resulting in the need to build multiple independent systems when performing comprehensive testing, which not only increases the cost of equipment and occupies space, but also introduces additional measurement errors due to multiple radio frequency connections. Second, the existing T / R component testing solution has limitations in spatial positioning accuracy control, and cannot realize high-precision automatic alignment of the antenna phase center and the center line of the radar antenna unit, so the tester needs to rely on manual adjustment, which is low in efficiency and easy to introduce human errors. Third, the traditional testing system lacks intelligent fault diagnosis capability, mainly relying on the experience of testers for data interpretation and abnormality identification, making it difficult to realize the correlation analysis and automatic diagnosis of T / R component multi-channel amplitude and phase data. Finally, the existing technology often needs to interrupt the transmission of radio frequency signals when switching between multiple testing modes, which cannot realize seamless switching in the true sense, seriously restricting the improvement of testing efficiency. SUMMARY

[0005] In order to solve the technical problems of single function, system fragmentation of traditional radar test equipment, and the fact that the T / R component test scheme cannot simultaneously meet the requirements of accurate spatial positioning and intelligent fault diagnosis, and achieve the technical effects of organic unification of multiple test functions, high-precision spatial positioning and intelligent diagnosis, the application provides a radar test system and method based on multi-mode dynamic reconstruction.

[0006] According to an aspect of the application, a radar test system based on multi-mode dynamic reconstruction is provided, comprising: a multi-mode radio frequency link dynamic reconstruction module, configured to integrate three working modes of target simulation, power measurement and T / R component test, and to realize reconstruction of a radio frequency signal link under the control of a control instruction through a programmable radio frequency switch matrix; a baseband signal processing module adopting a multi-core heterogeneous architecture, configured to perform spectrum analysis and target motion parameter calculation on a radar echo signal, and to generate a control instruction; a spatial positioning module adopting a multi-sensor fusion closed-loop control architecture, configured to control the alignment error of an antenna phase center relative to the center line of a radar antenna unit within a preset error range in a T / R component test mode; an intelligent diagnosis and visualization module, which is preconfigured with an amplitude and phase detection criterion library, performs correlation analysis on multi-channel amplitude and phase data of a T / R component based on a deep learning model, automatically identifies abnormal channels and generates a visual diagnosis report; and a closed-loop cross-linking control module, which adopts a gigabit Ethernet to build a distributed control network, configured to perform mute switching, calibration parameter loading, link self-checking and system clock synchronization when switching modes, and to realize seamless switching of the three working modes within a radio frequency signal gap.

[0007] Optionally, the programmable radio frequency switch matrix in the multi-mode radio frequency link dynamic reconstruction module comprises a plurality of single-pole multi-throw switches, each of which is switched according to a control instruction to realize rapid reconstruction of a signal link in different working modes.

[0008] Optionally, the multi-mode radio frequency link dynamic reconstruction module forms a bidirectional test link in a T / R test mode; the bidirectional test link comprises a transmitting branch and a receiving branch, the transmitting branch comprises a phase-adjustable phase shifter and a power amplifier, the receiving branch comprises a programmable attenuator and an I / Q demodulator, and the link insertion loss is compensated in real time.

[0009] Optionally, the multi-sensor fusion closed-loop control architecture comprises: a dual-frequency laser interferometer for constructing a three-dimensional reference coordinate system, at least three position-sensitive detectors, a piezoelectric ceramic driven translation stage, and a fuzzy PID controller; wherein the laser interferometer adopts dual-wavelength interference fringe counting of red light and green light for absolute distance measurement.

[0010] Optionally, the deep learning model-based correlation analysis of the T / R component multi-channel amplitude and phase data automatically identifies abnormal channels and generates a visual diagnostic report, comprising: multi-scale feature extraction and channel correlation analysis of the amplitude and phase response data of the T / R component based on the deep learning model, automatically generating a three-dimensional visual diagnostic report containing fault type, level and repair suggestion.

[0011] Optionally, the deep learning model comprises an Inception-v4 network structure containing a radar-specific frequency domain pooling branch for extracting key frequency band responses in a three-dimensional feature tensor composed of amplitude, phase, frequency and timestamp; the channel correlation analysis uses a graph attention network, wherein the nodes are the channels of the T / R component, and the edge weights are dynamically calculated based on the amplitude and phase correlation between channels.

[0012] Optionally, when switching modes, performing silent switching, calibration parameter loading, link self-checking and system clock synchronization, comprising: completing the reconstruction of the radio frequency switch matrix in the radio frequency signal idle gap, and loading the pre-stored gain and phase compensation coefficients, performing link self-checking through the built-in calibration signal source, and simultaneously using the IEEE 1588 precise time protocol to realize that the clock synchronization error of each module is less than the preset clock synchronization error.

