Radar plot fusion method

Abstract

The invention relates to the technical field of multi-station radar data fusion, and discloses a radar plot fusion method, which comprises the steps of acquiring radar receiving information and radar parameters, performing preliminary calculation according to the radar receiving information to obtain target preliminary position information, and performing target plot fusion according to the target preliminary position information and the radar parameters. The method comprises the following steps: acquiring radar parameters, performing differential calculation to obtain a relative position difference, performing accurate calculation according to the radar parameters and the relative position difference to obtain a target accurate position, performing weighted average according to the target accurate position to obtain intermediate fusion data, and performing Kalman filtering according to the intermediate fusion data to obtain multi-station radar fusion data. According to the method, multi-station radar data fusion can be realized.

Description

Technical Field

[0001] The invention relates to the technical field of multi-station radar data fusion, and in particular to a radar point trace fusion method. Background Art

[0002] With the rapid development of radar technology, multi-station radar networking has gradually become the mainstream application mode of radar systems. Due to the limitations of its monitoring range and processing capabilities, single-station radar cannot fully cover the monitoring needs of airspace and frequency domains in dynamic scenarios. Multi-station radar networking can achieve full coverage of airspace and frequency domains and real-time perception of target information through the collaborative work of multiple radars, and is widely used in military, civil aviation, ground surveillance and other fields.

[0003] At present, the main technical methods for multi-station radar track fusion include joint detection based on probability statistics, probability hypothesis density method, Bayes estimation method, and data-driven interactive fusion algorithm. These methods process the target track data of multi-station radars through steps such as track association, data matching, and feature optimization. However, these methods rely on the detection performance parameters and prior information of radar equipment, and have limited support for the heterogeneity and real-time requirements of multi-station data. Especially when there are problems such as missed detection, false alarm, and track association errors, these traditional methods are difficult to generate reliable fusion results.

[0004] In summary, multi-station radar networking has gradually become an important mode for achieving full airspace and full frequency domain monitoring in the radar field. However, existing technologies mainly rely on single-station radar data processing methods, such as fusion algorithms based on probability statistics and track association. These methods are difficult to play an effective role in the face of complex multi-station networking environments, especially the lack of systematic means to generate multi-station radar fusion data. Summary of the invention

[0005] The invention provides a radar point trace fusion method to realize multi-station radar data fusion.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a radar point trace fusion method, comprising:

[0007] Obtain radar reception information and radar parameters;

[0008] Performing preliminary calculations based on the radar received information to obtain preliminary target position information;

[0009] Performing differential calculation according to the preliminary position information of the target and the radar parameters to obtain a relative position difference;

[0010] Performing precise calculations based on the radar parameters and the relative position difference to obtain the precise position of the target;

[0011] According to the precise position of the target, weighted averaging is performed to obtain intermediate fusion data;

[0012] Kalman filtering is performed based on the intermediate fusion data to obtain multi-station radar fusion data.

[0013] Preferably, the radar receiving information includes: radar signal delay, target azimuth and target elevation angle;

[0014] The radar parameters include: radar antenna position, radar angle resolution and radar range resolution.

[0015] Preferably, performing preliminary calculations based on the radar received information to obtain preliminary target position information includes:

[0016] Performing distance calculation according to the radar signal delay to obtain the target distance;

[0017] The target distance is calculated using the following formula:

[0018]

[0019] Among them, R i is the distance to the i-th target, c is the speed of light, Δt i is the delay of the i-th radar signal;

[0020] Calculate the target's initial position based on the target distance, the target azimuth and the target pitch angle to obtain target initial position information;

[0021] The initial position coordinate of the target is calculated by the following formula:

[0022] x i =R i sin(θ i )·cos(φ i )

[0023] Among them, x i is the initial position abscissa of the i-th target, θ i is the azimuth of the i-th target, φ i is the pitch angle of the i-th target;

[0024] The target's initial position ordinate is calculated using the following formula:

[0025] y i =R i ·sin(θ i )·sin(φ i )

[0026] Among them, y i is the initial position ordinate of the i-th target;

[0027] The target initial position height is calculated by the following formula:

[0028] z i =R i ·cos(θ i )

[0029] Among them, z i is the initial position height of the i-th target;

[0030] The target preliminary position information is obtained by integrating the target preliminary position abscissa, the target preliminary position ordinate and the target preliminary position height.

[0031] Preferably, performing differential calculation according to the preliminary position information of the target and the radar parameters to obtain a relative position difference comprises:

[0032] The relative position difference is calculated by the following formula;

[0033]

[0034] in, is the ith relative position difference, is the initial position information of the i-th target, is the i-th radar antenna position, is the angular resolution of the i-th radar, is the range resolution of the ith radar, and a is the weight coefficient.

[0035] Preferably, the method of performing accurate calculation according to the radar parameters and the relative position difference to obtain the accurate position of the target comprises:

[0036]

[0037] in, is the precise position of the i-th target, is the i-th radar antenna position, is the ith relative position difference, is the range resolution of the ith radar, R max The preset maximum distance value.

