Optical frequency domain multi-sensor track association method and system
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CENT CHINA OPTOELECTRONICS TECH RES INST (CHINA STATE SHIPBUILDING CORP 717TH RES INST)
- Filing Date
- 2022-12-12
- Publication Date
- 2026-07-21
Smart Images
Figure CN115855025B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of optoelectronic information processing technology, and in particular to a method and system for correlation of multiple sensor tracks in the optical frequency domain. Background Technology
[0002] In the field of optical frequency domain multi-target tracking, facing a large number of targets, dense distribution, and complex interference phenomena, the tracking performance requirements of sensors are becoming increasingly demanding. Therefore, information obtained from a single sensor alone is no longer sufficient to meet practical needs. To improve the tracking performance of the entire system, multi-sensor information fusion technology has rapidly developed.
[0003] Multi-sensor information fusion technology fully utilizes the information resources of each sensor, leveraging the performance advantages of each sensor through information complementarity. By correlating and fusing information from multiple sensors at multiple levels, more accurate trajectory information is obtained, thereby improving system reliability. This enables multi-sensor systems to operate more efficiently and stably. By obtaining target measurement information from different sensors and processing it through multiple levels such as detection, registration, correlation, estimation, and evaluation, relatively accurate target status tracking information is obtained. Optimal assessment of the future environment and situation is then performed to achieve the best combat effect.
[0004] In distributed optical frequency domain multi-sensor systems, forming a more accurate system track by combining local tracks obtained from different sensors tracking the same target can improve the stability and robustness of the entire information fusion system. Since track association directly affects the quality of subsequent track fusion, and good association is fundamental to track tracking and recognition, research on track association is of great significance and application value. Summary of the Invention
[0005] The main objective of this invention is to propose a method for multi-sensor track association in the optical frequency domain. This method utilizes target state information from multiple sensors to associate target points in different sensors, then performs combination matching, transforming it into a minimum value optimization problem in a mathematical model. Finally, it uses a genetic optimization algorithm to solve the optical frequency domain multi-sensor track association problem, thereby achieving multi-target track association in the optical frequency domain.
[0006] The technical solution adopted in this invention is:
[0007] A method for correlation of multiple sensors in the optical frequency domain is provided, including the following steps:
[0008] S1. Construct a global coordinate system: Construct a global coordinate system and determine... The coordinates of each sensor relative to the origin of the global coordinate system ,as well as The position coordinates of the target in the global coordinate system ;
[0009] S2. Measuring Pitch and Azimuth Angles: Multiple target points are measured using multiple sensors. The measured data is time-aligned, and the pitch and azimuth angles of each target relative to each sensor are calculated using the following formula:
[0010] (1)
[0011] in, Let be the pitch angle of the j-th target relative to the measured position of the i-th sensor. Let be the position angle of the j-th target relative to the azimuth angle measured by the i-th sensor. , Let be the coordinates of the i-th sensor in the global coordinate system. Let J be the coordinates of the j-th target in the global coordinate system. This is noise error;
[0012] S3. Estimate pitch and azimuth angles: Within the effective range of the sensor, randomly generate n sets of target coordinate positions. The n sets of target data are encoded, and the estimated pitch angle and estimated azimuth angle of each target relative to each sensor in the n sets of target data are calculated using the following formula:
[0013] (2)
[0014] in, For predicting the target Relative to the estimated pitch angle of the i-th sensor, For predicting the target Relative to the estimated azimuth angle of the i-th sensor, This represents the estimated coordinates of the j-th target in the global coordinate system.
[0015] S4. Estimate the actual position of the target: Construct a fitness function based on the estimated and measured pitch and azimuth angles. The formula is
[0016] (3)
[0017] Then, a new set of n data is obtained through a genetic algorithm. Steps S3 and S4 are repeated. After iterative optimization, the estimated actual positions of n targets are obtained.
[0018] S5. Coordinate Interconnection to Obtain Target Track: Unify the estimated actual positions of n targets into the global coordinate system, perform spatial alignment, repeat steps S2 to S4 to obtain the estimated actual positions of n targets at the next moment, connect the position coordinates of the same target at different times to obtain the target's track information.
[0019] Following the above technical solution, the global coordinate system in step S1 is either the world coordinate system or the WGS84 coordinate system.