[0013] According to the second aspect of the present application, a radar test method based on multi-mode dynamic reconstruction is provided, comprising: integrating target simulation, power measurement and T / R component test three working modes through a multi-mode radio frequency link dynamic reconstruction module, and realizing the reconstruction of the radio frequency signal link under the control of the control instruction through a programmable radio frequency switch matrix; through a baseband signal processing module, using a multi-core heterogeneous architecture, performing spectrum analysis and target motion parameter solving on the radar echo signal, and generating a control instruction; through a spatial positioning module, using a multi-sensor fusion closed-loop control architecture, in the T / R component test mode, realizing that the alignment error of the antenna phase center relative to the center line of the radar antenna unit is controlled within a preset error range; through an intelligent diagnosis and visualization module, presetting an amplitude and phase detection criterion library, based on a deep learning model, performing correlation analysis on the T / R component multi-channel amplitude and phase data, automatically identifying abnormal channels and generating a visual diagnostic report; through a closed-loop cross-linking control module, using a gigabit Ethernet to build a distributed control network, when switching modes, performing silent switching, calibration parameter loading, link self-checking and system clock synchronization, realizing seamless switching of the three working modes within the radio frequency signal gap.

[0014] According to the third aspect of the present application, an electronic device is provided, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0015] According to a fourth aspect of the present application, a computer readable storage medium is provided, which stores a computer program, and the computer program, when executed by a processor, implements the steps of the above method.

[0016] The present application has the advantages that: compared with the prior art, the organic integration of multiple test functions is realized, three functions of target simulation, power measurement and T / R component test are integrated on the same hardware platform, repeated investment of multiple independent systems is avoided, and the equipment cost is reduced; through the radio frequency link dynamic reconstruction technology, the rapid switching of the three working modes within 5ms is realized, the additional error introduced by multiple connections is eliminated, and the test precision is improved; the high-precision spatial positioning of ±1mm is realized by using the laser positioning device, the strict positioning requirements of the T / R component test are met, and the positioning precision is greatly improved compared with the traditional manual adjustment mode; through the intelligent diagnosis and visualization module, the automatic interpretation and three-dimensional graphical output of the test data are realized, the manual interpretation mode is replaced, and the test efficiency and accuracy are significantly improved; a complete closed-loop control system is established, the collaborative work and fault automatic recovery of each functional module are realized, and the reliability and automation level of the system are improved. BRIEF DESCRIPTION OF DRAWINGS

[0017] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and serve to explain the principles of the application, and do not limit the application in any way.

[0018] Figure 1 The system framework diagram of the present application.

[0019] Figure 2 The overall flowchart of the present application.

[0020] Figure 3 The schematic diagram of the electronic device. DETAILED DESCRIPTION

[0021] Embodiments of the present application will be described in detail below, examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference signs represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as limiting the present application, and the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0022] Embodiment one

[0023] In view of the problems of single function and system fragmentation of the conventional radar test equipment in the prior art, multiple independent systems need to be built to respectively complete target simulation, power measurement and T / R component test when multi-dimensional test is performed, which leads to high equipment cost and introduces additional errors due to multiple connections. The existing T / R component test scheme cannot simultaneously meet the requirements of accurate spatial positioning and intelligent fault diagnosis, and the test personnel need to manually adjust the antenna position and manually interpret the test data, which is low in efficiency and prone to errors. Seamless switching of multiple test modes and accurate spatial positioning constraints become key bottlenecks restricting the improvement of radar test efficiency. The present application provides a radar test system based on multi-mode dynamic reconstruction, please refer to Figure 1 . The system includes a multi-mode radio frequency link dynamic reconstruction module, a baseband signal processing module, a spatial positioning module, an intelligent diagnosis and visualization module, and a closed-loop crosslinking control module.

[0024] The multi-mode radio frequency link dynamic reconstruction module integrates three working modes of target simulation, power measurement and T / R component test on the same hardware platform. The module adopts a programmable radio frequency switch matrix as a core reconstruction device to build a full-duplex signal path containing 12 radio frequency ports. The programmable radio frequency switch matrix includes multiple single-pole multi-throw switches, and each single-pole multi-throw switch is switched according to a control instruction to realize rapid reconstruction of the signal link in different working modes. The module receives the mode switching instruction sent by the upper computer through the FPGA controller and completes the hardware reconstruction of the radio frequency link within 5ms.

[0025] In the target simulation mode, the signal flow is excitation signal input→6-bit digital attenuator→up-conversion module→programmable filter group→output port. The up-conversion module adopts a two-stage mixing structure, the first stage moves the signal to an intermediate frequency of 1.2GHz, and the second stage generates a 9.8GHz local oscillator through a phase-locked loop synthesizer to complete the final frequency conversion. In the power measurement mode, the measured radar signal is connected through a directional coupler→low-noise amplifier→tunable bandpass filter→peak detection circuit→16-bit ADC sampling.

