Modular radar orientation monitoring method and system for industrial test

Abstract

The invention discloses a modular radar orientation monitoring method and system for industrial testing, and belongs to the field of radar monitoring. According to the method, the platform motion attitude, the radar azimuth signal and the radar local attitude change data are acquired, and the platform motion attitude data are subjected to coordinate system conversion to generate the target platform motion attitude data. And calculating dynamic characteristics according to the platform data and the radar local attitude change data to obtain azimuth signal adjustment parameters for adaptively adjusting original radar azimuth signals to generate target radar azimuth signal data. And fusing the target radar azimuth signal, the target platform motion attitude and the radar local attitude change data through a state estimation algorithm to generate fused radar azimuth data. By calculating and fusing radar azimuth data and target radar azimuth signals, the fault type is determined according to multi-dimensional residual features between azimuth information calculated by platform motion and azimuth change information calculated by local attitude, and the accuracy and effectiveness of bit signal fault diagnosis can be improved.

Description

Technical Field

[0001] The present application relates to the field of radar monitoring, and in particular to a modular radar azimuth monitoring method and system for industrial testing. Background Art

[0002] Radar direction monitoring is a key technology for ensuring accurate pointing and stable target tracking by radar systems. It has broad application prospects in aerospace, autonomous driving, ocean exploration, and industrial automation. By acquiring and analyzing the direction of the radar beam in real time, it is possible to assess the operating status of the radar system and ensure its detection performance and reliability in various environments.

[0003] Existing radar position monitoring typically relies on sensors such as built-in encoders or resolvers to obtain position readings, combined with feedback from servo control systems for status monitoring. Some systems also incorporate an inertial measurement unit (IMU) to compensate for platform motion, improving position accuracy in dynamic environments.

[0004] However, in scenarios oriented towards industrial test-level testing, when the radar platform is in a complex motion state, such as when a mobile vehicle platform experiences complex dynamics or encounters unknown interference, the vehicle platform motion and local vibration of the radar may cause complex distortion of the radar azimuth signal. The analysis methods and analysis dimensions of the existing technology are not comprehensive enough, and it is difficult to quickly and accurately identify the abnormal sources and types of radar azimuth signals. Therefore, the existing technology has a technical problem of poor accuracy and effectiveness of azimuth signal fault diagnosis when applied to radar azimuth signal monitoring on mobile vehicle platforms in scenarios oriented towards industrial test-level testing. Summary of the Invention

[0005] The present application provides a modular radar azimuth monitoring method, system, device and computer storage medium for industrial testing, which can improve the accuracy and effectiveness of position signal fault diagnosis.

[0006] In a first aspect, the present application provides a modular radar azimuth monitoring method for industrial testing, which is applied to radar azimuth signal monitoring of a mobile vehicle platform. The method comprises: Acquire the platform motion attitude data of the mobile vehicle platform, the radar azimuth signal data and the radar local attitude change data of the radar; Performing coordinate system conversion processing on the platform motion attitude data to generate target platform motion attitude data aligned with the reference coordinate system of the radar; According to the target platform motion attitude data and the radar local attitude change data, the azimuth signal adjustment parameters are obtained by calculating the dynamic change characteristics of the azimuth signal propagation path; Using the azimuth signal adjustment parameters to perform adaptive signal waveform adjustment and timing synchronization processing on the radar azimuth signal data to generate the target radar azimuth signal data; Based on the target radar azimuth signal data, the target platform motion attitude data and the radar local attitude change data, the state estimation algorithm is used to perform data fusion processing to generate fused radar azimuth data; By calculating the multi-dimensional residual features between the fused radar azimuth data and the target radar azimuth signal data, the platform motion azimuth information calculated from the target platform motion attitude data, and the attitude azimuth change information calculated from the radar local attitude change data, the fault type information is determined to complete radar azimuth monitoring in industrial test-level scenarios.

[0007] In one possible implementation, before performing data fusion processing using a state estimation algorithm based on the target radar azimuth signal data, the target platform motion attitude data, and the radar local attitude change data to generate fused radar azimuth data, the method further includes: Based on the fluctuation amplitude of the radar local attitude change data, fusion weight information is assigned to the radar local attitude change data, the target radar azimuth signal data and the target platform motion attitude data; Based on the target radar azimuth signal data, the target platform motion attitude data, and the radar local attitude change data, the state estimation algorithm is used to perform data fusion processing to generate fused radar azimuth data, including: Based on the target radar azimuth signal data, target platform motion attitude data, radar local attitude change data and fusion weight information, the state estimation algorithm is used to perform data fusion processing to generate fused radar azimuth data.

[0008] In one practicable embodiment, based on target radar azimuth signal data, target platform motion attitude data, radar local attitude change data, and fusion weight information, a state estimation algorithm is used to perform data fusion processing to generate fused radar azimuth data, including: Establish a dynamic state description structure with the radar scanning cycle as the step length, which includes the platform position state component, local attitude state component and azimuth measurement state component; Mapping the target platform motion attitude data into the observation value of the platform position state component, mapping the radar local attitude change data into the observation value of the local attitude state component, and mapping the target radar azimuth signal data into the observation value of the azimuth measurement state component; Convert the platform motion weight in the fusion weight information into the observation confidence parameter of the platform position state component, convert the local attitude weight into the observation confidence parameter of the local attitude state component, and convert the orientation signal weight into the observation confidence parameter of the orientation measurement state component; Recursive fusion is performed in the dynamic state description structure. Based on the state prediction value of the previous scanning cycle, the state update calculation is performed in combination with the observation value of the current scanning cycle and the corresponding observation confidence parameter, and the fused radar azimuth data of the current scanning cycle is output.

[0009] In one feasible implementation, the azimuth signal adjustment parameters are obtained by calculating the dynamic change characteristics of the azimuth signal propagation path based on the target platform motion posture data and the radar local posture change data, including: Calculate the projection change rate of the velocity component perpendicular to the radar beam azimuth plane in the target platform motion attitude data on the horizontal plane to obtain the azimuth angle deviation rate; Calculate the cumulative amount of the angular velocity component parallel to the normal of the radar beam azimuth plane in the radar local attitude change data during the scanning period to obtain the azimuth cumulative offset; Based on the time stamp of the radar pulse transmission time point, the dynamic change characteristic value of the azimuth signal propagation path is obtained by linearly superimposing the azimuth angle deviation rate and the azimuth cumulative deviation; The dynamically changing characteristic values ​​are input into the constructed mapping relationship table to obtain the azimuth signal adjustment parameters used to correct the radar transmission beam pointing and receiving timing.

[0010] In one feasible implementation, adaptive signal waveform adjustment and timing synchronization processing are performed on radar azimuth signal data using azimuth signal adjustment parameters to generate target radar azimuth signal data, including: Based on the beam pointing correction angle in the azimuth signal adjustment parameter, the original beam direction in the radar azimuth signal data is modified to generate a corrected radar transmission beam direction; Based on the pulse timing compensation amount in the azimuth signal adjustment parameter, the original signal sampling time point in the radar azimuth signal data is moved according to the time reference of the radar scanning cycle to generate a time-compensated radar azimuth signal; The corrected radar transmit beam direction is combined with the time-compensated radar azimuth signal to reconstruct the phase and time tag sequence of the radar echo signal; Based on the reconstructed phase and time label sequence, the original sampling point order of the radar azimuth signal data is rearranged to generate the target radar azimuth signal data.

[0011] In one feasible embodiment, the fault type information is determined by respectively calculating the multi-dimensional residual features between the fused radar azimuth data and the target radar azimuth signal data, the platform motion azimuth information calculated by the target platform motion attitude data, and the attitude azimuth change information calculated by the radar local attitude change data, including: Extract the tangent direction angle value of the carrier motion trajectory from the target platform motion posture data as the platform motion orientation information; Extract the beam pointing angle change in adjacent scanning cycles from the radar local attitude change data as attitude azimuth change information; The arithmetic difference between the fused radar azimuth data and the target radar azimuth signal data is calculated to obtain a first residual component, the arithmetic difference between the fused radar azimuth data and the platform motion azimuth information is calculated to obtain a second residual component, and the arithmetic difference between the fused radar azimuth data and the attitude and azimuth change information is calculated to obtain a third residual component, which are combined to form a multi-dimensional residual feature vector; A target residual component is determined in the multi-dimensional residual feature vector, and associated fault type information is determined based on the target residual component.

[0012] In one feasible implementation, determining a target residual component in a multi-dimensional residual feature vector, and determining associated fault type information based on the target residual component include: The residual component whose absolute value of the residual component in the multi-dimensional residual feature vector exceeds the preset threshold number of times or more is determined as the target residual component; The fault type information corresponding to the target residual component is determined through the preset mapping relationship between the residual component and the fault type information.

[0013] In a second aspect, the present application provides a modular radar position monitoring system for industrial testing, which is applied to radar position signal monitoring of a mobile vehicle platform. The system includes: A data acquisition module is used to acquire the platform motion posture data of the mobile vehicle platform, the radar azimuth signal data of the radar, and the radar local posture change data; A data conversion module is used to perform coordinate system conversion processing on the platform motion attitude data to generate target platform motion attitude data aligned with the reference coordinate system of the radar; The data processing module is used to obtain the azimuth signal adjustment parameters by calculating the dynamic change characteristics of the azimuth signal propagation path based on the target platform motion posture data and the radar local posture change data; The data processing module is further used to perform adaptive signal waveform adjustment and timing synchronization processing on the radar azimuth signal data using the azimuth signal adjustment parameters to generate target radar azimuth signal data; The data processing module is further used to perform data fusion processing using a state estimation algorithm based on the target radar azimuth signal data, the target platform motion attitude data, and the radar local attitude change data to generate fused radar azimuth data; The analysis and diagnosis module is used to determine the fault type information by calculating the multi-dimensional residual characteristics between the fused radar azimuth data and the target radar azimuth signal data, the platform motion azimuth information calculated by the target platform motion attitude data, and the attitude azimuth change information calculated by the radar local attitude change data, so as to complete the radar azimuth monitoring in the industrial test-level scenario.