[0020] Following the above technical solution, step S2, which involves aligning the measured data in time, means extrapolating the points of each track to the same moment to obtain the pitch and azimuth angle arrays.
[0021] Following the above technical solution, the noise error in step S2 follows a mean of . The standard deviation is Gaussian distribution error.
[0022] Following the above technical solution, encoding the n sets of target data in step S3 refers to encoding the n sets of data into binary or decimal.
[0023] Following the above technical solution, obtaining new n sets of data through genetic algorithm in step S4 refers to obtaining new n sets of data through mutation operator, crossover operator and recombination operator.
[0024] Following the above technical solution, completing the iterative optimization in step S4 means stopping the iteration when the number of iterations reaches the set maximum number of iterations or the fitness function is less than the set threshold.
[0025] The present invention also provides an optical frequency domain multi-sensor trajectory correlation system, comprising:
[0026] The global coordinate system construction module is used to construct and define the global coordinate system. The coordinates of each sensor relative to the origin of the global coordinate system ,as well as The position coordinates of the target in the global coordinate system ;
[0027] The elevation and azimuth measurement module is used to measure multiple target points using multiple sensors, align the measured data in time, and calculate the measured elevation and azimuth angles of each target relative to each sensor. The calculation formula is as follows:
[0028] (1)
[0029] in, Let be the pitch angle of the j-th target relative to the measured position of the i-th sensor. Let be the azimuth angle of the j-th target relative to the measurement of the i-th sensor. , Let be the coordinates of the i-th sensor in the global coordinate system. Let J be the coordinates of the j-th target in the global coordinate system. This is noise error;
[0030] The elevation and azimuth estimation modules are used to randomly generate n sets of target coordinate positions within the effective range of the sensor. The n sets of target data are encoded, and the estimated pitch angle and estimated pitch angle of each target relative to each sensor in the n sets of data are calculated using the following formula:
[0031] (2)
[0032] in, For predicting the target Relative to the estimated pitch angle of the i-th sensor, Let be the estimated coordinates of the j-th target.
[0033] The target actual position estimation module is used to construct a fitness function based on the estimated and measured pitch and azimuth angles. The formula is
[0034]
[0035] Then, a new set of n data is obtained through a genetic algorithm. Steps S3 and S4 are repeated. After iterative optimization, the estimated actual positions of n targets are obtained.
[0036] The target trajectory acquisition module is used to unify the estimated actual positions of n targets into the global coordinate system, perform spatial alignment, repeat steps S2 to S4 to obtain the estimated actual positions of n targets at the next moment, and connect the position coordinates of the same target at different times to obtain the target's trajectory information.
[0037] Following the above technical solution, the target actual location estimation module specifically obtains new n sets of data through genetic algorithm, which means obtaining new n sets of data through mutation operator, crossover operator and recombination operator.
[0038] The present invention also provides a computer storage medium storing a computer program executable by a processor, the computer program executing the optical frequency domain multi-sensor track association method described in the above technical solution.
[0039] The beneficial effects of this invention are as follows: By constructing a global coordinate system and utilizing multiple sensors to measure multiple target points, this invention obtains the measured pitch and azimuth angles of each target relative to each sensor. It then estimates the pitch and azimuth angles of each target relative to each sensor, constructs an adaptive function based on the measured and estimated pitch angles, and uses a genetic algorithm for iterative optimization to obtain the optimal trajectory information. This invention transforms the trajectory association problem into a mathematical minimum optimization problem. By using trajectories obtained from different sensors tracking the same target, it forms a more accurate system trajectory, improving the stability and robustness of the entire information fusion system, solving the problem of data explosion in multi-target trajectory association, and further improving operational efficiency. Attached Figure Description
[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0041] Figure 1 This is a flowchart of the optical frequency domain multi-sensor track association method according to an embodiment of the present invention;
[0042] Figure 2 This is a schematic diagram of the multi-sensor trajectory measurement of the present invention.
[0043] Figure 3 This is a schematic diagram of the structure of the optical frequency domain multi-sensor track association system according to an embodiment of the present invention. Detailed Implementation
[0044] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.