[0026] In the T / R component test mode, the multi-mode radio frequency link dynamic reconstruction module forms a bidirectional test link. The bidirectional test link includes a transmit branch and a receive branch, the transmit branch includes a phase-adjustable phase shifter and a power amplifier, and the receive branch includes a programmable attenuator and an I / Q demodulator, and real-time compensation of link insertion loss is realized. The transmit branch is a baseband signal generator→phase-adjustable phase shifter→power amplifier→measured T / R component, and the receive branch is a measured component output signal→programmable attenuator→down-converter→I / Q demodulator. During the test process, the link insertion loss is compensated in real time through closed-loop calibration technology.

[0027] The baseband signal processing module adopts a multi-core heterogeneous architecture, and is used for performing spectrum analysis on radar echo signals, solving target motion parameters, and generating control instructions. The module includes an adaptive window function Fourier transform engine, a multi-motion model joint solver, a Doppler compensation unit, and an instruction packaging and transmission unit. The adaptive window function Fourier transform engine performs spectrum analysis by using a Nuttall window function, performs windowing processing on 2048-point IQ data, then completes 4096-point FFT operation through an FFT accelerator, and finally performs amplitude normalization processing. The multi-motion model joint solver fuses a uniform speed model and a uniform acceleration model by using an alpha-beta-gamma filter, and innovatively introduces a motion mode probability weight. The Doppler compensation unit compensates for an accuracy of 0.1 Hz by using a recursive least square method to update coefficients in real time based on a polynomial fitting frequency offset correction algorithm.

[0028] In one embodiment of the present application, the adaptive window function Fourier transform engine performs spectrum analysis by using a Nuttall window function, and a window function expression is as follows: wherein, , , , N is a sampling point number, n is an nth sampling point, is a time domain window function sequence. Compared with a traditional Hanning window, the window function has a lower sidelobe level (-98 dB), and is particularly suitable for weak signal detection in a multi-target scene.

[0029] In one embodiment of the present application, the multi-motion model joint solver fuses a uniform speed model and a uniform acceleration model by using an alpha-beta-gamma filter, and a state equation is as follows: wherein, is a distance in a T sampling time, is a distance in a T-1 sampling time, is a radial velocity in the T sampling time, is a radial velocity in the T-1 sampling time, is an acceleration in the T sampling time, is an acceleration in the T-1 sampling time, T is a sampling interval, is noise in distance measurement, is noise in radial velocity measurement, is noise in acceleration measurement. A motion mode probability weight is introduced, and a formula is as follows: wherein, is a uniform speed model confidence, is a speed measurement variance, is a current acceleration estimation value, is a time interval.

[0030] In one embodiment of the present application, the polynomial fitting based frequency offset correction algorithm of the Doppler compensation unit is: wherein, is the Doppler frequency offset value at time t, is the polynomial coefficient obtained by least square fitting, which determines the shape of the frequency offset curve, t is the time variable, and n is the polynomial order, is the fitting compensation constant.

[0031] The spatial positioning module adopts a multi-sensor fusion closed-loop control architecture, which is used to control the alignment error of the antenna phase center relative to the center line of the radar antenna unit within a preset error range in the T / R component test mode. The multi-sensor fusion closed-loop control architecture includes a dual-frequency laser interferometer for constructing a three-dimensional reference coordinate system, at least three position sensitive detectors, a piezoelectric ceramic driven translation stage, and a fuzzy PID controller. The laser interferometer adopts red and green dual-wavelength interference fringe counting for absolute distance measurement, emits dual-color laser beams with wavelengths of 633 nm red light and 532 nm green light, and realizes absolute distance measurement through wavelength synthesis, with a measurement resolution of 0.1 μm. Three sets of PSD position sensitive detectors constitute a detection array, each PSD outputs the center coordinates of the light spot, and the position of the antenna phase center is calculated by the least square method. The piezoelectric ceramic driven high precision translation stage has a dynamic response bandwidth of 500 Hz. The fuzzy PID controller adopts a prediction-correction algorithm, inputs the deviation between the laser measurement value and the target position into the controller, dynamically adjusts the proportional coefficient, integral coefficient and differential coefficient according to the error size, and realizes the control of the spatial positioning error within ±1 mm.

[0032] In one embodiment of the present application, the formula for realizing absolute distance measurement by wavelength synthesis of the dual-frequency laser interferometer is: wherein, L is the absolute distance, , Nred and Ngreen are the interference fringe counts of the two laser beams, , λred and λgreen are the emission wavelengths of the two laser beams.

[0033] In one embodiment of the present application, three sets of PSD position sensitive detectors are deployed to constitute a detection array, and each PSD position sensitive detector outputs the center coordinates of the light spot The formula for calculating the position of the antenna phase center by the least square method is: wherein, A is the detection array containing the PSD installation azimuth angle parameter, b is the vector from each PSD position sensitive detector to the center of the light spot, is the position of the antenna phase center.

[0034] In one embodiment of the present application, the piezoelectric ceramic driven translation stage serves as a motion compensation actuator, and its displacement The relationship between the driving voltage is: wherein, is the displacement amount, is the driving voltage, is the polynomial coefficient, and β is the nonlinear coefficient. The calibration is performed by a cubic polynomial fitting.