[0014] In a third aspect, the present application provides an electronic device, comprising: a processor, and a memory storing computer program instructions; the processor reads and executes the computer program instructions to implement a modular radar azimuth monitoring method for industrial testing as in any embodiment of the first aspect.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, a modular radar azimuth monitoring method for industrial testing as in any one of the embodiments of the first aspect is implemented.

[0016] The present application implements a modular radar azimuth monitoring method, system, equipment and computer storage medium for industrial testing. First, based on the dynamic change characteristics of the azimuth signal propagation path calculated under the combined action of platform motion and local attitude, the radar original azimuth signal is adaptively adjusted in advance to actively respond to and compensate for the signal distortion caused by the complex dynamic motion of the vehicle and the radar's own vibration; then, the adjusted radar azimuth signal is fused with the platform motion attitude and radar local attitude change data to generate a more stable and accurate fused radar azimuth; finally, the fault type is determined by comparing and analyzing the multi-dimensional residual characteristics between this fused radar azimuth and the azimuth information indicated by each independent information source. Through a step-by-step progressive processing method from signal preprocessing, multi-source fusion to multi-dimensional residual analysis, not only the refined compensation capability for the inherent changes of the radar azimuth signal in a complex dynamic environment is enhanced, but also a more comprehensive fault judgment basis is constructed, making it possible to more effectively examine and locate the anomalies of the azimuth signal from multiple angles. Therefore, this application effectively solves the technical problem of poor accuracy and effectiveness of radar azimuth signal fault diagnosis in the existing technology when applied to mobile vehicle platforms in industrial test-level testing scenarios, and significantly improves the reliability of radar azimuth monitoring and the accuracy of fault identification and positioning.

[0017] Furthermore, by introducing a dynamic fusion weight allocation mechanism based on the fluctuation amplitude of the radar's local attitude change data, when performing multi-source data fusion processing to generate fused radar azimuth data, the confidence or contribution of each data source in the fusion process can be adaptively adjusted according to the real-time vibration or jitter of the radar module itself. This dynamic weight adjustment based on actual working conditions enables the state estimation algorithm to more intelligently cope with the complexity of the radar's local attitude changes on mobile vehicle platforms, optimizes the utilization efficiency of information from each data source under different vibration conditions, and thus can further improve the accuracy and stability of the fused radar azimuth data in complex dynamic environments. This further enhances the robustness of radar azimuth signal monitoring and the reliability of fault diagnosis in the face of radar local vibration interference when applied to mobile vehicle platforms in industrial test-level scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] Figure 1 This is a flow chart of a modular radar position monitoring method for industrial testing provided by one embodiment of the present application; Figure 2 This is a flow chart of a method for generating fused radar azimuth data using a state estimation algorithm provided by an embodiment of the present application; Figure 3 This is a flowchart of a method for determining fault type information provided by an embodiment of the present application; Figure 4 This is a schematic diagram of the structure of a modular radar position monitoring system for industrial testing provided by one embodiment of the present application; Figure 5 This is a schematic diagram of the hardware structure of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0020] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is merely to provide a better understanding of the present application by illustrating the examples of the present application.

[0021] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, the elements defined by the phrase "comprising..." do not exclude the presence of other identical elements in the process, method, article, or device comprising the elements.

[0022] To solve the problems of the prior art, the present invention provides a modular radar position monitoring method, system, device, and computer storage medium for industrial testing. The following first introduces the modular radar position monitoring method for industrial testing provided by the present invention.

[0023] Figure 1 The flowchart of the modular radar position monitoring method for industrial testing provided by an embodiment of the present application is shown. The radar position signal monitoring for a mobile vehicle platform is shown in FIG. Figure 1 As shown, the method includes steps S110 to S160.

[0024] S110: Acquire platform motion attitude data of the mobile vehicle platform, radar azimuth signal data of the radar, and radar local attitude change data.

[0025] A mobile platform is a mobile carrier device equipped with a radar, such as a test vehicle or drone, used to simulate dynamic motion in industrial testing scenarios. Platform motion attitude data refers to information describing the overall motion state of the platform, including position coordinates, velocity vectors, acceleration vectors, and attitude angles such as roll, pitch, and yaw. Radar azimuth signal data refers to the azimuth-related information output by the radar system, including beam pointing angles, scan angle sequences, and time tags. Radar local attitude change data refers to information on the local attitude changes of the radar device relative to the platform base, including angular changes caused by vibration, angular velocity components, and time series data.

[0026] First, the vehicle platform's integrated inertial measurement unit and global positioning system directly measure position, velocity, acceleration, and attitude angle, outputting digital platform motion attitude data. Parameters such as the velocity vector are calculated in real time using the sensor's built-in algorithm. Second, the radar system's built-in encoder or resolver reads the antenna azimuth angle and scanning time series to generate radar azimuth signal data. This data is referenced to the pulse emission time point and is directly output without additional processing. Finally, a high-precision gyroscope or accelerometer attached to the radar antenna base monitors local vibration and angular changes, outputting radar local attitude change data. Parameters such as angular velocity components are sampled by the sensor and converted into digital signals. All data is synchronously collected by the data acquisition module, timestamped, and integrated into a unified dataset for direct use in subsequent processing.

[0027] For example, in an application for industrial test-level scenarios, for example, a test vehicle equipped with a radar system to be tested is driving on a road simulating complex working conditions. The vehicle may experience rough roads or emergency maneuvers. First, the vehicle's integrated high-precision combined navigation system collects the platform motion posture data of the vehicle platform in real time. The system includes an inertial measurement unit and a global satellite positioning system. The collected data include the vehicle's position coordinate change sequence in three-dimensional space, three-axis velocity vector, three-axis acceleration vector, and key attitude angle information such as roll angle, pitch angle, and yaw angle representing the direction of travel; at the same time, the radar system outputs radar azimuth signal data through its built-in azimuth encoder or rotary transformer. The data is marked with a precise timestamp to indicate the antenna beam pointing angle, scanning angle sequence, and complete echo signal sampling sequence corresponding to each radar pulse emission moment; in addition, in order to capture the radar equipment's own To prevent slight vibrations or deformations of the vehicle body relative to the vehicle platform base, a highly sensitive miniature inertial sensor, such as a three-axis gyroscope or a three-axis accelerometer, is additionally deployed at the radar antenna mounting base to continuously measure and output radar local attitude change data. This data includes the instantaneous angular velocity component, small angular offset, and its corresponding time series in the radar body coordinate system. All three types of data, namely platform motion attitude data, radar azimuth signal data, and radar local attitude change data, are synchronously collected by the on-board high-speed data acquisition unit. A unified high-precision clock source is used to mark all data streams with strictly synchronized microsecond-level time tags. The data are then transmitted to the central processing unit in real time via the on-board network such as Ethernet or a high-speed bus, ultimately forming a time-aligned and well-formatted raw input data set, providing complete and synchronized basic information for subsequent coordinate system conversion processing, signal dynamic compensation, and multi-source data fusion processing.

[0028] S120: performing coordinate system conversion processing on the platform motion attitude data to generate target platform motion attitude data aligned with the reference coordinate system of the radar.

[0029] The target platform's motion data is the platform's motion information, aligned with the radar's reference coordinate system after coordinate conversion. This data includes the converted position coordinates, velocity vector, acceleration vector, and attitude angle parameters. Its coordinate axis definitions are identical to the radar's azimuth measurement reference, ensuring that subsequent calculations can be directly correlated with the radar beam pointing.

[0030] First, the radar reference coordinate system is determined by defining a coordinate system based on the radar mounting structure. Typically, the antenna rotation axis is used as the reference axis, such as the forward direction (X-axis), which represents the main beam direction; the vertical downward direction (Z-axis); and the horizontal horizontal direction (Y-axis). Next, based on the relative installation parameters between the vehicle platform and the radar, such as the radar base yaw and pitch angle installation offsets, the rotation matrix from the platform coordinate system to the radar coordinate system is calculated to establish a coordinate system transformation relationship. This matrix is ​​constructed using direction cosines or quaternion methods, with parameters derived from pre-set installation calibration data. Finally, the position, velocity, and acceleration vectors in the original platform motion attitude data are multiplied by the rotation matrix, and the attitude angle data is recalculated using the Euler angle conversion formula to complete the data conversion. The target platform motion attitude data is ultimately output, with all motion parameters expressed relative to the radar reference coordinate system.

[0031] For example, in an industrial test scenario involving a test vehicle, the platform motion attitude data output by the vehicle's high-precision integrated navigation system is based on the East-North-Up (ENU) geographic coordinate system. In this case, the radar reference coordinate system is defined as follows: the X-axis is parallel to the vehicle's forward direction (i.e., the primary scanning plane of the radar beam), the Y-axis points to the right of the vehicle, and the Z-axis points vertically downward. Using preset radar installation parameters, such as a 2-degree pitch offset on the radar base, the rotation matrix from the ENU coordinate system to the radar coordinate system is calculated. The vehicle's real-time three-axis velocity vector is multiplied by this matrix. For example, the velocity component in the northeast direction can be multiplied by this matrix to obtain the velocity component in the radar coordinate system. Simultaneously, the attitude angle data is converted according to coordinate transformation rules, for example, converting the geographic yaw angle to the heading angle relative to the radar's X-axis. In the converted target platform motion attitude data, the X-component of the velocity vector directly represents the velocity in the direction of the radar beam, spatially aligned with the radar azimuth signal data, providing a unified benchmark for subsequent calculations of the dynamic characteristics of the azimuth signal propagation path.