[0038] Preferably, the step of performing weighted averaging according to the precise position of the target to obtain intermediate fusion data comprises:

[0039] The intermediate fusion data is calculated by the following formula:

[0040]

[0041] in, is the intermediate fusion data, is the precise position of the i-th target, w i is the weighting coefficient of the ith radar, o is the correction factor, e i is the error compensation term of the i-th radar, and N is the total number of radars.

[0042] Preferably, performing Kalman filtering according to the intermediate fusion data to obtain multi-station radar fusion data includes:

[0043] The multi-station radar fusion data is calculated by the following formula:

[0044]

[0045] in, To fuse data from multiple radar stations, is the state prediction value of multi-station radar fusion data, K k is the Kalman gain, is the intermediate fusion data, and H is the observation matrix.

[0046] In a second aspect, the present invention provides a radar point-trace fusion system, comprising:

[0047] A data acquisition module is used to obtain radar reception information and radar parameters;

[0048] A preliminary calculation module, used to perform preliminary calculations based on the information received by the radar to obtain preliminary target position information;

[0049] A differential calculation module, used to perform differential calculation according to the preliminary position information of the target and the radar parameters to obtain a relative position difference;

[0050] An accurate calculation module, used to perform accurate calculation according to the radar parameters and the relative position difference to obtain the accurate position of the target;

[0051] A weighted averaging module is used to perform weighted averaging according to the precise position of the target to obtain intermediate fusion data;

[0052] The fusion module is used to perform Kalman filtering based on the intermediate fusion data to obtain multi-station radar fusion data.

[0053] In a third aspect, the present invention further provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements any one of the above-mentioned radar point track fusion methods when executing the computer program.

[0054] In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the radar point track fusion methods described above.

[0055] Compared with the prior art, the present invention has the following beneficial effects: the present invention provides a radar point trace fusion method, the method comprising: acquiring radar reception information and radar parameters, performing preliminary calculation according to the radar reception information to obtain preliminary target position information, performing differential calculation according to the preliminary target position information and the radar parameters to obtain relative position difference, performing precise calculation according to the radar parameters and the relative position difference to obtain the precise position of the target, performing weighted averaging according to the precise position of the target to obtain intermediate fusion data, performing Kalman filtering according to the intermediate fusion data to obtain multi-station radar fusion data.

[0056] In the present invention, the method obtains radar receiving information and radar parameters, performs preliminary calculation on the receiving information, obtains preliminary position information of the target, performs differential calculation based on the preliminary position information of the target and the radar parameters to determine the relative position difference; further uses the radar parameters and the relative position difference to perform precise calculation, thereby obtaining the precise position of the target. According to the precise position of the target, intermediate fusion data is generated by weighted average method, and then the intermediate fusion data is processed by Kalman filtering to obtain multi-station radar fusion data. The method realizes multi-station radar fusion data. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic diagram of the flow of the radar point trace fusion method provided by the first embodiment of the present invention;

[0058] Figure 2 It is a schematic diagram of a radar point trace fusion system provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] Reference Figure 1 The first embodiment of the present invention provides a radar point trace fusion method, comprising the following steps:

[0061] S11, obtaining radar reception information and radar parameters;

[0062] S12, performing preliminary calculations based on the radar received information to obtain preliminary target position information;

[0063] S13, performing differential calculation according to the preliminary position information of the target and the radar parameters to obtain a relative position difference;

[0064] S14, performing accurate calculation according to the radar parameters and the relative position difference to obtain the accurate position of the target;

[0065] S15, performing weighted averaging according to the precise position of the target to obtain intermediate fusion data;

[0066] S16, performing Kalman filtering based on the intermediate fusion data to obtain multi-station radar fusion data.

[0067] In step S11, radar receiving information and radar parameters are obtained; the radar receiving information includes: radar signal delay, target azimuth and target elevation angle;

[0068] The radar parameters include: radar antenna position, radar angle resolution and radar range resolution.

[0069] It is worth noting that in step S11, by acquiring radar reception information and radar parameters, a data input basis is established to provide reliable raw data support for subsequent calculations and fusion. Radar reception information includes radar signal delay, target azimuth and target elevation, while radar parameters include radar antenna position, radar angle resolution and radar range resolution. The following is the detailed implementation process and related technical background of this step.

[0070] The acquisition of radar receiving information is the first step in the entire radar point fusion process, and its main goal is to collect real-time observation data related to the target. The radar signal delay is obtained by the radar system by recording the difference between the signal transmission time and the echo signal reception time. This delay time reflects the time required for the radar signal to be transmitted to the target and reflected back to the radar antenna, and is an important basis for calculating the distance between the target and the radar. The radar system requires high-precision time synchronization equipment to record the transmission and reception time of the signal to ensure the accuracy of the delay time data. The accuracy of the time synchronization equipment is at the nanosecond level to avoid significant deviations in distance calculations due to time measurement errors.