[0035] In an embodiment of the present application, the prediction-correction algorithm is specifically: inputting the deviation between the laser measurement value and the target position into a fuzzy PID controller, and outputting a residual error, the formula being: wherein, is the proportional coefficient, is the integral coefficient, is the differential coefficient, is the deviation between the laser measurement value at time t and the target position, is the residual error, is the deviation between the laser measurement value at time t-1 and the target position, is the deviation between the laser measurement value at time t-2 and the target position.

[0036] The intelligent diagnosis and visualization module predefines an amplitude-phase detection criterion library, performs correlation analysis on the multi-channel amplitude-phase data of the T / R assembly based on a deep learning model, automatically identifies abnormal channels, and generates a visual diagnosis report. The module can include a data preprocessing submodule, a multi-scale feature extraction submodule, a fault diagnosis decision submodule, and a three-dimensional visualization submodule. The data preprocessing submodule performs Z-Score normalization processing on the T / R assembly test data, eliminates the influence of the dimension, and constructs a three-dimensional feature tensor including the amplitude response, the phase response, the frequency point, and the timestamp.

[0037] The deep learning model comprises an Inception-v4 network structure, and a radar special frequency domain pooling branch is arranged in the Inception-v4 network structure, so as to extract a key frequency band response in a three-dimensional feature tensor composed of amplitude, phase, frequency and time stamp. A multi-scale feature extraction submodule adopts the Inception-v4 network structure, and a radar special frequency domain pooling branch is arranged in a traditional Inception module, so as to realize key frequency band feature extraction through FFT transformation and a frequency band selection convolution kernel. In a preferred embodiment of the present application, the Inception-v4 network structure is designed as follows: (1) a basic module retains the original 1x1, 3x3 and 5x5 parallel convolution paths of the Inception-v4; (2) a radar special frequency domain pooling branch is newly added, so as to form a four-way parallel structure; and (3) the output feature maps of the paths are fused through depth concatenation. The implementation steps of the radar special frequency domain pooling branch comprise: (1) a time-frequency transformation layer: performing short-time Fourier transformation on each feature channel of the input three-dimensional feature tensor, and the formula is as follows: wherein, is a frequency spectrum, and the dimension is time (T) x frequency (F) x channel (C) x frequency point (K); K is the frequency point number of STFT, a Hanning window is adopted, the window length is 128 points, and the overlap rate is 50%; STFT is a short-time Fourier transformation function, which converts a signal to a time-frequency domain, and X is the input three-dimensional feature tensor; (2) a frequency band selection layer: designing a learnable frequency band selection convolution kernel wherein, M is the number of key frequency bands; then, frequency band importance weighting is realized through 1D convolution, and the formula is as follows: wherein, is an output weighted frequency band feature vector, sigma is a Sigmoid activation function, b is a bias term, and * is a convolution operation; (3) a feature compression layer: a frequency domain attention mechanism is adopted to calculate a frequency band weight, and the formula is as follows: , wherein, GAP is global average pooling, MLP is a two-layer fully connected network, alpha is a frequency band weight vector, is a weighted frequency band feature, is a weighted feature of the i-th frequency band, is the weight of the i-th frequency band, and M is the number of frequency bands. The implementation steps of multi-scale feature fusion comprise: (1) the output feature map of the traditional Inception path contains space domain (spatial or temporal) and frequency domain information; (2) the output feature map of the radar special frequency domain pooling branch contains key frequency band features; and (3) feature fusion is performed, and the formula is as follows: wherein, is a learnable fusion weight matrix, This is the final fused feature map. Channel correlation analysis uses a graph attention network (GAT), where nodes represent channels of the T / R component, and edge weights are dynamically calculated based on the amplitude-phase correlation between channels. The fault diagnosis decision submodule uses a graph attention network (GAT) for channel correlation analysis, with the following formula: ,in, Let be the edge weights between node feature i and node feature j, and W be the learnable weight matrix. Here, || represents the attention vector, and || represents the concatenation operation. For node feature i, Let j be the node feature. The fault level classification uses a dynamic threshold method, with the following formula: ,in, This is a dynamic threshold used to determine the fault level, where β is a smoothing coefficient that adaptively adjusts the threshold based on historical fault data. Within the time window k The moving average of the correlation deviation index between a channel and its adjacent channels. Within the time window k The maximum value of the correlation deviation index between a channel and its adjacent channels.

[0038] This system uses a deep learning model to perform correlation analysis on multi-channel amplitude and phase data of a T / R component, automatically identifying abnormal channels and generating a visual diagnostic report. This includes multi-scale feature extraction and channel correlation analysis of the T / R component's amplitude and phase response data using a deep learning model, automatically generating a 3D visual diagnostic report containing fault type, level, and repair suggestions. The 3D visualization submodule constructs an interactive 3D coordinate system based on WebGL. The X-axis represents the channel number, the Y-axis represents the test frequency, and the Z-axis represents the amplitude and phase deviation value. Abnormal channels are displayed in red in the 3D coordinate system, and a diagnostic report is automatically generated containing the abnormal channel number, fault type, fault level, and recommended actions. For example, taking test data from a T / R component containing 64 channels as an example, after correlation analysis, an interactive 3D coordinate system is constructed, and a red mark is displayed at 15GHz for channel 32 in the interactive 3D coordinate system. The automatically generated diagnostic report includes: abnormal channel 32, fault type is phase mismatch, and recommended action is to check the phase shifter control circuit.