[0032] S130: Obtaining azimuth signal adjustment parameters by calculating dynamic change characteristics of the azimuth signal propagation path based on the target platform motion posture data and the radar local posture change data.

[0033] The dynamic characteristics of the azimuth signal propagation path refer to the real-time quantitative changes in the radar beam propagation path caused by the combined effects of the vehicle platform's motion and local vibration. These include the rate of change of the beam pointing offset angle and the change in timing delay. The azimuth signal adjustment parameters are the compensation parameters used to correct the radar's transmit beam pointing and receive timing, including the beam pointing correction angle and pulse timing compensation.

[0034] First, the velocity component perpendicular to the radar beam azimuth plane, such as the Y-axis velocity, is extracted from the target platform's motion posture data. The rate of change of its projection on the horizontal plane is calculated to obtain the azimuth angle deviation rate; this value reflects the trend of beam pointing deviation caused by the lateral motion of the vehicle platform.

[0035] Next, the angular velocity component parallel to the normal to the radar beam's azimuth plane, such as the angular velocity about the Z-axis, is extracted from the radar's local attitude change data. This is integrated within a single radar scan cycle to obtain the cumulative azimuth offset, which quantifies the cumulative beam pointing error caused by vibration. Next, based on the time stamp of the radar pulse transmission point, the azimuth angle offset rate is multiplied by the time interval to obtain the instantaneous displacement. This is then linearly superimposed with the cumulative azimuth offset to generate a dynamic characteristic value of the azimuth signal propagation path, which comprehensively represents the real-time distortion of the beam path. Finally, the dynamic characteristic value is input into a preset two-dimensional mapping table with beam distortion on the horizontal axis and timing delay on the vertical axis. A table lookup is used to output the azimuth signal adjustment parameters. This table is pre-established through calibration experiments to ensure that the compensation parameters match the actual distortion.

[0036] S140: Using the azimuth signal adjustment parameters, adaptively adjust the signal waveform and perform timing synchronization processing on the radar azimuth signal data to generate target radar azimuth signal data.

[0037] Signal waveform adjustment refers to the process of modifying the spatial pointing characteristics of the radar beam direction based on the azimuth signal adjustment parameters, including redirection of the transmit beam. Timing synchronization processing refers to the process of adjusting the radar signal sampling time base based on the timing compensation to eliminate timing delays caused by propagation path changes. Target radar azimuth signal data refers to the reconstructed radar azimuth information after dual waveform and timing corrections, including the corrected beam pointing angle, synchronized signal sampling sequence, and corresponding time tag sequence.

[0038] First, the original beam direction in the radar azimuth signal data is geometrically corrected based on the beam pointing correction angle in the azimuth signal adjustment parameters. The beam pointing angle is modified using a direction rotation matrix or an angle superposition algorithm to generate the corrected radar transmit beam direction. Simultaneously, based on the pulse timing compensation in the azimuth signal adjustment parameters, the original signal sampling time points are shifted with the radar scan cycle as the origin. Linear interpolation is used to recalculate the compensated sampling point positions to generate a time-compensated radar azimuth signal. The corrected beam direction parameters are then combined with the time-compensated signal sequence, and the phase consistency of the radar echo signal is reconstructed using a phase compensation algorithm. The signal time tag sequence is also updated to reflect the timing adjustment. Finally, based on the reconstructed phase and time tag sequences, the sampling points of the original radar azimuth signal data are reordered and interpolated to generate the target radar azimuth signal data.

[0039] S150: Based on the target radar azimuth signal data, the target platform motion attitude data, and the radar local attitude change data, a state estimation algorithm is used to perform data fusion processing to generate fused radar azimuth data.

[0040] A state estimation algorithm is a mathematical method that estimates the unknown state of a dynamic system by fusing noisy measurement data from multiple sources. In this application, it specifically refers to an algorithm that integrates azimuth-related information from three different physical sources to obtain an optimal estimate of the radar azimuth angle that is more accurate and reliable than any single source. This algorithm can be an extended Kalman filter or an unscented Kalman filter. The fused radar azimuth data is the final output of the state estimation algorithm. This data is a high-precision estimate of the true azimuth angle of the radar beam at a specific moment. It integrates the effects of platform motion, radar measurements, and local vibrations, eliminating random errors and some systematic biases from each data source.

[0041] First, a mathematical model is constructed to describe the dynamic behavior of radar azimuth. This model uses the radar's true azimuth angle as the core state variable to be estimated and defines how this state variable evolves over time. Subsequently, three input data sources—the target radar azimuth signal data, the target platform's motion attitude data, and the radar's local attitude change data—are used as observations or measurements of the system's state in different dimensions. A recursive prediction and update cycle can be implemented using a state estimation algorithm, such as a Kalman filter. In the prediction phase, the algorithm predicts the current azimuth angle based on the previous azimuth estimate and the dynamic model. In the update phase, the algorithm compares this prediction with the actual observations obtained from the three data sources at the current moment. Based on the confidence level or noise level of each data source, the algorithm calculates an optimal correction to adjust the prediction, thereby generating the final state estimate for the current moment. This process is repeated throughout each radar scan cycle or data sampling moment, ultimately outputting continuous and highly accurate fused radar azimuth data.

[0042] S160: Determine the fault type information by respectively calculating the multi-dimensional residual features between the fused radar azimuth data and the target radar azimuth signal data, the platform motion azimuth information calculated by the target platform motion attitude data, and the attitude azimuth change information calculated by the radar local attitude change data, so as to complete the radar azimuth monitoring in the industrial test-level scenario.

[0043] The multidimensional residual feature is a composite indicator used to quantify the internal consistency of the radar azimuth system. It is composed of multiple independent residual components. Specifically, the feature vector contains the arithmetic difference between the fused radar azimuth data and the target radar azimuth signal data, the platform motion azimuth information, and the attitude and azimuth change information. Fault type information refers to the specific fault diagnosis conclusion derived from the multidimensional residual feature analysis. This information clearly indicates the source or type of radar azimuth signal anomaly, such as a fault in the radar's own encoder, anomalies in the vehicle platform's navigation system data, or uncompensated severe vibrations in the radar mounting structure.

[0044] First, the system calculates the platform's motion orientation, representing the vehicle's direction of travel, from the target platform's motion attitude data. It also deciphers attitude and orientation changes caused by factors such as vibration from the radar's local attitude change data. Next, using the high-precision fused radar orientation data as a benchmark, the system calculates three sets of differences in parallel through arithmetic subtraction: the difference between the fused data and the target radar orientation signal data forms the first residual component, the difference between the fused data and the platform's motion orientation information forms the second residual component, and the difference between the fused data and the attitude and orientation change information forms the third residual component. These three residual components are combined in real time into a multidimensional residual feature vector. Finally, the system analyzes this feature vector to determine the fault type. A decision logic monitors whether each residual component consistently exceeds a preset normal fluctuation threshold. If one or more residual components exhibit significant and persistent anomalies, the system matches this specific anomaly pattern to the corresponding fault type based on a pre-defined fault knowledge base or rule set, thereby completing the diagnosis.

[0045] This embodiment performs pre-adaptive adjustments to the radar's original azimuth signal based on the dynamic change characteristics of the azimuth signal propagation path calculated under the combined effects of platform motion and local attitude, in order to actively respond to and compensate for signal distortion caused by the vehicle's complex dynamic motion and the radar's own vibration. Subsequently, the adjusted radar azimuth signal is fused with the platform's motion attitude and radar's local attitude change data to generate a more stable and accurate fused radar azimuth. Finally, the fault type is determined by comparing and analyzing the multi-dimensional residual characteristics between this fused radar azimuth and the azimuth information indicated by each independent information source. Through a step-by-step progressive processing approach from signal preprocessing, multi-source fusion, to multi-dimensional residual analysis, not only is the ability to fine-tune compensation for the inherent changes in the radar azimuth signal in complex dynamic environments enhanced, but a more comprehensive basis for fault judgment is also established, enabling more effective examination and location of azimuth signal anomalies from multiple angles. Therefore, this application effectively solves the technical problem of poor accuracy and effectiveness of radar azimuth signal fault diagnosis in the existing technology when applied to mobile vehicle platforms in industrial test-level testing scenarios, and significantly improves the reliability of radar azimuth monitoring and the accuracy of fault identification and positioning.

[0046] In one feasible embodiment, before step S150: performing data fusion processing using a state estimation algorithm based on the target radar azimuth signal data, the target platform motion attitude data, and the radar local attitude change data to generate fused radar azimuth data, the method further includes: Based on the fluctuation amplitude of the radar local attitude change data, fusion weight information is assigned to the radar local attitude change data, the target radar azimuth signal data and the target platform motion attitude data.

[0047] Fusion weight information refers to a set of numerical parameters used to characterize the relative reliability or confidence of three types of data sources during the data fusion process: radar local attitude change data, target radar azimuth signal data, and target platform motion attitude data. Its allocation is based on the real-time fluctuation amplitude of the radar local attitude change data.