[0071] The target azimuth and elevation describe the relative position of the target in the horizontal and vertical planes respectively, and are important data for determining the spatial direction of the target. The azimuth is the horizontal angle of the target relative to the positive direction of the radar antenna, which is determined by the radar system through the signal intensity peak combined with the horizontal scanning angle of the antenna. The elevation is the relative angle of the target in the vertical direction, which is determined by the vertical scanning function of the antenna combined with the received signal strength. The measurement accuracy of the azimuth and elevation directly affects the initial calculation accuracy of the target position, so the scanning angle step and resolution of the antenna need to be strictly calibrated to ensure the reliability of the observation data.

[0072] In addition to the target information received in real time, radar parameters are also important inputs for calculating the target position. The radar antenna position refers to the fixed coordinate position of the radar in three-dimensional space, which is calibrated and recorded by high-precision geolocation equipment when the radar system is deployed. These position data are stored in the form of three-dimensional coordinates for subsequent relative position calculations. The accuracy of the radar antenna position depends on the environmental measurement and calibration results during the system deployment phase. Therefore, when the radar system is initialized, the antenna position will be loaded into the radar system as a basic parameter for subsequent calculations.

[0073] Radar angular resolution is the system's ability to distinguish targets in azimuth and elevation, and its value is determined by the beam width of the radar antenna. The smaller the angular resolution, the stronger the radar's ability to distinguish the target direction. Specifically, the angular resolution reflects the minimum angular separation between two targets within the radar's field of view. If the angular difference between the two targets is less than the angular resolution, they cannot be distinguished. Therefore, the angular resolution not only affects the accuracy of target direction measurement, but also affects the radar system's target detection and tracking capabilities.

[0074] Radar range resolution is the minimum ability of the system to distinguish the distance to a target, and its value is determined by the pulse width or bandwidth of the radar's transmitted signal. The higher the range resolution, the more accurately the radar can measure the distance to a target, and the clearer it can distinguish closer targets. For high-resolution radars, the range resolution is sub-meter, while for lower-resolution radars, the range resolution is several meters or even higher. The specific value of the range resolution is determined by the radar's design parameters and is recorded after experimental verification during the system calibration phase.

[0075] After obtaining all necessary radar reception information and parameters, the system will pre-process these data, including formatting and consistency checking of signal delay, azimuth, elevation, antenna position and other data, to ensure that the data can be correctly parsed by the subsequent calculation model. At the same time, the system also needs to store and manage the received data in real time to ensure that these input data can be quickly and efficiently called in each step of point-trace fusion.

[0076] In summary, in step S11, the radar reception information related to the target is successfully obtained through the high-precision time synchronization equipment and the observation function of the radar system, and combined with the radar parameters recorded in the system initialization phase, it provides comprehensive and accurate data input for the subsequent point-track fusion calculation. Signal delay, azimuth and elevation angles constitute the real-time observation information of the target, while antenna position, angle resolution and distance resolution provide a fixed benchmark for the radar system for subsequent calculations. The acquisition and processing of each data strictly follows the design and calibration process of the radar system to ensure the accuracy and reliability of the entire point-track fusion process.

[0077] In step S12, preliminary calculation is performed based on the radar received information to obtain preliminary target position information; including:

[0078] Performing distance calculation according to the radar signal delay to obtain the target distance;

[0079] The target distance is calculated using the following formula:

[0080]

[0081] Among them, R i is the distance to the i-th target, c is the speed of light, Δt i is the delay of the i-th radar signal;

[0082] Calculate the target's initial position based on the target distance, the target azimuth and the target pitch angle to obtain target initial position information;

[0083] The initial position coordinate of the target is calculated by the following formula:

[0084] x i =R i ·sin(θ i )·cos(φ i )

[0085] Among them, x i is the initial position abscissa of the i-th target, θ i is the azimuth of the i-th target, φ i is the pitch angle of the i-th target;

[0086] The target's initial position ordinate is calculated using the following formula:

[0087] y i =R i ·sin(θ i )·sin(φ i )

[0088] Among them, y i is the initial position ordinate of the i-th target;

[0089] The target initial position height is calculated by the following formula:

[0090] z i =R i ·cos(θ i )

[0091] Among them, z i is the initial position height of the i-th target;

[0092] The target preliminary position information is obtained by integrating the target preliminary position abscissa, the target preliminary position ordinate and the target preliminary position height.

[0093] It is worth noting that the process of calculating the preliminary position information of the target in detail based on the radar received information includes obtaining the target distance from the radar signal delay, and then determining the three-dimensional spatial coordinates of the target using the target azimuth and elevation angle. The implementation of each step requires rigorous calculation and precise parameter acquisition to ensure the accuracy and reliability of the preliminary position information of the target.