[0045] This invention provides a method for correlation of multiple sensor tracks in the optical frequency domain, such as... Figure 1 As shown, it includes the following steps:
[0046] S1. Construct a global coordinate system: Construct a global coordinate system and determine... The coordinates of each sensor relative to the origin of the global coordinate system ,as well as The position coordinates of the target in the global coordinate system ;
[0047] S2. Measuring Pitch and Azimuth Angles: Multiple target points are measured using multiple sensors. The measured data is time-aligned, and the measured pitch and azimuth angles of each target relative to each sensor are calculated using the following formula:
[0048] (1)
[0049] in, Let be the pitch angle of the j-th target relative to the measured position of the i-th sensor. Let be the position angle of the j-th target relative to the azimuth angle measured by the i-th sensor. , Let be the coordinates of the i-th sensor in the global coordinate system. Let J be the coordinates of the j-th target in the global coordinate system. This is noise error;
[0050] S3. Estimate pitch and azimuth angles: Within the effective range of the sensor, randomly generate n sets of target coordinate positions. The n sets of target data are encoded, and the estimated pitch angle and estimated azimuth angle of each target relative to each sensor in the n sets of data are calculated using the following formula:
[0051] (2)
[0052] in, For predicting the target Relative to the estimated pitch angle of the i-th sensor, For predicting the target Relative to the estimated azimuth angle of the i-th sensor, This represents the estimated coordinates of the j-th target in the global coordinate system.
[0053] S4. Estimate the actual position of the target: Construct a fitness function based on the estimated and measured pitch and azimuth angles. The formula is
[0054] (3)
[0055] Then, a new set of n data is obtained through a genetic algorithm. Steps S3 and S4 are repeated. After iterative optimization, the estimated actual positions of n targets are obtained.
[0056] S5. Coordinate Interconnection to Obtain Target Track: Unify the estimated actual positions of n targets into the global coordinate system, perform spatial alignment, repeat steps S2 to S4 to obtain the estimated actual positions of n targets at the next moment, connect the position coordinates of the same target at different times to obtain the target's track information.
[0057] The method of this invention transforms the trajectory association problem into a mathematical minimum optimization problem. Based on trajectories obtained from different sensors tracking the same target, a more accurate system trajectory is formed, improving the stability and robustness of the entire information fusion system. Compared to other traditional trajectory association algorithms, this method can solve the problem of multi-target trajectory association data explosion. It relies on GPUs or multi-core processors for parallel computation, further improving operational efficiency and obtaining real-time multi-target trajectory association information.
[0058] When detecting target tracks using multiple sensors in the optical frequency domain, track association is necessary due to the overlap in the spatiotemporal coverage areas of the sensors. Current methods for multi-sensor track association include weighted statistical distance testing, modified weighted statistical distance testing, independent sequential methods, correlated sequential methods, and nearest neighbor methods. However, while these methods work well with a limited number of sensors and targets, they become ineffective with multiple sensors and multiple targets. As the number of sensors and targets increases, the computational complexity of track association and combination increases exponentially.
[0059] This method fully utilizes target state information from multiple sensors, associates target points from different sensors, and then performs combined matching, transforming it into a minimum optimization problem in a mathematical model. It proposes using a genetic optimization algorithm to solve the optical frequency domain multi-sensor track association problem. Its theoretical basis is specifically reflected in:
[0060] Based on formula (1), in an ideal situation, the noise error A value of zero indicates that the actual coordinates match the measured coordinates. Theoretically, this noise error follows a mean of [value missing]. The standard deviation is The Gaussian distribution, i.e.
[0061] (4)
[0062] in, If the elevation or azimuth angle of target T relative to sensor S is ignored, then m sensors observe the same target. The maximum likelihood estimate is:
[0063] (5)
[0064] Taking the logarithm of both sides of equation (5) maximizes the maximum likelihood estimate, which means minimizing the difference between the estimated position of the target observed by multiple sensors and the corresponding actual position.
[0065] (6)
[0066] The objective function that can be minimized is:
[0067] (7)
[0068] Its constraints can be expressed as actual constraints, which is the actual effective range of the sensor.
[0069] Therefore, the trajectory association problem is transformed into a mathematical minimum optimization problem, and the genetic algorithm can be used to find the global optimal solution and obtain the optimal trajectory information.
[0070] In a preferred embodiment, the global coordinate system in step S1 is either the world coordinate system or the WGS84 coordinate system.