[0039] The closed-loop interconnection control module employs a gigabit Ethernet network to construct a distributed control network. During mode switching, it performs silent switching, calibration parameter loading, link self-testing, and system clock synchronization, achieving seamless switching between the three operating modes within RF signal gaps. This module utilizes a distributed control architecture, constructing a star topology control network via gigabit Ethernet to enable real-time coordinated control of all functional modules. The module includes a status monitoring subsystem, link reconfiguration control logic, timing synchronization mechanisms, and fault recovery strategies.

[0040] During mode switching, silent switching, calibration parameter loading, link self-checking and system clock synchronization are performed, including completing radio frequency switch matrix reconstruction in the radio frequency signal idle gap, and loading pre-stored gain and phase compensation coefficients, performing link self-checking through the built-in calibration signal source, and simultaneously using the IEEE 1588 precise time protocol to realize that the clock synchronization error of each module is less than the preset clock synchronization error. Three-step triggering mechanisms are performed during mode switching: pre-detection verifies the availability of the hardware resources required for the target mode, silent switching completes switch matrix reconfiguration in the radio frequency signal gap, and post-calibration automatically loads the corresponding calibration coefficients. The timing synchronization mechanism uses the IEEE 1588 precise time protocol to realize that the clock synchronization error of each module is less than 100 ns. In addition, the application also designs a fault recovery strategy for abnormal processing, which is a three-level abnormal processing mechanism, including: the module level triggers a reset through a local watchdog timer, the system level starts a backup link by a main controller, and the network level automatically switches to a redundant control channel. For example, when an abnormality that the port VSWR suddenly rises to 3.5 is detected, the abnormal processing steps include: (1) module level: automatically reduce the transmit power by 20 dB; (2) system level: switch to a backup radio frequency channel; (3) record the fault code to the black box memory.

[0041] The radar test system realizes the integration of three working modes of target simulation, power measurement and T / R component test through multi-mode radio frequency link dynamic reconstruction, realizes the automatic identification and diagnosis of T / R component faults through intelligent diagnosis and visualization modules, ensures the test accuracy through high-precision spatial positioning, realizes seamless switching between modes through closed-loop cross-linking control, and significantly improves the efficiency and accuracy of radar testing.

[0042] Embodiment Two

[0043] This embodiment is based on the above-mentioned embodiment one, and provides a radar test method based on multi-mode dynamic reconstruction, please refer to Figure 2 , a radar test system based on multi-mode dynamic reconstruction applied to embodiment one, the method realizes comprehensive testing of the radar system through the collaborative work of the multi-mode radio frequency link dynamic reconstruction module, the baseband signal processing module, the spatial positioning module, the intelligent diagnosis and visualization module, and the closed-loop cross-linking control module. The specific implementation steps are as follows.

[0044] S1, integrate three working modes of target simulation, power measurement and T / R component test through the multi-mode radio frequency link dynamic reconstruction module, and realize the reconstruction of the radio frequency signal link under the control of the control instruction through the programmable radio frequency switch matrix.

[0045] S2, use the multi-core heterogeneous architecture of the baseband signal processing module to perform spectrum analysis and target motion parameter solving on the radar echo signal, and generate control instructions.

[0046] S3, by the spatial positioning module, the multi-sensor fusion closed loop control architecture is adopted, in the T / R component test mode, the alignment error of the antenna phase center relative to the radar antenna unit center line is controlled within the preset error range.

[0047] S4, by the intelligent diagnosis and visualization module, the amplitude and phase detection criterion library is preset, the correlation analysis of the multi-channel amplitude and phase data of the T / R component is carried out based on the deep learning model, the abnormal channel is automatically identified, and the visual diagnosis report is generated.

[0048] S5, by the closed loop crosslinking control module, a distributed control network is constructed by using gigabit Ethernet, when the mode is switched, the mute switching, calibration parameter loading, link self-checking and system clock synchronization are executed, and the seamless switching of the three working modes in the radio frequency signal gap is realized.