[0094] First of all, the acquisition of radar signal delay is the core basis for target distance calculation. The radar system transmits an electromagnetic pulse signal, and when receiving the echo reflected from the target, it records the difference between the transmission time and the reception time, which is called signal delay. In order to obtain this delay value, the radar system needs to have high-precision time synchronization capabilities and use nanosecond-level time measurement equipment to ensure the accuracy of the delay time. The recording of the reception time and the transmission time is completed by the time control module inside the radar system. This module provides a stable clock signal through a high-frequency crystal oscillator. Any error in time recording will directly affect the calculation accuracy of the target distance.

[0095] Next, the signal delay is used to calculate the distance between the target and the radar. The speed of radar signal propagation is the speed of light, and the target distance is equal to half of the round-trip propagation distance of the signal. During the calculation process, the radar needs to call the fixed value of the speed of light in real time, combined with the delay time, to obtain the straight-line distance between the target and the radar. This calculation process is completed by the digital signal processing unit, which will output the target distance value in real time based on the input delay time and speed of light parameters. It should be noted here that since the strength of the target reflection signal is affected by environmental factors, the radar also needs to perform threshold detection on the signal strength to ensure that the calculated delay time comes from the real target, not clutter or interference signals.

[0096] After obtaining the straight-line distance between the target and the radar, the three-dimensional spatial position of the target is calculated. The azimuth and elevation angles of the target are important parameters that describe the spatial position of the target. The azimuth angle represents the angle of the target in the horizontal plane relative to the positive direction of the radar antenna, while the elevation angle represents the angle of the target in the vertical direction. The acquisition of these two angles depends on the scanning system of the radar antenna. The radar antenna scans in fixed angle steps in the horizontal and vertical directions, and determines the peak point of the target signal based on the received signal strength, thereby obtaining the azimuth and elevation angles. In order to ensure accuracy, the step value of the scanning angle is determined by the angular resolution of the radar antenna. The smaller the step value, the higher the angle measurement accuracy.

[0097] Based on the calculated target distance, azimuth and pitch angle, the horizontal coordinate, vertical coordinate and height of the target in three-dimensional space can be calculated step by step. The calculation of the horizontal coordinate combines the target distance and azimuth, while considering the projection angle of the target in the horizontal plane, and the horizontal position of the target is obtained through the trigonometric function relationship. The calculation of the vertical coordinate combines the target distance, azimuth and pitch angle to further refine the actual position distribution of the target in the horizontal plane. The calculation of the height is based on the target distance and pitch angle to determine the vertical position of the target. These calculations are completed by the mathematical processing unit inside the radar, which calls the angle data and distance data input in real time and uses a precise mathematical model to output the three-dimensional coordinate value of each target.

[0098] Finally, the horizontal coordinate, vertical coordinate and height of the target are integrated into a unified three-dimensional coordinate form, which is the preliminary position information of the target. This integration process is completed through the data fusion module, which combines the three coordinate values ​​into a structured position information data packet and outputs it to the subsequent differential calculation or fusion algorithm for use. The integrated position information not only describes the spatial position of the target, but also provides a unified input format for subsequent calculations to facilitate further processing of multi-station radar point fusion. The entire process requires strict verification of each input data and ensures the reliability and accuracy of the output data through precise calculation processes, thereby laying the foundation for real-time and high-precision fusion of multi-station radar systems.

[0099] In step S13, a differential calculation is performed according to the preliminary target position information and the radar parameters to obtain a relative position difference; including:

[0100] The relative position difference is calculated by the following formula;

[0101]

[0102] in, is the ith relative position difference, is the initial position information of the i-th target, is the i-th radar antenna position, is the angular resolution of the i-th radar, is the range resolution of the ith radar, and a is the weight coefficient.

[0103] It is worth noting that the difference calculation is performed based on the preliminary position information of the target and the radar parameters to obtain the relative position difference of the target relative to the radar. This step corrects the preliminary position information of the target and the radar antenna, combines the angle resolution and distance resolution of the radar, and introduces weight coefficients to dynamically adjust the data in different situations, thereby providing a basis for subsequent accurate calculations.

[0104] First, the preliminary position information of the target is obtained through the pre-order calculation, which specifically includes the three-dimensional spatial position of the target (horizontal coordinate, vertical coordinate, height). The radar antenna position is the fixed reference point of the radar system in the spatial coordinate system, and its data is recorded by high-precision positioning equipment when the system is deployed. By calculating the vector difference between the preliminary position information of the target and the radar antenna position, the relative spatial offset of the target in the radar coordinate system can be preliminarily determined. This vector difference is the key data describing the relative position relationship between the target and the radar.

[0105] Based on the vector difference, the radar's angle resolution and distance resolution are introduced to correct the data. Angle resolution is the minimum resolvable angle of the radar detection system in the azimuth and elevation directions, and this value directly affects the angle error correction amplitude of the target position. Distance resolution is the radar's minimum resolution capability at different distances between targets, and its value determines the radar's accuracy in measuring the distance between the target and the radar antenna. Both parameters are determined by the radar system's hardware design, and specific values ​​are provided by the radar platform.