[0071] In a preferred embodiment, the time alignment of the measured data in step S2 refers to extrapolating the points of each track to the same moment to obtain the pitch angle and azimuth angle array.
[0072] In a preferred embodiment, the noise error in step S2 follows a mean of . The standard deviation is Gaussian distribution error.
[0073] In a preferred embodiment, encoding the n sets of target data in step S3 means encoding the n sets of data in binary or decimal.
[0074] In a preferred embodiment, obtaining new n sets of data through a genetic algorithm in step S4 refers to obtaining new n sets of data through mutation operators, crossover operators, and recombination operators.
[0075] In a preferred embodiment, completing the iterative optimization in step S4 means stopping the iteration when the number of iterations reaches the set maximum number of iterations or the fitness function is less than the set threshold.
[0076] The optical frequency domain multi-sensor track correlation system of this invention is mainly used to implement the above-described method embodiments, such as... Figure 3 As shown, the system includes:
[0077] The global coordinate system construction module is used to construct and define the global coordinate system. The coordinates of each sensor relative to the origin of the global coordinate system ,as well as The position coordinates of the target in the global coordinate system ;
[0078] The elevation and azimuth measurement module is used to measure multiple target points using multiple sensors, align the measured data in time, and calculate the measured elevation and azimuth angles of each target relative to each sensor. The calculation formula is as follows:
[0079] (1)
[0080] in, Let be the pitch angle of the j-th target relative to the measured position of the i-th sensor. Let be the azimuth angle of the j-th target relative to the measurement of the i-th sensor. , Let be the coordinates of the i-th sensor in the global coordinate system. Let J be the coordinates of the j-th target in the global coordinate system. This is noise error;
[0081] The elevation and azimuth estimation modules are used to randomly generate n sets of target coordinate positions within the effective range of the sensor. The n sets of target data are encoded, and the estimated pitch and azimuth angles of each target relative to each sensor in the n sets of data are calculated using the following formula:
[0082] (2)
[0083] in, For predicting the target Relative to the estimated pitch angle of the i-th sensor, Let be the estimated coordinates of the j-th target.
[0084] The target actual position estimation module is used to construct a fitness function based on the estimated and measured pitch and azimuth angles. The formula is
[0085]
[0086] Then, a new set of n data is obtained through a genetic algorithm. Steps S3 and S4 are repeated. After iterative optimization, the estimated actual positions of n targets are obtained.
[0087] The target trajectory acquisition module is used to unify the estimated actual positions of n targets into the global coordinate system, perform spatial alignment, repeat steps S2 to S4 to obtain the estimated actual positions of n targets at the next moment, and connect the position coordinates of the same target at different times to obtain the target's trajectory information.
[0088] In the preferred embodiment of the system, each module is specifically used to implement the above preferred method embodiment, which will not be elaborated here.
[0089] This application also provides a non-transitory computer-readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, server, App application store, etc., which stores a computer program, and the program implements corresponding functions when executed by a processor. The computer-readable storage medium of this embodiment is used to implement the optical frequency domain multi-sensor track association method of the method embodiment when executed by a processor.
[0090] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.
Claims
1. A method for correlation of multiple sensor tracks in the optical frequency domain, characterized in that, Includes the following steps: S1. Construct a global coordinate system: Construct a global coordinate system and determine... The coordinates of each sensor relative to the origin of the global coordinate system ,as well as The position coordinates of the target in the global coordinate system ; S2. Measuring Pitch and Azimuth Angles: Multiple target points are measured using multiple sensors. The measured data is time-aligned, and the pitch and azimuth angles of each target relative to each sensor are calculated using the following formula: (1) in, Let be the pitch angle of the j-th target relative to the measured position of the i-th sensor. Let be the position angle of the j-th target relative to the azimuth angle measured by the i-th sensor. , Let be the coordinates of the i-th sensor in the global coordinate system. Let J be the coordinates of the j-th target in the global coordinate system. This is noise error; S3. Estimating pitch and azimuth angles: Within the effective range of the sensor, randomly generate n sets of target coordinate positions. The n sets of target data are encoded, and the estimated pitch angle and estimated azimuth angle of each target relative to each sensor in the n sets of target data are calculated using the following formula: （2） in, For predicting the target Relative to the estimated pitch angle of the i-th sensor, For predicting the target Relative to the estimated azimuth angle of the i-th sensor, Let be the estimated coordinates of the j-th target in the global coordinate system; S4. Estimate the actual position of the target: Construct a fitness function based on the estimated and measured pitch and azimuth angles. The formula is （3） Then, a new set of n target data is obtained through a genetic algorithm. Steps S3 and S4 are repeated. After iterative optimization, the actual position estimates of n targets are obtained. S5. Coordinate Interconnection to Obtain Target Track: Unify the estimated actual positions of n targets into the global coordinate system, perform spatial alignment, repeat steps S2 to S4 to obtain the estimated actual positions of n targets at the next moment, connect the position coordinates of the same target at different times to obtain the target's track information.