[0049] In an embodiment of the present application, the method first integrates three working modes of target simulation, power measurement and T / R component test on the same hardware platform through a multi-mode radio frequency link dynamic reconstruction module. The module uses a programmable radio frequency switch matrix as the core reconstruction device, and constructs a full-duplex signal path containing 12 radio frequency ports. Through the FPGA controller, the mode switching instruction sent by the host computer is received, and the hardware reconstruction of the radio frequency link is completed within 5ms. In the target simulation mode, the signal flow is excitation signal input→6-bit digital attenuator→up-conversion module→programmable filter group→output port. The up-conversion module adopts a two-stage mixing structure, the first stage moves the signal to an intermediate frequency of 1.2GHz, and the second stage generates a 9.8GHz local oscillator through a phase-locked loop synthesizer to complete the final frequency conversion. In the power measurement mode, the measured radar signal is connected through a directional coupler→low-noise amplifier→tunable band-pass filter→peak detection circuit→16-bit ADC sampling. In the T / R component test mode, a bidirectional test link is formed, the transmission branch is baseband signal generator→phase-adjustable phase shifter→power amplifier→measured T / R component, and the receiving branch is measured component output signal→program-controlled attenuator→down-converter→I / Q demodulator. During the test process, the link insertion loss is compensated in real time through the closed loop calibration technology.

[0050] The baseband signal processing module adopts a multi-core heterogeneous architecture to perform spectrum analysis and target motion parameter calculation on radar echo signals. The module includes an adaptive window function Fourier transform engine, which uses the Nuttall window function for spectrum analysis, with a lower sidelobe level of -98 dB compared to the traditional Hanning window, making it particularly suitable for weak signal detection in multi-target scenarios. The multi-motion model joint solver uses an alpha-beta-gamma filter to fuse uniform and uniform acceleration models, and innovatively introduces motion pattern probability weights. The Doppler compensation unit uses a polynomial fitting frequency offset correction algorithm, with recursive least squares method to update the coefficients in real time, with a compensation accuracy of 0.1 Hz. After processing, control commands containing timestamps, distances, speeds, accelerations, and target numbers are generated and sent to each control module via Gigabit Ethernet using UDP protocol.

[0051] The spatial positioning module adopts a multi-sensor fusion closed-loop control architecture to achieve accurate alignment of the antenna phase center in T / R component test mode. The module includes a laser reference establishment module that uses a dual-frequency laser interferometer to construct a three-dimensional coordinate system, emitting dual-color laser beams with wavelengths of 632.8 nm and 532 nm, and achieving absolute distance measurement through wavelength synthesis with a measurement resolution of 0.1 μm. The six-degree-of-freedom pose detection module deploys three sets of PSD position sensitive detectors to form a detection array, with each PSD position sensitive detector outputting the spot center coordinates, and the antenna phase center position is calculated by the least squares method. The motion compensation actuator uses a piezoelectric ceramic driven high precision translation stage with a dynamic response bandwidth of 500 Hz. The closed-loop control algorithm uses a predictive-correction algorithm, which inputs the deviation between the laser measurement value and the target position into a fuzzy PID controller, to control the alignment error of the antenna phase center relative to the center line of the radar antenna unit within ±1 mm.

[0052] The intelligent diagnosis and visualization module presets the T / R component amplitude and phase detection criteria library, and performs correlation analysis on the T / R component multi-channel amplitude and phase data based on a deep learning model. The module uses a multi-scale feature fusion algorithm based on deep learning, the data preprocessing submodule normalizes the T / R component test data, and constructs a three-dimensional feature tensor containing amplitude response, phase response, frequency point and timestamp. The multi-scale feature extraction submodule uses the Inception-v4 network structure, and adds a radar-specific frequency domain pooling branch in the traditional Inception module. The fault diagnosis decision submodule uses Graph Attention Network for channel correlation analysis, and the fault level division uses a dynamic threshold method that adapts to historical fault data. The three-dimensional visualization submodule constructs an interactive three-dimensional coordinate system based on WebGL, with the X-axis representing the channel number, the Y-axis representing the test frequency, and the Z-axis representing the amplitude and phase deviation value. It automatically identifies abnormal channels and generates a visualization diagnosis report containing abnormal channel number, fault level and recommended measures.

[0053] The closed-loop crosslinking control module adopts a gigabit Ethernet to build a distributed control network, and performs silent switching, calibration parameter loading, link self-checking and system clock synchronization when switching modes. The module adopts a distributed control architecture, and builds a star topology control network through a gigabit Ethernet. The state monitoring subsystem is deployed on a hardware state acquisition unit on the FPGA, and polls key parameters of each module at a period of 10 ms, including the port standing wave ratio of the radio frequency switch matrix, the local oscillator locking state of the frequency conversion module and the memory occupancy rate of the baseband processor. The link reconstruction control logic performs a three-step triggering mechanism of pre-detection, silent switching and post-calibration when switching modes, the pre-detection verifies the availability of hardware resources required by the target mode, the silent switching completes the switch matrix reconstruction within the radio frequency signal gap, and the post-calibration automatically loads the corresponding calibration coefficients. The timing synchronization mechanism adopts the IEEE 1588 precision time protocol, and realizes that the clock synchronization error of each module is less than 100 ns. The fault recovery strategy establishes a three-level abnormality processing mechanism of the module level, the system level and the network level, realizes seamless switching of the three working modes within the radio frequency signal gap, and the entire switching process takes no more than 8 ms.