[0106] Next, the relative position difference of the target is further corrected by introducing the weight coefficient. The selection of the weight coefficient is the key to this step, which directly affects the corrected relative position difference result. The selection of the weight coefficient needs to be dynamically adjusted according to the actual radar working environment and system characteristics. For example, when the radar system works in a high signal-to-noise ratio environment, the weight coefficient can be assigned a higher value, and the weight coefficient should be 0.85 to enhance the impact of high-precision data on the final result. At this time, the data of angle resolution and distance resolution have a greater effect on the difference correction.

[0107] On the contrary, in complex environments, the weight coefficient needs to be selected as a lower value, and the weight coefficient should be 0.35 to reduce the impact of environmental interference on the calculation results. At the same time, it is also necessary to dynamically adjust the correction amplitude of the resolution parameter to the calculation to adapt to the reliability of data under different conditions. For example, when the radar angle resolution is low, there are large errors in the azimuth and elevation angle data. At this time, the weight ratio of the angle resolution correction needs to be reduced, and other reliable data sources should be used for compensation first.

[0108] By combining the vector difference, angle resolution, distance resolution and weight coefficient, the relative position difference of the target is calculated comprehensively. This result effectively combines the measurement characteristics of the radar equipment and the influence of the actual working environment, providing a corrected and reliable data basis for the subsequent accurate calculation of the target position. The entire calculation process accurately controls the dynamic adjustment of the resolution parameters and weight coefficients, ensuring that the relative position difference output in the end has high accuracy and applicability.

[0109] In step S14, accurate calculation is performed according to the radar parameters and the relative position difference to obtain the accurate position of the target; including:

[0110]

[0111] in, is the precise position of the i-th target, is the i-th radar antenna position, is the ith relative position difference, is the range resolution of the ith radar, R max The preset maximum distance value.

[0112] It is worth noting that the precise position of the target is obtained by combining radar parameters with relative position differences. This process introduces the resolution parameters and the maximum design distance value of the radar system based on the target position information obtained by preliminary calculation, and further corrects the relative position difference to ensure that the final target position is highly accurate and reliable.

[0113] First, the precise position of the target is determined by the radar antenna position and the relative position difference. The radar antenna position is a known fixed reference point of the radar system in three-dimensional space, and its accuracy is guaranteed by the initial deployment and calibration process of the radar. The relative position difference is calculated in the previous step using the target's preliminary position information and radar parameters, and describes the preliminary spatial offset between the target and the radar.

[0114] The preset maximum distance value is calculated through the key performance parameters and physical models of the radar system, including transmit power, receive sensitivity, antenna gain, signal bandwidth, target reflection cross-sectional area, and environmental attenuation characteristics. Specifically, the radar's transmit power determines the initial energy of signal propagation. During the propagation process, the signal will gradually attenuate due to atmospheric absorption, scattering, and other environmental effects. Therefore, the maximum distance that the signal can reach must be calculated based on the power loss on the propagation path. At the same time, receive sensitivity is an important factor affecting the preset maximum distance, representing the minimum signal power threshold that the radar system can receive. Combined with the signal propagation model, it is possible to calculate whether the power of the signal when it reaches the maximum range is higher than the receive sensitivity, thereby determining whether the range is valid.

[0115] In addition, antenna gain enhances the echo strength by concentrating signal energy in a specific direction. This feature needs to be combined with the target's reflection cross-sectional area to calculate the strength of the target echo signal. The target reflection cross-sectional area is the target's ability to reflect radar signals, which directly affects the detectability of the echo signal. When calculating the maximum distance, it is assumed that the target has a minimum detectable reflection cross-sectional area, and the path loss of signal propagation and reflection is derived from this. The signal bandwidth determines the range resolution of the radar system and also affects the amount of effective information retained by the signal during propagation. Its contribution to target detection accuracy must be considered in the calculation.

[0116] After integrating all parameters, the radar system's preset maximum distance value can be calculated through the radar equation. For example, for a radar with a transmit power of 10 kilowatts, a receiving sensitivity of -110 dBm, and an antenna gain of 30 dB, the calculated result is 200 kilometers at a specific frequency and target reflection characteristics. The preset maximum distance value is ultimately defined as a performance limit parameter of the radar, which is used to constrain the range of target position calculations and ensure that the output results have physical meaning and are consistent with actual detection capabilities.

[0117] During the calculation process, the core of the correction is to scale the relative position difference to compensate for the error caused by the distance resolution. Specifically, the ratio of the distance resolution to the maximum distance value determines the correction amplitude. A larger distance resolution means a larger measurement error, and the correction ratio is correspondingly smaller to reduce the impact of the error on the target position calculation. Conversely, a smaller distance resolution means a higher measurement accuracy and a larger correction ratio, thereby enhancing the contribution of the relative position difference to the precise position.