[0048] First, the fluctuation amplitude of the radar's local attitude change data within a specific time window is calculated in real time. This fluctuation amplitude can be quantified using statistical indicators such as the variance, standard deviation, or peak-to-peak value of the angular velocity or angle change. Second, based on a preset mapping relationship or dynamic adjustment strategy, the calculated fluctuation amplitude is converted into a fusion weight corresponding to the radar's local attitude change data. A larger fluctuation amplitude indicates greater interference or uncertainty in that data source, and its corresponding fusion weight should be appropriately reduced; conversely, a smaller fluctuation amplitude should be assigned a higher weight. Next, corresponding fusion weights are assigned to the target radar's azimuth signal data and the target platform's motion attitude data. These weights can be set based on prior knowledge or adjusted in conjunction with the weight of the radar's local attitude change data. For example, when the weight of the local attitude data is reduced, the weights of the other two data sources are correspondingly increased to ensure the validity of the overall information input. All weights are typically normalized to ensure that their sum is a specific value, such as 1. Finally, the output contains fusion weight information for each of the three data sources.

[0049] Step S150: Based on the target radar azimuth signal data, the target platform motion attitude data, and the radar local attitude change data, a state estimation algorithm is used to perform data fusion processing to generate fused radar azimuth data, including: Based on the target radar azimuth signal data, target platform motion attitude data, radar local attitude change data and fusion weight information, the state estimation algorithm is used to perform data fusion processing to generate fused radar azimuth data.

[0050] First, fusion weight information is input into the selected state estimation algorithm, such as the extended Kalman filter or the unscented Kalman filter. The weight information is used to adjust the measurement noise covariance matrix within the state estimation algorithm. Specifically, higher weights correspond to smaller measurement noise variances, indicating that the algorithm trusts the measurements from that data source more; lower weights correspond to larger measurement noise variances. The state estimation algorithm then executes according to its standard workflow. In the state update phase following the prediction step, the predicted state is corrected using the weighted measurement noise covariance matrix, combined with the target radar azimuth signal data, the target platform motion attitude data, and the radar local attitude change data as system observations. The algorithm then determines the contribution of each observation to the state update based on its weight. Ultimately, this weighted data fusion process outputs the current fused radar azimuth data, which is the optimal estimate after integrating the reliability assessments of each data source.

[0051] This embodiment introduces a dynamic fusion weight allocation mechanism based on the fluctuation amplitude of the radar local attitude change data, so that when performing multi-source data fusion processing to generate fused radar azimuth data, the confidence or contribution of each data source in the fusion process can be adaptively adjusted according to the real-time vibration or jitter of the radar module itself. This dynamic weight adjustment based on actual working conditions enables the state estimation algorithm to more intelligently cope with the complexity of the local attitude changes of the radar on the mobile vehicle platform, optimizes the utilization efficiency of the information of each data source under different vibration conditions, and thus can further improve the accuracy and stability of the fused radar azimuth data in complex dynamic environments. It further enhances the robustness of radar azimuth signal monitoring and the reliability of fault diagnosis in the face of radar local vibration interference when applied to mobile vehicle platforms in industrial test-level scenarios.

[0052] Figure 2 FIG. 1 shows a flow chart of a method for generating fused radar azimuth data using a state estimation algorithm according to an embodiment of the present application. Figure 2 As shown, the method includes steps S210 to S240.

[0053] In one practicable embodiment, based on target radar azimuth signal data, target platform motion attitude data, radar local attitude change data, and fusion weight information, a state estimation algorithm is used to perform data fusion processing to generate fused radar azimuth data, including: S210: Establish a dynamic state description structure with a radar scanning period as a step length, where the dynamic state description structure includes a platform position state component, a local attitude state component, and an orientation measurement state component.

[0054] The dynamic state description structure is a mathematical model that defines the rules for how states evolve over time. It can be used in state-space representation for estimation algorithms such as Kalman filtering. This structure uses the radar scan period as a discrete time step, and its state vector contains three key components: the platform position state component, which describes the kinematic parameters of the mobile platform, such as position and velocity, in the radar reference coordinate system; the local attitude state component, which describes subtle changes in the radar's attitude relative to the platform, such as angular displacement and angular velocity caused by vibration; and the azimuth measurement state component, which describes the azimuth angle measured by the radar's own sensors and its rate of change.

[0055] First, the specific composition of the state vector is determined. Specifically, the specific physical quantities contained in the platform position state component, the local attitude state component, and the azimuth measurement state component are clarified. For example, the platform position state component may include three-dimensional position and three-dimensional velocity, the local attitude state component may include three-axis angles and three-axis angular velocities, and the azimuth measurement state component may include azimuth angle and azimuth angular velocity. Second, a state transition model is established. This model describes how the current state evolves from the previous state within a radar scan cycle. This model is typically based on physical kinematic equations or a simplified dynamic model and takes into account the effects of process noise. For example, the platform position can be updated based on its velocity, and the radar azimuth angle can be updated based on its angular velocity. Finally, a complete dynamic state description structure is formed. This structure forms the basis for the subsequent recursive fusion algorithm and defines the state prediction method.

[0056] S220: Mapping the target platform motion posture data into the observation value of the platform position state component, mapping the radar local posture change data into the observation value of the local posture state component, and mapping the target radar azimuth signal data into the observation value of the azimuth measurement state component.

[0057] Observations are measurements directly acquired from individual sensors or data sources that correspond to state components in the dynamic state description structure. The mapping process converts and associates raw data from various sources and formats with corresponding components in the state vector. This data serves as the actual measurement input used by the state estimation algorithm to update the state estimate.

[0058] First, from the target platform's motion attitude data, parameters related to the platform's position state component are extracted. For example, the platform's three-dimensional position and velocity data, after coordinate transformation, can be directly used as observations of the platform's position state component. Second, from the radar's local attitude change data, parameters describing the radar's attitude change relative to the platform base are extracted. For example, the angular velocity measured by the gyroscope or accelerometer and the calculated small angular offset can be used as observations of the local attitude state component. Third, from the target radar's azimuth signal data, azimuth information output by the radar's own encoder and other devices is extracted. For example, the radar beam pointing angle, after waveform adjustment and timing synchronization, can be used as the observation of the azimuth measurement state component. This process requires ensuring that the timestamps of all observations are aligned with the radar scan period and that their physical units and coordinate system definitions are consistent with the state components in the dynamic state description structure.

[0059] S230: Convert the platform motion weight in the fusion weight information into the observation confidence parameter of the platform position state component, convert the local attitude weight into the observation confidence parameter of the local attitude state component, and convert the orientation signal weight into the observation confidence parameter of the orientation measurement state component.

[0060] Observation confidence parameters are used to quantify the reliability of individual observations in state estimation algorithms. Within the Kalman filter framework, these observation confidence parameters are typically expressed as the inverse of the measurement noise covariance matrix or its diagonal elements. The platform motion weight, local attitude weight, and orientation signal weight are specific weights for the three data sources in the fusion weight information.

[0061] First, the weights of the target platform's motion attitude data, radar local attitude change data, and target radar azimuth signal data are extracted from the fusion weight information. The platform motion weights are then mapped directly or through a conversion function to confidence parameters for the platform's position state components. These parameters are used to construct the portion of the measurement noise covariance matrix related to the platform's position observations. The conversion function can be the inverse of the value multiplied by a scaling factor. Similarly, the local attitude weights are converted to confidence parameters for the local attitude state components, and the azimuth signal weights are converted to confidence parameters for the azimuth measurement state components. This conversion process ensures that observations with higher weights have smaller corresponding measurement noise variances, thus being assigned greater confidence during state updates.

[0062] S240: Execute recursive fusion in the dynamic state description structure, perform state update calculation based on the state prediction value of the previous scanning cycle, combined with the observation value of the current scanning cycle and the corresponding observation confidence parameter, and output the fused radar azimuth data of the current scanning cycle.

[0063] Recursive fusion is the core operation of the state estimation algorithm, which iteratively predicts and updates the state during each radar scan cycle. The state prediction value is a priori estimate of the current cycle's state, calculated based on the estimated state from the previous cycle and the state transition model in the dynamic state description structure. The state update calculation uses the actual observations of the current cycle and the corresponding observation confidence parameters to correct the state prediction value, resulting in a posterior estimate of the current cycle's state, i.e., the fused radar position data.

[0064] First, at the beginning of each radar scan cycle, a state prediction is performed using the state transition model in the established dynamic state description structure and the fused radar bearing data obtained in the previous scan cycle, namely the posterior state estimate. This yields the a priori state estimate and a priori error covariance for the current scan cycle. Secondly, the various observations for the current scan cycle are obtained, along with the converted observation confidence parameters. These parameters are reflected in the measurement noise covariance matrix. The Kalman gain is then calculated. This gain determines the contribution of the observation to the state update and is influenced by the observation confidence parameter. Next, the a priori state estimate is corrected using the Kalman gain and the difference between the observed and predicted observations, also known as the innovation, to obtain the a priori state estimate for the current scan cycle, namely the fused radar bearing data. Simultaneously, the posterior error covariance is updated. This prediction and update cycle continues throughout each radar scan cycle, continuously outputting high-precision fused radar bearing data.

[0065] For example, the system first establishes a dynamic state description structure that includes the vehicle's three-dimensional position and velocity in the radar coordinate system (the platform position state component); the three-axis angular attitude and three-axis angular velocity of the radar antenna relative to the vehicle base (the local attitude state component); and the azimuth angle and azimuth angular velocity output by the radar encoder (the azimuth measurement state component). The system also defines how these state quantities evolve periodically based on Newtonian kinematics and uniform angular velocity models. Then, during each radar scan cycle, the position and velocity of the target platform's motion attitude data are extracted as observations of the platform position state component; the radar's local angular attitude and angular velocity are extracted from the radar's local attitude change data as observations of the local attitude state component; and the azimuth angle is extracted from the target radar's azimuth signal data as the observation of the azimuth measurement state component.