2. The optical frequency domain multi-sensor track association method according to claim 1, characterized in that, The global coordinate system in step S1 is either the world coordinate system or the WGS84 coordinate system.
3. The optical frequency domain multi-sensor track association method according to claim 1, characterized in that, The time alignment of the measured data in step S2 refers to extrapolating the points of each track to the same moment to obtain the pitch angle and azimuth angle array.
4. The optical frequency domain multi-sensor track association method according to claim 1, characterized in that, The noise error in step S2 follows a mean of The standard deviation is Gaussian distribution error.
5. The optical frequency domain multi-sensor track association method according to claim 1, characterized in that, Encoding the n sets of target data in step S3 refers to encoding the n sets of target data into binary or decimal.
6. The optical frequency domain multi-sensor track association method according to claim 1, characterized in that, The step S4, obtaining new n sets of target data through genetic algorithm, refers to obtaining new n sets of target data through mutation operator, crossover operator, and recombination operator.
7. The optical frequency domain multi-sensor track association method according to claim 1, characterized in that, Step S4, completing the iterative optimization, means stopping the iteration when the number of iterations reaches the set maximum number of iterations, or when the fitness function is less than the set threshold.
8. A multi-sensor trajectory correlation system in the optical frequency domain, characterized in that, include: The global coordinate system construction module is used to construct and define the global coordinate system. The coordinates of each sensor relative to the origin of the global coordinate system ,as well as The position coordinates of the target in the global coordinate system ; The module for measuring pitch and azimuth angles is used to measure multiple target points using multiple sensors, align the measured data in time, and calculate the measured pitch and azimuth angles of each target relative to each sensor. The calculation formula is as follows: （1） in, Let be the pitch angle of the j-th target relative to the measured position of the i-th sensor. Let be the azimuth angle of the j-th target relative to the measurement of the i-th sensor. , Let be the coordinates of the i-th sensor in the global coordinate system. Let J be the coordinates of the j-th target in the global coordinate system. This is noise error; The elevation and azimuth estimation modules are used to randomly generate n sets of target coordinate positions within the effective range of the sensor. The n sets of target data are encoded, and the estimated pitch angle and estimated azimuth angle of each target relative to each sensor in the n sets of target data are calculated using the following formula: （2） in, For predicting the target Relative to the estimated pitch angle of the i-th sensor, Let be the estimated coordinates of the j-th target. The target actual position estimation module is used to construct a fitness function based on the estimated and measured pitch and azimuth angles. The formula is Then, a new set of n sets of target data is obtained through a genetic algorithm. The calculation of the pitch and azimuth angle estimation module and the target actual position estimation module is repeatedly executed. After iterative optimization, the actual position estimation values of n targets are obtained. The target trajectory acquisition module is used to unify the estimated actual positions of n targets into the global coordinate system, perform spatial alignment, and repeatedly execute the calculations of the measurement of pitch and azimuth angles module, the estimation of pitch and azimuth angles module, and the target actual position estimation module to obtain the estimated actual positions of n targets at the next moment. The position coordinates of the same target at different times are connected to obtain the target's trajectory information.
9. The optical frequency domain multi-sensor trajectory correlation system according to claim 8, characterized in that, The target actual location estimation module specifically obtains new n sets of target data through genetic algorithms, which means obtaining new n sets of target data through mutation operators, crossover operators, and recombination operators.
10. A computer storage medium, characterized in that, It contains a computer program that can be executed by a processor, which performs the optical frequency domain multi-sensor track association method according to any one of claims 1-7.