[0118] The corrected relative position difference is added to the radar antenna position to obtain the precise position of the target. The radar antenna position provides the reference point of the target in the radar coordinate system, while the corrected relative position difference further refines the specific position of the target in space. Through this calculation process, the precise position of the target not only reflects the relative relationship between the target and the radar, but also takes into account the resolution characteristics and performance limitations of the radar system.

[0119] Finally, the precise position of the target is output in the form of three-dimensional coordinates for subsequent weighted averaging or fusion processing. Each step of this process strictly relies on high-precision input data and parameters. By dynamically correcting the relative position difference, the accuracy and consistency of the target position calculation are ensured, providing a solid foundation for achieving high-precision targets of multi-station radar point fusion.

[0120] In step S15, weighted averaging is performed according to the precise position of the target to obtain intermediate fusion data; including:

[0121] The intermediate fusion data is calculated by the following formula:

[0122]

[0123] in, is the intermediate fusion data, is the precise position of the i-th target, w i is the weighting coefficient of the ith radar, o is the correction factor, e i is the error compensation term of the i-th radar, and N is the total number of radars.

[0124] It is worth noting that in step S15, according to the precise position of the target, weighted averaging is performed to obtain intermediate fusion data. The core of this process is to optimize the fusion result by combining the precise position of the target, weighting coefficient, correction factor and error compensation item of each radar, so that the intermediate fusion data has high accuracy and robustness. The role of each parameter and the specific calculation process are reflected in detail in this step.

[0125] First, the intermediate fusion data is a combination of the measurement results of multiple radars on the same target. Its purpose is to eliminate the errors and deviations in the measurement of a single radar by fusing the observation information of multiple radars, so as to obtain more reliable target position information. The precise position of the target of each radar is based on the previous precise calculation and is the independent estimation result of the target position by the radar. However, due to the differences in the measurement capabilities of each radar and the influence of the actual environment, the quality of the measurement data of each radar is different. Therefore, a weighting coefficient needs to be introduced in the fusion calculation to reflect the importance of each radar data to the final fusion result.

[0126] The selection of weighting coefficients is directly related to the accuracy of the fusion results. In practical applications, the weighting coefficients are dynamically adjusted according to factors such as the signal-to-noise ratio, resolution, target echo intensity, and working environment of each radar. For example, for radars with higher signal-to-noise ratios and higher resolutions, the credibility of their measurement data is higher, and the weighting coefficient can be assigned a larger weight value, and the weighting coefficient is taken as 0.75. On the contrary, for radars with low signal-to-noise ratios, poor resolutions, and greater environmental interference, the uncertainty of their measurement results is higher, and the weighting coefficient should be reduced accordingly, and the weighting coefficient is taken as 0.3. This weight distribution mechanism can effectively highlight the contribution of high-quality data to the fusion results, while suppressing the interference of low-quality data.

[0127] In addition to the weighting coefficient, the correction factor is also a key parameter in this step. The correction factor is mainly used to correct the deviation in the weighted averaging process to ensure that the fusion result is closer to the actual position of the target. The value of the correction factor is based on the statistical analysis of historical data and experimental verification. For example, in the long-term operation of a multi-radar system, the average deviation and variation range between the measurement results of each radar and the actual position can be calculated by statistically analyzing the observation results of the known target position. Based on these statistical data, a reasonable correction factor value can be set to compensate for the systematic error and algorithm deviation of the radar system. The correction factor is a fixed value or dynamically adjusted within a certain range, depending on the design of the radar system and the actual operating conditions.

[0128] The error compensation term is another important correction parameter, which is used to correct the measurement error caused by the radar hardware characteristics or environmental factors. The error compensation term of each radar is determined based on the performance evaluation of the radar and the deviation analysis of the actual measurement results. For example, some radars have systematic errors when measuring the target distance due to hardware aging or insufficient calibration. This error can be corrected by the error compensation term. Similarly, there are specific interference factors in the radar working environment, and the error compensation term can also be used to reduce the impact of these factors on the measurement results. In the specific calculation, the value of the error compensation term is obtained through regular system calibration and environmental modeling, and is introduced one by one in the fusion calculation.

[0129] In the actual fusion calculation, the intermediate fusion data is obtained by weighted summing and normalizing the precise target position, weighting coefficient, correction factor and error compensation item of each radar. The precise target position of each radar is first multiplied by its corresponding weighting coefficient to obtain the weighted value of the radar data in the fusion calculation. Then, the weighted values ​​of all radars are added together, and the error compensation item is added to obtain the unnormalized fusion result. Finally, the unnormalized result is normalized using the sum of all weighting coefficients plus the correction factor to obtain the final intermediate fusion data. Through this calculation process, the intermediate fusion data fully integrates the measurement information of each radar, while eliminating the influence of systematic errors and environmental interference, providing high-quality input for subsequent multi-station radar fusion.

[0130] In general, this step constructs a sophisticated fusion calculation framework by introducing weighting coefficients, correction factors, and error compensation items. The weighting coefficients are dynamically adjusted according to the quality of the radar measurement data, the correction factors compensate for the system errors based on historical statistical data, and the error compensation items are corrected for radar hardware characteristics and environmental interference. The comprehensive application of these parameters ensures the high accuracy and reliability of the intermediate fusion data, laying a solid foundation for the subsequent processing of multi-station radar fusion.