[0054] Embodiment Three

[0055] This embodiment is based on the above-mentioned embodiment two, and further provides an electronic device, please refer to the attached Figure 3 , Figure 3 The electronic device shown is merely an example, and should not impose any limitation on the functions and use range of the embodiments of the present disclosure.

[0056] As Figure 3 shown, the electronic device can include a processing device (e.g., a central processor, a graphics processor, etc.) that can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) or loaded from a storage device into a random access memory (RAM). In the RAM, various programs and data required for the operation of the electronic device are also stored. The processing device, the ROM, and the RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0057] In general, the following devices can be connected to the I / O interface: input devices including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, etc.; output devices including, for example, a liquid crystal display (LCD), a speaker, etc.; storage devices including, for example, a magnetic tape, a hard disk, etc.; and communication devices. The communication devices can allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 The electronic device with various devices is shown, but it should be understood that it is not required to implement or have all the devices shown. More or less devices can be alternatively implemented or provided. Figure 3Each block in the flow diagrams of FIGS. 1-3 can represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that the

[0058] In particular, the processes described above with reference to the flow diagrams can be implemented as a computer software program according to some embodiments of the present disclosure. For example, some embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer readable medium, the computer program comprising program code for implementing the methods illustrated by the flow diagrams. In some such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processing device, the above-mentioned functions defined in the methods of some embodiments of the present disclosure are implemented.

[0059] Embodiment Four

[0060] This embodiment is based on the above-mentioned embodiment two, and further provides a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the steps of the above-mentioned method.

[0061] It should be noted that, in some embodiments of this disclosure, the computer-readable medium described above may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), or any suitable combination thereof.

[0062] In some embodiments, the client and server may communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and may interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0063] The computer readable medium can be contained in the device or exist separately and not assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: dynamically reconstruct three working modes of target simulation, power measurement and T / R component test through a multi-mode radio frequency link module, and realize reconstruction of a radio frequency signal link under control of a control instruction through a programmable radio frequency switch matrix; through a baseband signal processing module, perform spectrum analysis and target motion parameter calculation on a radar echo signal using a multi-core heterogeneous architecture, and generate a control instruction; through a spatial positioning module, realize alignment of an antenna phase center relative to a radar antenna unit center line within a preset error range in a T / R component test mode using a multi-sensor fusion closed-loop control architecture; through an intelligent diagnosis and visualization module, preset an amplitude and phase detection criterion library, perform correlation analysis on multi-channel amplitude and phase data of a T / R component based on a deep learning model, automatically identify an abnormal channel and generate a visual diagnosis report; through a closed-loop cross-linking control module, build a distributed control network using a gigabit Ethernet, perform mute switching, calibration parameter loading, link self-checking and system clock synchronization when switching modes, and realize seamless switching of the three working modes within a radio frequency signal gap.

[0064] Computer program code for carrying out operations of some embodiments of the disclosure can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0065] The computer program product of the first aspect can include a computer readable storage medium. The computer readable storage medium can include instructions. The instructions can include one or both of: instructions for causing a computer to implement a multi-mode radio frequency link dynamic reconfiguration unit; and instructions for causing a computer to implement a baseband signal processing unit. The computer readable storage medium can include instructions for causing a computer to implement a spatial positioning unit. The computer readable storage medium can include instructions for causing a computer to implement an intelligent diagnosis and visualization unit. The computer readable storage medium can include instructions for causing a computer to implement a closed loop crosslink control unit.

[0066] The units described in some embodiments of the present disclosure can be implemented in software or in hardware. The described units can also be arranged in a processor, for example, a processor can be described as including a multi-mode radio frequency link dynamic reconfiguration unit, a baseband signal processing unit, a spatial positioning unit, an intelligent diagnosis and visualization unit, and a closed loop crosslink control unit. In some cases, the names of these units do not constitute a limitation on the units themselves, for example, the multi-mode radio frequency link dynamic reconfiguration unit can also be described as a unit that integrates three working modes of target simulation, power measurement, and T / R component testing, and realizes the reconfiguration of the radio frequency signal link under the control of the control instruction through a programmable radio frequency switch matrix.

[0067] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that can be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system on a chip (SOCs), complex programmable logic devices (CPLDs), etc.

[0068] It is apparent that a person skilled in the art should understand that the above-described embodiments of the steps of the present application can be performed in a manner different from the present application, and the simulation methods and experimental equipment include but are not limited to the above description. The above-described steps of the present application can be performed in a different order in some cases, and the steps shown or described above can be performed separately. Therefore, the present application is not limited to any particular combination of hardware and software.

[0069] The above further describes the present application in detail in combination with specific embodiments, and cannot be deemed as limitation of the specific embodiments of the present application. For those skilled in the art of the present application, some simple deductions or replacements can be made without departing from the concept of the present application, and all of them shall be deemed as falling within the protection scope of the present application.