[0066] Next, processing is performed based on the fusion weight information calculated based on the fluctuation amplitude of the radar local attitude data. For example, when the vehicle is jolted, causing large fluctuations in the local attitude data, the corresponding local attitude weight is low. This low weight translates into a larger measurement noise variance, meaning that the observation confidence assigned to the local attitude state component observations is low. Finally, an extended Kalman filter begins operation: it first predicts the vehicle position, radar local attitude, and radar azimuth for the current scan cycle based on the fused azimuth results from the previous scan cycle. It then updates the data by combining the observations from the three data sources and their corresponding observation confidence parameters for the current scan cycle. At this point, the low observation confidence of the local attitude weakens its role in the update process. The filter then calculates the optimal Kalman gain and uses this gain to correct the prediction, resulting in the most accurate fused radar attitude data for the current scan cycle. This process is repeated repeatedly, ensuring the continuous output of reliable radar attitude information even in complex and dynamic environments.

[0067] In one feasible embodiment, step S130: obtaining the azimuth signal adjustment parameter by calculating the dynamic change characteristics of the azimuth signal propagation path based on the target platform motion posture data and the radar local posture change data, includes: The projection change rate of the velocity component perpendicular to the radar beam azimuth plane in the target platform motion attitude data on the horizontal plane is calculated to obtain the azimuth angle deviation rate.

[0068] The radar beam azimuth plane refers to the plane where the radar beam's primary energy is concentrated during scanning. It is typically a horizontal plane or a plane at a certain angle to the horizontal plane. The velocity component in the direction perpendicular to the radar beam azimuth plane refers to the speed of the mobile vehicle platform in that vertical direction. The horizontal projection change rate refers to how quickly this vertical velocity component changes over time after being projected onto the horizontal plane. This rate of change is directly related to the azimuth angle deviation rate of the radar beam caused by the lateral movement of the platform. The azimuth angle deviation rate refers to the rate at which the pointing angle of the radar beam in the azimuth plane changes due to the lateral movement of the platform. The unit is usually degrees per second or radians per second.

[0069] First, the three-dimensional velocity vector of the vehicle platform in the radar reference coordinate system is extracted from the target platform's motion posture data. Secondly, the radar beam azimuth plane is determined, and the direction vector perpendicular to this plane is found. The platform's three-dimensional velocity vector is then projected onto this perpendicular direction vector to obtain the velocity component perpendicular to the radar beam azimuth plane. This velocity component is then further projected onto the horizontal plane, and its rate of change over time over one or more radar scan cycles is calculated, for example, by differential calculation or slope fitting. This resulting rate of change is the azimuth angle deviation rate, which quantifies the rate at which the platform's lateral motion dynamically affects the radar azimuth angle.

[0070] The cumulative amount of the angular velocity component parallel to the normal of the radar beam azimuth plane in the radar local attitude change data during the scanning period is calculated to obtain the azimuth cumulative offset.

[0071] The radar beam normal is the direction perpendicular to the radar beam's azimuth plane. The angular velocity component parallel to the radar beam normal is the angular velocity of the radar's rotation about this normal due to factors such as local vibration or mounting deformation. The cumulative value within a scan cycle is the angle obtained by integrating this angular velocity component over a complete radar scan cycle. The cumulative azimuth offset is the total offset in the radar beam's pointing angle in the azimuth plane caused by changes in the radar's local attitude during a radar scan cycle.

[0072] First, the three-axis angular velocity data in the radar coordinate system is obtained from the radar's local attitude change data. Secondly, based on the definition of the radar reference coordinate system and the radar beam azimuth plane, the direction vector of the azimuth plane normal is determined. The radar's three-axis angular velocity vector is then projected onto this normal direction to obtain the angular velocity component parallel to the radar beam azimuth plane normal. Next, the time-varying angular velocity component is integrated over the radar scan period, using numerical integration methods such as the trapezoidal rule or Simpson's rule. Alternatively, if the angular velocity is approximately constant within this period, it can be directly multiplied by the scan period duration. The resulting integral is the cumulative azimuth offset, which represents the total azimuth angle deviation caused by local vibrations within a scan period.

[0073] Based on the time stamp of the radar pulse transmission time point, the dynamic change characteristic value of the azimuth signal propagation path is obtained by linearly superimposing the azimuth angle deviation rate and the azimuth cumulative deviation.

[0074] The time stamp of the radar pulse transmission time point refers to the precise record of the transmission moment of each transmitted pulse in the radar system. Linear superposition refers to the direct addition of the azimuth angle offset caused by platform motion and the cumulative azimuth offset caused by local vibration to obtain the total azimuth distortion. The azimuth angle offset can be calculated from the azimuth angle offset rate and time. The dynamic change characteristic value of the azimuth signal propagation path is a comprehensive quantitative indicator. It represents the total angular deviation of the actual pointing direction of the radar beam from the ideal pointing direction due to the combined effects of platform motion and local vibration at the specific radar pulse transmission moment.

[0075] First, the time difference between the current radar pulse transmission time and the start of the scanning cycle is obtained. The azimuth angle deviation rate calculated in the previous step is multiplied by this time difference to obtain the instantaneous azimuth angle deviation caused by the platform's lateral motion at the time of pulse transmission. This instantaneous azimuth angle deviation is then arithmetic added to the cumulative azimuth deviation within the current scanning cycle, calculated in the previous step. The resulting sum is the dynamic characteristic value of the azimuth signal propagation path at the time of the radar pulse transmission.

[0076] The dynamically changing characteristic values ​​are input into the constructed mapping relationship table to obtain the azimuth signal adjustment parameters used to correct the radar transmission beam pointing and receiving timing.

[0077] The mapping relationship table is a lookup table established in advance through simulation. The input of the table is the dynamically changing characteristic value of the azimuth signal propagation path, and the output is the specific azimuth signal adjustment parameters, including the beam pointing correction angle and the pulse timing compensation amount.

[0078] First, the calculated dynamically changing characteristic values ​​of the azimuth signal propagation path are used as input, and a lookup or interpolation operation is performed in the mapping table. The mapping table is established through simulation, with its index being the dynamically changing characteristic values ​​in different intervals, and the corresponding contents being the corresponding beam pointing correction angles and pulse timing compensations. For example, if the characteristic value falls within the range of a certain entry in the table, the corresponding correction parameters are directly used; if the characteristic value falls between two entries, the precise adjustment parameters can be calculated through linear interpolation or other interpolation methods. Ultimately, the beam pointing correction angles and pulse timing compensations output from the mapping table are the azimuth signal adjustment parameters required for the current radar pulse.

[0079] For example, assume that the radar beam scans mainly in the horizontal plane. When the test vehicle drifts rapidly to the right, its target platform motion posture data will show a significant velocity component in the Y-axis direction of the radar, that is, in the direction perpendicular to the beam azimuth plane. The system calculates the rate of change of the projection of this Y-axis velocity on the horizontal plane over time, and obtains a positive azimuth angle offset rate, such as 0.5 degrees to the right per second. At the same time, the gyroscope installed on the base of the radar antenna detects that due to the uneven road surface, the radar has a slight clockwise rotation around its vertical axis, that is, the normal to the azimuth plane, during the current scanning cycle. After integration calculation, the accumulated azimuth offset is 0.1 degrees. For a radar pulse emitted at the midpoint of the scanning cycle, its time mark is half the length of the scanning cycle.

[0080] The instantaneous offset caused by platform motion is calculated as follows: 0.5 degrees per second multiplied by 0.5 scan cycle duration. Assuming a scan cycle of 0.05 seconds, the instantaneous offset is 0.5 * (0.05 / 2) = 0.0125 degrees. This instantaneous offset is then linearly superimposed with the cumulative offset caused by local vibration: 0.0125 degrees + 0.1 degrees = 0.1125 degrees. This 0.1125 degree represents the dynamic variation characteristic value of the azimuth signal propagation path for the pulse. Finally, the system enters this value of 0.1125 degrees into the preset mapping table shown in Table 1. The table then retrieves the corresponding azimuth signal adjustment parameters. The first and second columns of the table are searched for the interval [0.10, 0.15) containing 0.1125. The corresponding beam pointing correction angle is -0.12 degrees, indicating a 0.12 degree leftward correction. The corresponding pulse timing compensation is -6 nanoseconds, indicating a 6 nanosecond timing delay. These adjustment parameters will be used to compensate the transmit and receive processing of the radar pulse.

[0081] Dynamic change characteristic value of azimuth signal propagation path (degrees) interval range Beam pointing correction angle (degrees) Pulse timing compensation (nanoseconds) [-0.50, -0.45) 0.47 20 [-0.45, -0.40) 0.42 18 [-0.40, -0.35) 0.37 16 [-0.35, -0.30) 0.32 14 [-0.30, -0.25) 0.27 12 [-0.25, -0.20) 0.22 10 [-0.20, -0.15) 0.17 8 [-0.15, -0.10) 0.12 6 [-0.10, -0.05) 0.07 4 [-0.05, 0.00) 0.02 2 [0.00, 0.05) -0.02 -2 [0.05, 0.10) -0.07 -4 [0.10, 0.15) -0.12 -6 [0.15, 0.20) -0.17 -8 [0.20, 0.25) -0.22 -10 [0.25, 0.30) -0.27 -12 [0.30, 0.35) -0.32 -14 [0.35, 0.40) -0.37 -16 [0.40, 0.45) -0.42 -18 [0.45, 0.50] -0.47 -20 Table 1 In one feasible embodiment, step S130: using the azimuth signal adjustment parameter to adaptively adjust the signal waveform and perform timing synchronization processing on the radar azimuth signal data to generate the target radar azimuth signal data, includes: Based on the beam pointing correction angle in the azimuth signal adjustment parameter, the original beam direction in the radar azimuth signal data is modified to generate a corrected radar transmission beam direction.