[0131] In step S16, Kalman filtering is performed according to the intermediate fusion data to obtain multi-station radar fusion data, including:

[0132] The multi-station radar fusion data is calculated by the following formula:

[0133]

[0134] in, To fuse data from multiple radar stations, is the state prediction value of multi-station radar fusion data, K k is the Kalman gain, is the intermediate fusion data, and H is the observation matrix.

[0135] It is worth noting that the multi-station radar fusion data is obtained by performing Kalman filtering on the intermediate fusion data. Kalman filtering is a recursive estimation method that is used to minimize the estimation error and improve the accuracy and stability of the data by weighting the dynamic prediction and real-time observation of the system. In this process, the Kalman filter gradually optimizes the fusion data of the target by dynamically adjusting multiple key parameters, including the state prediction value, Kalman gain, and observation matrix. This method is widely used in multi-station radar systems and has significant advantages because it can update the target state in real time in dynamic scenarios.

[0136] The core of Kalman filtering is to estimate the target state by alternating between prediction and correction. In the prediction step, the target state prediction value at the current moment is calculated based on the fusion data state and system model at the previous moment. The target state prediction value is the prediction result of the radar system on the target position, speed and other information based on the physical model and dynamic laws, and is recorded as This parameter represents the estimated value of the multi-station radar fusion data at the current moment, but is not combined with the current observation data. Its calculation process depends on the motion model of the radar system, such as the uniform linear motion model or the acceleration model. The target state prediction value is the basis of the filtering process, which provides the initial input for the state correction at the current moment.

[0137] In the correction step, the state prediction value is adjusted by introducing intermediate fusion data and observation matrix. The intermediate fusion data is the fusion result obtained by weighted averaging of multiple radar observation data, reflecting the actual observation value of the target at the current moment. The observation matrix H describes the relationship between the observation value and the state prediction value, and is a bridge connecting the observation data and the system state. The specific form of the observation matrix is ​​determined by the measurement model of the system. For example, for the three-dimensional position observation of the target, the observation matrix is ​​a matrix that extracts the three-dimensional position part of the target state. If the target state contains more variables, such as velocity and acceleration, the dimension and structure of the observation matrix will change accordingly. The accuracy of the observation matrix directly affects the effect of state correction, so it needs to be accurately set according to the radar measurement model and the dynamic characteristics of the target.

[0138] During the calibration process, one of the key parameters is the Kalman gain K k , which is the weight factor of the filtering algorithm, is used to balance the contribution of the state prediction value and the observation value to the target state estimation. The size of the Kalman gain is determined by the uncertainty of the prediction value and the uncertainty of the observation value. When the prediction uncertainty is large and the reliability of the observation data is high, the Kalman gain will be larger, making the observation value have a higher weight in the state update, thereby enhancing the influence of the observation data on the target state estimation. Conversely, when the observation data is more disturbed and the prediction value is more reliable, the Kalman gain will be smaller, and the state update will rely more on the prediction value. The calculation of the Kalman gain involves the state covariance matrix and the observation noise covariance matrix. Through the interaction of these parameters, the weighted ratio of the prediction value to the observation value is dynamically adjusted to achieve the optimal state estimation.

[0139] Through the iterative update of the Kalman filter, the target state at the current moment is optimized to the fusion data closest to the true value. This update process is recursive, that is, the state estimation result at the current moment will be used as the state prediction value at the next moment, and will continue to be combined with the new observation data for filtering. This recursiveness enables the Kalman filter to update the target state in real time in a dynamic environment, adapt to changes in the target's motion trajectory, and maintain high accuracy and low error.

[0140] In practical applications, the advantage of Kalman filtering in multi-station radar systems is not only reflected in the high-precision fusion of single targets, but also in the ability to maintain stable performance in multi-target scenarios. Through precise adjustment of the Kalman gain, state prediction value, and observation matrix, the filtering algorithm can effectively handle the noise and interference in multi-radar observation data, improve the reliability of multi-station radar fusion data, and provide high-quality input for subsequent target tracking and situation analysis. Ultimately, the output multi-station radar fusion data reflects the comprehensive estimation of the target position by multiple radars. It is high-precision target information obtained through alternating optimization of prediction and correction in a dynamic environment, providing important technical support for the application of radar networking systems.

[0141] Reference Figure 2 The second embodiment of the present invention provides a radar point trace fusion system, comprising:

[0142] A data acquisition module is used to obtain radar reception information and radar parameters;

[0143] A preliminary calculation module, used to perform preliminary calculations based on the information received by the radar to obtain preliminary target position information;

[0144] A differential calculation module, used to perform differential calculation according to the preliminary position information of the target and the radar parameters to obtain a relative position difference;

[0145] An accurate calculation module, used to perform accurate calculation according to the radar parameters and the relative position difference to obtain the accurate position of the target;

[0146] A weighted averaging module is used to perform weighted averaging according to the precise position of the target to obtain intermediate fusion data;

[0147] The fusion module is used to perform Kalman filtering based on the intermediate fusion data to obtain multi-station radar fusion data.