Claims

1. A radar testing system based on multi-mode dynamic reconfiguration, characterized in that, include: The multi-mode RF link dynamic reconfiguration module integrates three working modes: target simulation, power measurement, and T / R component testing. It also reconfigures the RF signal link under the control of control commands through a programmable RF switch matrix. The baseband signal processing module adopts a multi-core heterogeneous architecture to perform spectrum analysis of radar echo signals, calculate target motion parameters, and generate control commands. The spatial positioning module adopts a closed-loop control architecture with multi-sensor fusion to control the alignment error of the antenna phase center relative to the center line of the radar antenna element within a preset error range in the T / R component test mode. The intelligent diagnosis and visualization module has a pre-set amplitude and phase detection criterion library. Based on a deep learning model, it performs correlation analysis on multi-channel amplitude and phase data of the T / R component, automatically identifies abnormal channels, and generates a visual diagnostic report. The closed-loop cross-linking control module uses a gigabit Ethernet to build a distributed control network. It is used to perform silent switching, calibration parameter loading, link self-test and system clock synchronization during mode switching, so as to achieve seamless switching of the three working modes within the radio frequency signal gap.

2. The radar testing system based on multi-mode dynamic reconfiguration as described in claim 1, characterized in that, The programmable RF switch matrix in the multi-mode RF link dynamic reconfiguration module includes multiple single-pole multi-throw switches. Each single-pole multi-throw switch switches are switched according to control commands to achieve rapid reconfiguration of the signal link under different operating modes.

3. The radar testing system based on multi-mode dynamic reconfiguration as described in claim 2, characterized in that, The multi-mode RF link dynamic reconfiguration module forms a bidirectional test link in T / R test mode. The bidirectional test link includes a transmit branch and a receive branch. The transmit branch includes a phase-adjustable phase shifter and a power amplifier, and the receive branch includes a programmable attenuator and an I / Q demodulator, and compensates for link insertion loss in real time.

4. The radar testing system based on multi-mode dynamic reconfiguration as described in claim 1, characterized in that, The closed-loop control architecture for multi-sensor fusion includes: a dual-frequency laser interferometer for constructing a three-dimensional reference coordinate system, at least three position-sensitive detectors, a piezoelectric ceramic-driven translation stage, and a fuzzy PID controller; wherein, the laser interferometer uses red and green light dual-wavelength interference fringe counting for absolute distance measurement.

5. The radar testing system based on multi-mode dynamic reconfiguration as described in claim 1, characterized in that, The method of performing correlation analysis on multi-channel amplitude and phase data of the T / R component based on a deep learning model, automatically identifying abnormal channels and generating a visual diagnostic report includes: Based on a deep learning model, multi-scale feature extraction and channel correlation analysis are performed on the amplitude and phase response data of the T / R component to automatically generate a three-dimensional visualization diagnostic report containing fault type, level and repair suggestions.

6. The radar testing system based on multi-mode dynamic reconfiguration as described in claim 5, characterized in that, The deep learning model includes: an Inception-v4 network structure, which contains a radar-specific frequency domain pooling branch for extracting key frequency band responses from a three-dimensional feature tensor composed of amplitude, phase, frequency, and timestamp; the channel correlation analysis uses a graph attention network, where nodes are channels of the T / R component, and edge weights are dynamically calculated based on the amplitude and phase correlation between channels.

7. The radar testing system based on multi-mode dynamic reconfiguration as described in claim 1, characterized in that, During mode switching, the process includes silent switching, calibration parameter loading, link self-testing, and system clock synchronization. This includes: completing RF switch matrix reconstruction during RF signal idle intervals and loading pre-stored gain and phase compensation coefficients; performing link self-testing through a built-in calibration signal source; and using the IEEE 1588 precise time protocol to ensure that the clock synchronization error of each module is less than the preset clock synchronization error.

8. A radar testing method based on multi-mode dynamic reconstruction, characterized in that, include: The multi-mode RF link dynamic reconfiguration module integrates three working modes: target simulation, power measurement, and T / R component testing. It also achieves RF signal link reconfiguration under control commands through a programmable RF switch matrix. The baseband signal processing module employs a multi-core heterogeneous architecture to perform spectrum analysis and target motion parameter calculation on radar echo signals, and generate control commands. By employing a closed-loop control architecture that integrates multiple sensors through the spatial positioning module, the alignment error of the antenna phase center relative to the center line of the radar antenna element is controlled within a preset error range under the T / R component test mode. Through the intelligent diagnosis and visualization module, a pre-set amplitude and phase detection criterion library is used to perform correlation analysis on multi-channel amplitude and phase data of T / R components based on a deep learning model, automatically identify abnormal channels and generate a visual diagnostic report; A distributed control network is constructed using Gigabit Ethernet through a closed-loop cross-link control module. During mode switching, silent switching, calibration parameter loading, link self-test and system clock synchronization are performed to achieve seamless switching of the three working modes within the radio frequency signal gap.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in claim 8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in claim 8.

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