[0082] First, the beam pointing correction angle is extracted from the azimuth signal adjustment parameters. The correction angle is a scalar value in degrees, with its sign representing the direction of the correction; for example, a positive value represents a clockwise correction. Second, the original beam direction of each radar pulse recorded in the radar azimuth signal data is obtained. The original beam direction is a scalar angle value. Then, the original beam direction angle value and the beam pointing correction angle value are directly added. Finally, the new angle value obtained after the addition operation is the corrected radar transmit beam direction.

[0083] Based on the pulse timing compensation amount in the azimuth signal adjustment parameter, the original signal sampling time point in the radar azimuth signal data is moved according to the time reference of the radar scanning cycle to generate a time-compensated radar azimuth signal.

[0084] First, the pulse timing compensation value is extracted from the azimuth signal adjustment parameters. The compensation value is a time value, and its positive or negative sign indicates the compensation direction. For example, a positive value indicates a time advance. Second, the original time tag of each sampling point in the radar azimuth signal data is obtained. Then, based on the pulse timing compensation value, a new time tag is calculated for each sampling point after compensation: the original time tag plus the pulse timing compensation value. Next, for each new time tag, the two original sampling points closest in time before and after it in the original signal sequence are searched. Using the time and amplitude of these two original sampling points, a linear interpolation algorithm is used to calculate the signal amplitude corresponding to the new time tag. Finally, the signal amplitude calculated from all new time tags and their corresponding interpolated values ​​constitutes the time-compensated radar azimuth signal.

[0085] The corrected radar transmit beam direction is combined with the time-compensated radar azimuth signal to reconstruct the phase and time tag sequence of the radar echo signal.

[0086] The corrected beam direction and time-compensated signal sequence contain new time tags and interpolated amplitudes. For each data point with a new time tag, the original phase information is extracted from its corresponding original sampling point. This original phase, new time tag, new amplitude, and corrected beam direction are then combined into a complete data record. Finally, all these data records are collected to form a reconstructed radar echo signal sequence, whose time tag is the updated time tag and whose phase is the retained original phase.

[0087] Based on the reconstructed phase and time label sequence, the original sampling point order of the radar azimuth signal data is rearranged to generate the target radar azimuth signal data.

[0088] The target radar azimuth signal data is a new dataset containing signal sampling information aligned with the corrected spatiotemporal reference. This includes the amplitude, reconstructed phase, and updated time tag of each sampling point, associated with the corrected beam direction. The reconstructed phase and time tag sequence, along with the corresponding signal amplitude, is obtained. Next, a new data structure is created. For each new time tag, it is stored in the new data structure along with the corresponding signal amplitude and reconstructed phase value. This process ensures that all output sampling points are aligned according to the corrected, synchronized time reference. At the same time, the corrected radar transmit beam direction is used as the associated attribute for this set of target radar azimuth signal data. The resulting new dataset is the target radar azimuth signal data.

[0089] For example, the beam direction adjustment parameters for a radar pulse's azimuth signal require a 0.12-degree leftward correction, and a 6-nanosecond timing delay. First, the pulse's originally recorded beam direction is 35.50 degrees. The system calculates the corrected direction: 35.50 degrees minus 0.12 degrees, resulting in a new beam direction of 35.38 degrees. Next, the system examines a sample point in the pulse's echo signal. The original time of this sample point is 1000 nanoseconds, the signal strength is X, and the original phase is P. Due to the 6-nanosecond timing delay, the new time point is 1006 nanoseconds. Using linear interpolation, the system calculates that the signal strength at this sample point at 1006 nanoseconds should be Y. The system then associates the new beam direction of 35.38 degrees with the new signal point information—the time of 1006 nanoseconds and the strength Y. The original phase P is also associated with this new time point and strength. Finally, the system generates updated target radar azimuth signal data. It includes such an entry: the signal associated with the beam pointing to 35.38 degrees, at time 1006 nanoseconds, the signal strength is Y and the phase is P. Similar processing is performed on all sampling points to obtain a complete set of compensated radar signal data.

[0090] Figure 3 FIG. 1 shows a flow chart of a method for determining fault type information provided by an embodiment of the present application, such as Figure 3 As shown, the method includes steps S310 to S340.

[0091] In one possible implementation, step S160: determining fault type information by respectively calculating multi-dimensional residual features between the fused radar azimuth data and the target radar azimuth signal data, the platform motion azimuth information calculated from the target platform motion attitude data, and the attitude azimuth change information calculated from the radar local attitude change data, includes: S310: Extracting the tangent direction angle value of the carrier motion trajectory from the target platform motion posture data as the platform motion orientation information.

[0092] Platform motion orientation information refers to an angle value calculated based on the overall motion trajectory of the mobile vehicle platform, which can represent the current instantaneous motion direction of the vehicle. This angle is defined in the radar reference coordinate system, for example, the angle relative to the front of the radar.

[0093] A time-series position coordinate sequence is extracted from the target platform's motion posture data. For the current analysis moment, the position coordinate points of several adjacent moments before and after are selected. The local tangent direction of the trajectory represented by these points at that moment is calculated through numerical differentiation and converted into the azimuth angle in the radar reference coordinate system. The angle value is the platform motion azimuth information.

[0094] S320: Extracting the beam pointing angle change amount of adjacent scanning cycles from the radar local attitude change data as attitude azimuth change information.

[0095] Attitude and azimuth change information refers to the change in beam pointing angle between consecutive radar scanning cycles caused by local vibration or slight attitude change of the radar equipment itself relative to the installation platform.

[0096] The time series of attitude angles is extracted from the radar's local attitude change data. For the current radar scan cycle and the immediately preceding scan cycle, the total azimuth deviation of the radar beam caused by the local attitude change during these two cycles is calculated. This difference is the attitude and azimuth change information.

[0097] S330: Calculate the arithmetic difference between the fused radar azimuth data and the target radar azimuth signal data to obtain a first residual component, calculate the arithmetic difference between the fused radar azimuth data and the platform motion azimuth information to obtain a second residual component, and calculate the arithmetic difference between the fused radar azimuth data and the attitude and azimuth change information to obtain a third residual component, and combine them to form a multi-dimensional residual feature vector.

[0098] The first residual component, the second residual component, and the third residual component represent the consistency differences between the fused optimal orientation estimate and the radar's own measurement, the orientation indicated by the platform motion, and the orientation indicated by the local attitude change, respectively.

[0099] First, the azimuth angle in the fused radar azimuth data of the current scan cycle is subtracted from the azimuth angle in the target radar azimuth signal data of the current scan cycle to obtain the first residual component. Then, the azimuth angle in the platform motion azimuth information of the current scan cycle is subtracted from the azimuth angle in the fused radar azimuth data of the current scan cycle to obtain the second residual component. Next, the difference between the azimuth angle in the fused radar azimuth data of the current scan cycle and the azimuth angle in the fused radar azimuth data of the previous scan cycle is calculated to obtain the cycle-by-cycle change in the fused radar azimuth. The azimuth angle difference in the attitude azimuth change information of the current scan cycle is subtracted from this cycle-by-cycle change in the fused radar azimuth to obtain the third residual component. Finally, these three calculated residual components are combined into a vector to form a multi-dimensional residual feature vector.

[0100] S340: Determine a target residual component in the multi-dimensional residual feature vector, and determine associated fault type information based on the target residual component.

[0101] In one feasible implementation, step S340: determining a target residual component in the multi-dimensional residual feature vector, and determining associated fault type information based on the target residual component, includes: The residual component whose absolute value in the multidimensional residual feature vector exceeds the preset threshold for more than a set number of times is determined as the target residual component; the fault type information corresponding to the target residual component is determined through the preset mapping relationship between the residual component and the fault type information.

[0102] The target residual component refers to one or more residual components that exhibit significant anomalies in the multidimensional residual feature vector. The mapping relationship between residual components and fault type information refers to a diagnostic rule base constructed based on historical data analysis and expert knowledge, which can determine the possible fault type indicated by one or more target residual components.

[0103] In each analysis cycle, the absolute value of each residual component in the multi-dimensional residual feature vector is calculated and compared with the corresponding amplitude threshold. Each residual component corresponds to a preset amplitude threshold and a set number of consecutive over-limit times. At the same time, the number of times the absolute value of each residual component exceeds the threshold continuously in multiple consecutive cycles is recorded. If the absolute value of a residual component continuously exceeds its threshold, and the number of consecutive over-limit times is greater than the preset set number, the residual component is determined as a target residual component. One or more target residual components are used as input, and the mapping relationship table is queried to find the fault type that matches the current state, which is the currently diagnosed fault type information.