[0148] It should be noted that a radar point trace fusion system provided in an embodiment of the present invention is used to execute all the process steps of a radar point trace fusion method in the above embodiment, and the working principles and beneficial effects of the two correspond one to one, so they will not be repeated here.

[0149] The embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a precise calculation program. When the processor executes the computer program, the steps in the above-mentioned radar point trace fusion method embodiments are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the fusion module.

[0150] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0151] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0152] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0153] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0154] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0155] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0156] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A radar point trace fusion method, characterized in that: include: Obtain radar reception information and radar parameters; Performing preliminary calculations based on the radar received information to obtain preliminary target position information; Performing differential calculation according to the preliminary position information of the target and the radar parameters to obtain a relative position difference; Performing precise calculations based on the radar parameters and the relative position difference to obtain the precise position of the target; According to the precise position of the target, weighted averaging is performed to obtain intermediate fusion data; Kalman filtering is performed based on the intermediate fusion data to obtain multi-station radar fusion data.

2. The radar point trace fusion method according to claim 1, characterized in that: The radar receiving information includes: radar signal delay, target azimuth and target elevation angle; The radar parameters include: radar antenna position, radar angle resolution and radar range resolution.

3. The radar point trace fusion method according to claim 2, characterized in that: The step of performing preliminary calculations based on the radar received information to obtain preliminary target location information includes: Performing distance calculation according to the radar signal delay to obtain the target distance; The target distance is calculated using the following formula: Among them, R i is the distance to the ith target, c is the speed of light, Δt i is the delay of the i-th radar signal; Calculate the target's initial position based on the target distance, the target azimuth and the target pitch angle to obtain target initial position information; The initial position coordinate of the target is calculated by the following formula: x i =R i ·sin(θ i )·cos(φ i ) Among them, x i is the initial position abscissa of the i-th target, θ i is the azimuth of the i-th target, φ i is the pitch angle of the i-th target; The target's initial position ordinate is calculated using the following formula: y i =R i ·sin(θ i )·sin(φ i ) Among them, y i is the ordinate of the initial position of the i-th target; The target initial position height is calculated by the following formula: z i =R i ·cos(θ i ) Among them, z i is the initial position height of the i-th target; The target preliminary position information is obtained by integrating the target preliminary position abscissa, the target preliminary position ordinate and the target preliminary position height.

4. The radar point trace fusion method according to claim 2, characterized in that: The step of performing differential calculation according to the preliminary target position information and the radar parameters to obtain a relative position difference comprises: The relative position difference is calculated by the following formula; in, is the ith relative position difference, is the initial position information of the i-th target, is the i-th radar antenna position, is the angular resolution of the i-th radar, is the range resolution of the ith radar, and a is the weight coefficient.

5. The radar point trace fusion method according to claim 1, characterized in that: The accurate calculation is performed according to the radar parameters and the relative position difference to obtain the accurate position of the target; include: in, is the precise position of the i-th target, is the i-th radar antenna position, is the ith relative position difference, is the range resolution of the ith radar, R max The preset maximum distance value.

6. The radar point trace fusion method according to claim 1, characterized in that: The method of performing weighted averaging according to the precise position of the target to obtain intermediate fusion data includes: The intermediate fusion data is calculated by the following formula: in, is the intermediate fusion data, is the precise position of the i-th target, w i is the weighting coefficient of the ith radar, o is the correction factor, e i is the error compensation term of the i-th radar, and N is the total number of radars.

7. The radar point trace fusion method according to claim 1, characterized in that: According to the intermediate fusion data, Kalman filtering is performed to obtain multi-station radar fusion data, including: The multi-station radar fusion data is calculated by the following formula: in, To fuse data from multiple radar stations, is the state prediction value of multi-station radar fusion data, K k is the Kalman gain, is the intermediate fusion data, and H is the observation matrix.

8. A radar point trace fusion system, characterized in that: include: A data acquisition module is used to obtain radar reception information and radar parameters; A preliminary calculation module, used to perform preliminary calculations based on the information received by the radar to obtain preliminary target position information; A differential calculation module, used to perform differential calculation according to the preliminary position information of the target and the radar parameters to obtain a relative position difference; An accurate calculation module, used to perform accurate calculation according to the radar parameters and the relative position difference to obtain the accurate position of the target; A weighted averaging module is used to perform weighted averaging according to the precise position of the target to obtain intermediate fusion data; The fusion module is used to perform Kalman filtering based on the intermediate fusion data to obtain multi-station radar fusion data.

9. An electronic device, characterized in that: The invention comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the radar point trace fusion method 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 includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the radar point trace fusion method according to any one of claims 1 to 7.

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