[0104] For example, during the current radar scan cycle, the platform motion azimuth information representing the vehicle's current motion direction is first calculated through numerical differentiation based on the time-series position coordinates in the target platform motion attitude data, which is 35.40 degrees. Simultaneously, based on the attitude angle time series in the radar local attitude change data, the change in beam pointing angle due to local vibration in the current scan cycle relative to the previous scan cycle is calculated, i.e., the attitude azimuth change information is 0.02 degrees to the right. Next, the fused radar azimuth data for the current cycle is obtained as 35.45 degrees, the previous cycle as 35.42 degrees, and the target radar azimuth signal data for the current cycle as 35.05 degrees. Based on these data, the first residual R1 is calculated as 0.40 degrees, which is the difference between the current fused azimuth of 35.45 degrees and the target radar signal azimuth of 35.05 degrees. The second residual R2 is 0.05 degrees, which is the difference between the current fused azimuth of 35.45 degrees and the platform motion azimuth information of 35.40 degrees. The cycle-by-cycle change in the fused radar's azimuth is 0.03 degrees. The difference between this and the attitude azimuth change of 0.02 degrees gives a third residual, R3, of 0.01 degrees. The resulting multidimensional residual eigenvector for this cycle is [0.40, 0.05, 0.01].

[0105] Fault diagnosis is then performed. The preset residual amplitude threshold is 0.04 degrees, and it is only determined to be a target residual if it exceeds this threshold for three consecutive scanning cycles. Monitoring found that the absolute value of the first residual R1, 0.40 degrees, has exceeded 0.04 degrees for three consecutive cycles. Although the absolute value of the second residual R2, 0.05 degrees, exceeds the threshold in this cycle, it is assumed that it has not exceeded the limit continuously in the previous two cycles. The absolute value of the third residual R3, 0.01 degrees, remains within the threshold. Therefore, R1 is determined to be the target residual, and R2 and R3 are not target residuals. Comparing with the preset mapping relationship shown in Table 2, the entries corresponding to R1=1, R2=0, and R3=0 are found, and the fault type indicated is a radar encoder fault. Finally, the fault type information output by this radar azimuth monitoring is a radar encoder fault. Among them, 1 indicates that the corresponding first residual R1, second residual R2 or third residual R3 has been determined as a target residual component indicating an abnormality, while 0 indicates that the residual component is in a normal state and is not determined as a target residual. The table maps to specific fault types according to the combination of these 1s and 0s.

[0106] The first residual R1 The second residual R2 The third residual R3 Fault type 1 0 0 Radar encoder failure 0 1 0 Abnormal vehicle navigation IMU output 0 0 1 Radar vibration sensor failure 1 1 0 Coordinate system calibration error 1 0 1 Radar encoder and vibration sensor failure 0 1 1 Vehicle navigation and vibration sensor failure 1 1 1 Strong external electromagnetic interference 0 0 0 The system is running normally Table 2 Based on the same concept, the embodiment of the present application provides a modular radar position monitoring system for industrial testing. Figure 4 The modular radar position monitoring system for industrial testing provided in an embodiment of the present application is described in detail.

[0107] Figure 4 This is a structural block diagram of a modular radar position monitoring system for industrial testing shown in an embodiment of the present application.

[0108] like Figure 4 As shown, the modular radar position monitoring system for industrial testing may include: The data acquisition module 410 is used to acquire the platform motion attitude data of the mobile vehicle platform, the radar azimuth signal data and the radar local attitude change data of the radar; The data conversion module 420 is used to perform coordinate system conversion processing on the platform motion attitude data to generate target platform motion attitude data aligned with the reference coordinate system of the radar; The data processing module 430 is used to obtain the azimuth signal adjustment parameters by calculating the dynamic change characteristics of the azimuth signal propagation path based on the target platform motion posture data and the radar local posture change data; The data processing module 430 is further configured to perform adaptive signal waveform adjustment and timing synchronization processing on the radar azimuth signal data using the azimuth signal adjustment parameters to generate target radar azimuth signal data; The data processing module 430 is further configured to perform data fusion processing using a state estimation algorithm based on the target radar azimuth signal data, the target platform motion attitude data, and the radar local attitude change data to generate fused radar azimuth data; The analysis and diagnosis module 440 is used to determine the fault type information by respectively calculating the multi-dimensional residual characteristics between the fused radar azimuth data and the target radar azimuth signal data, the platform motion azimuth information calculated by the target platform motion attitude data, and the attitude azimuth change information calculated by the radar local attitude change data, so as to complete the radar azimuth monitoring in the industrial test-level scenario.

[0109] In one embodiment, the data processing module 430 is also used to perform data fusion processing based on the target radar azimuth signal data, the target platform motion posture data and the radar local posture change data using a state estimation algorithm to generate fused radar azimuth data. Before generating the fused radar azimuth data, the data processing module 430 is used to assign fusion weight information to the radar local posture change data, the target radar azimuth signal data and the target platform motion posture data based on the fluctuation amplitude of the radar local posture change data; based on the target radar azimuth signal data, the target platform motion posture data, the radar local posture change data and the fusion weight information, the data fusion processing is performed using a state estimation algorithm to generate the fused radar azimuth data.

[0110] In one embodiment, the data processing module 430 is specifically used to establish a dynamic state description structure with a radar scanning cycle as a step size, the dynamic state description structure including a platform position state component, a local attitude state component and an azimuth measurement state component; mapping the target platform motion attitude data to the observation value of the platform position state component, mapping the radar local attitude change data to the observation value of the local attitude state component, and mapping the target radar azimuth signal data to the observation value of the azimuth measurement state component; converting the platform motion weight in the fusion weight information into the observation confidence parameter of the platform position state component, converting the local attitude weight into the observation confidence parameter of the local attitude state component, and converting the azimuth signal weight into the observation confidence parameter of the azimuth measurement state component; performing recursive fusion in the dynamic state description structure, performing state update calculation based on the state prediction value of the previous scanning cycle, combining the observation value of the current scanning cycle and the corresponding observation confidence parameter, and outputting the fused radar azimuth data of the current scanning cycle.

[0111] In one embodiment, the data processing module 430 is specifically used to calculate the projection change rate of the velocity component perpendicular to the radar beam azimuth plane in the target platform motion posture data on the horizontal plane to obtain the azimuth angle offset rate; calculate the cumulative amount of the angular velocity component parallel to the normal of the radar beam azimuth plane in the radar local posture change data within the scanning period to obtain the azimuth cumulative offset; based on the time mark of the radar pulse transmission time point, the azimuth angle offset rate and the azimuth cumulative offset are linearly superimposed to obtain the dynamic change characteristic value of the azimuth signal propagation path; the dynamic change characteristic value is input into the constructed mapping relationship table to obtain the azimuth signal adjustment parameter used to correct the radar transmission beam pointing and receiving timing.

[0112] In one embodiment, the data processing module 430 is specifically used to modify the original beam direction in the radar azimuth signal data based on the beam pointing correction angle in the azimuth signal adjustment parameters to generate a corrected radar transmit beam direction; based on the pulse timing compensation amount in the azimuth signal adjustment parameters, move the original signal sampling time point in the radar azimuth signal data according to the time reference of the radar scanning cycle to generate a time-compensated radar azimuth signal; combine the corrected radar transmit beam direction with the time-compensated radar azimuth signal to reconstruct the phase and time label sequence of the radar echo signal; and rearrange the original sampling point sequence of the radar azimuth signal data based on the reconstructed phase and time label sequence to generate target radar azimuth signal data.

[0113] In one embodiment, the analysis and diagnosis module 440 is specifically used to extract the tangent direction angle value of the carrier motion trajectory from the target platform motion posture data as the platform motion azimuth information; extract the beam pointing angle change of adjacent scanning cycles from the radar local posture change data as the posture azimuth change information; calculate the arithmetic difference between the fused radar azimuth data and the target radar azimuth signal data to obtain the first residual component, calculate the arithmetic difference between the fused radar azimuth data and the platform motion azimuth information to obtain the second residual component, calculate the arithmetic difference between the fused radar azimuth data and the posture azimuth change information to obtain the third residual component, and combine them to form a multi-dimensional residual feature vector; determine the target residual component in the multi-dimensional residual feature vector, and determine the associated fault type information based on the target residual component.

[0114] In one embodiment, the analysis and diagnosis module 440 is specifically used to determine the residual component in the multi-dimensional residual feature vector whose absolute value exceeds the preset threshold number of times greater than the set number as the target residual component; and determine the fault type information corresponding to the target residual component through the preset mapping relationship between the residual component and the fault type information.

[0115] Figure 4 Each module in the system shown has the function of implementing Figures 1 to 3The functions of each step in the embodiment can achieve the corresponding technical effects, which will not be described in detail here for the sake of brevity.

[0116] Figure 5 A schematic diagram of the hardware structure of an electronic device provided in one embodiment of the present application is shown.

[0117] The electronic device may include a processor 510 and a memory 520 storing computer program instructions.

[0118] Specifically, the processor 510 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0119] The memory 520 may include a large-capacity memory for data or instructions. By way of example and not limitation, the memory 520 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 520 may include removable or non-removable (or fixed) media. Where appropriate, the memory 520 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 520 is a non-volatile solid-state memory.

[0120] The memory may include read-only memory (ROM), random access memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical / tangible memory storage devices. Thus, typically, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to the first aspect of the present disclosure.

[0121] The processor 510 reads and executes computer program instructions stored in the memory 520 to implement any one of the modular radar position monitoring methods for industrial testing in the above embodiments.

[0122] In one example, the electronic device may further include a communication interface 530 and a bus 540. Figure 5As shown, the processor 510 , the memory 520 , and the communication interface 530 are connected via a bus 540 and communicate with each other.

[0123] The communication interface 530 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0124] Bus 540 includes hardware, software, or both, and couples the components of the online data traffic metering device to each other. By way of example, and not limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industrial Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Area Network (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 540 may include one or more buses. Although the embodiments of the present application describe and illustrate specific buses, the present application contemplates any suitable bus or interconnect.

[0125] The electronic device can execute the modular radar azimuth monitoring method for industrial testing in the embodiment of the present application, thereby realizing the combination of Figures 1 to 3 A modular radar position monitoring method for industrial testing is described.

[0126] In addition, in conjunction with the modular radar position monitoring method for industrial testing in the above-mentioned embodiments, embodiments of the present application may provide a computer-readable storage medium for implementation. The computer-readable storage medium stores computer program instructions; when executed by a processor, the computer program instructions implement any of the modular radar position monitoring methods for industrial testing in the above-mentioned embodiments.

[0127] It should be understood that the present application is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and illustrated as examples. However, the method process of the present application is not limited to the specific steps described and illustrated. Those skilled in the art can make various changes, modifications, and additions, or change the order of the steps after understanding the spirit of the present application.

[0128] The functional blocks shown in the block diagrams described above can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they may be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, and the like. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments may be stored in a machine-readable medium or transmitted via a data signal carried in a carrier wave over a transmission medium or communication link. "Machine-readable medium" may include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memory, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, and the like. Code segments may be downloaded via a computer network such as the Internet or an intranet.

[0129] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps. In other words, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0130] Aspects of the present application have been described above with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present application. It should be understood that each block in the flowcharts and / or block diagrams, as well as combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine such that execution of these instructions by the processor of the computer or other programmable data processing device enables the implementation of the functions / actions specified in one or more blocks in the flowcharts and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It should also be understood that each block in the block diagrams and / or flowcharts, as well as combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware that performs the specified functions or actions, or by a combination of dedicated hardware and computer instructions.

[0131] The above description is only a specific embodiment of the present application. Those skilled in the art will clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the scope of protection of the present application.

Claims

1. A modular radar azimuth monitoring method for industrial testing, applied to radar azimuth signal monitoring of mobile vehicle platforms, characterized by: The method comprises: Acquiring platform motion posture data of the mobile vehicle platform, radar azimuth signal data of the radar, and radar local posture change data; Performing coordinate system conversion processing on the platform motion attitude data to generate target platform motion attitude data aligned with the reference coordinate system of the radar; Obtaining an azimuth signal adjustment parameter by calculating a dynamic change characteristic of an azimuth signal propagation path based on the target platform motion posture data and the radar local posture change data; performing adaptive signal waveform adjustment and timing synchronization processing on the radar azimuth signal data using the azimuth signal adjustment parameters to generate target radar azimuth signal data; Based on the target radar azimuth signal data, the target platform motion posture data and the radar local posture change data, a state estimation algorithm is used to perform data fusion processing to generate fused radar azimuth data; By respectively calculating the multi-dimensional residual features between the fused radar azimuth data and the target radar azimuth signal data, the platform motion azimuth information calculated from the target platform motion posture data, and the posture azimuth change information calculated from the radar local posture change data, the fault type information is determined to complete radar azimuth monitoring in industrial test-level scenarios.

2. The method according to claim 1, characterized in that Before performing data fusion processing based on the target radar azimuth signal data, the target platform motion posture data, and the radar local posture change data using a state estimation algorithm to generate fused radar azimuth data, the method further includes: Based on the fluctuation amplitude of the radar local attitude change data, allocating fusion weight information to the radar local attitude change data, the target radar azimuth signal data and the target platform motion attitude data; The method of performing data fusion processing based on the target radar azimuth signal data, the target platform motion posture data, and the radar local posture change data using a state estimation algorithm to generate fused radar azimuth data includes: Based on the target radar azimuth signal data, the target platform motion posture data, the radar local posture change data and the fusion weight information, a state estimation algorithm is used to perform data fusion processing to generate fused radar azimuth data.

3. The method according to claim 2, characterized in that The method of performing data fusion processing based on the target radar azimuth signal data, the target platform motion posture data, the radar local posture change data and the fusion weight information using a state estimation algorithm to generate fused radar azimuth data includes: Establishing a dynamic state description structure with a radar scanning period as a step length, wherein the dynamic state description structure includes a platform position state component, a local attitude state component, and an azimuth measurement state component; Mapping the target platform motion posture data to the observation value of the platform position state component, mapping the radar local posture change data to the observation value of the local posture state component, and mapping the target radar azimuth signal data to the observation value of the azimuth measurement state component; Converting the platform motion weight in the fusion weight information into the observation confidence parameter of the platform position state component, converting the local attitude weight into the observation confidence parameter of the local attitude state component, and converting the orientation signal weight into the observation confidence parameter of the orientation measurement state component; Recursive fusion is performed in the dynamic state description structure, and state update calculation is performed based on the state prediction value of the previous scanning cycle in combination with the observation value of the current scanning cycle and the corresponding observation confidence parameter, and the fused radar azimuth data of the current scanning cycle is output.

4. The method according to claim 1, wherein The method of obtaining the azimuth signal adjustment parameter by calculating the dynamic change characteristics of the azimuth signal propagation path according to the target platform motion posture data and the radar local posture change data includes: Calculating the projection change rate of the velocity component in the direction perpendicular to the radar beam azimuth plane in the target platform motion posture data on the horizontal plane to obtain the azimuth angle deviation rate; Calculating the cumulative amount of the angular velocity component parallel to the normal of the radar beam azimuth plane in the radar local attitude change data during the scanning period to obtain the azimuth cumulative offset; Based on the time mark of the radar pulse transmission time point, the dynamic change characteristic value of the azimuth signal propagation path is obtained by linearly superimposing the azimuth angle deviation rate and the azimuth cumulative deviation; The dynamically changing characteristic value is input into the constructed mapping relationship table to obtain the azimuth signal adjustment parameter used to correct the radar transmission beam pointing and receiving timing.

5. The method according to claim 1, wherein The method of using the azimuth signal adjustment parameter to perform adaptive signal waveform adjustment and timing synchronization processing on the radar azimuth signal data to generate target radar azimuth signal data includes: Modifying the original beam direction in the radar azimuth signal data based on the beam pointing correction angle in the azimuth signal adjustment parameter to generate a corrected radar transmit beam direction; Based on the pulse timing compensation amount in the azimuth signal adjustment parameter, shifting the original signal sampling time point in the radar azimuth signal data according to the time reference of the radar scanning cycle to generate a time-compensated radar azimuth signal; Combining the corrected radar transmit beam direction with the time-compensated radar azimuth signal to reconstruct a phase and time tag sequence of the radar echo signal; Based on the reconstructed phase and time tag sequence, the original sampling point sequence of the radar azimuth signal data is rearranged to generate target radar azimuth signal data.

6. The method according to claim 1, characterized in that The determining of the fault type information by respectively calculating multi-dimensional residual features between the fused radar azimuth data and the target radar azimuth signal data, the platform motion azimuth information calculated from the target platform motion attitude data, and the attitude azimuth change information calculated from the radar local attitude change data includes: Extracting the tangent direction angle value of the carrier motion trajectory from the target platform motion posture data as the platform motion orientation information; Extracting the beam pointing angle changes in adjacent scanning cycles from the radar local attitude change data as attitude azimuth change information; Calculating the arithmetic difference between the fused radar azimuth data and the target radar azimuth signal data to obtain a first residual component, calculating the arithmetic difference between the fused radar azimuth data and the platform motion azimuth information to obtain a second residual component, and calculating the arithmetic difference between the fused radar azimuth data and the attitude and azimuth change information to obtain a third residual component, and combining them to form a multi-dimensional residual feature vector; A target residual component is determined in the multi-dimensional residual feature vector, and associated fault type information is determined based on the target residual component.

7. The method according to claim 1, characterized in that The determining of a target residual component in the multi-dimensional residual feature vector, and determining associated fault type information based on the target residual component, includes: Determine the residual component in the multidimensional residual feature vector whose absolute value exceeds the preset threshold value more than a set number of times as the target residual component; The fault type information corresponding to the target residual component is determined through a preset mapping relationship between the residual component and the fault type information.

8. A modular radar position monitoring system for industrial testing, used for radar position signal monitoring of mobile vehicle platforms, characterized by: The system comprises: A data acquisition module, configured to acquire the platform motion posture data of the mobile vehicle platform, the radar azimuth signal data of the radar, and the radar local posture change data; a data conversion module, configured to perform coordinate system conversion processing on the platform motion attitude data to generate target platform motion attitude data aligned with the reference coordinate system of the radar; a data processing module, configured to obtain an azimuth signal adjustment parameter by calculating a dynamic change characteristic of an azimuth signal propagation path based on the target platform motion posture data and the radar local posture change data; The data processing module is further configured to perform adaptive signal waveform adjustment and timing synchronization processing on the radar azimuth signal data using the azimuth signal adjustment parameters to generate target radar azimuth signal data; The data processing module is further configured to perform data fusion processing using a state estimation algorithm based on the target radar azimuth signal data, the target platform motion posture data, and the radar local posture change data to generate fused radar azimuth data; The analysis and diagnosis module is used to determine the fault type information by respectively calculating the multi-dimensional residual characteristics between the fused radar azimuth data and the target radar azimuth signal data, the platform motion azimuth information calculated from the target platform motion posture data, and the posture azimuth change information calculated from the radar local posture change data, so as to complete radar azimuth monitoring in industrial test-level scenarios.

9. An electronic device, characterized in that: The device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the modular radar azimuth monitoring method for industrial testing according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement the modular radar azimuth monitoring method for industrial testing according to any one of claims 1 to 7.

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