Association matching method, system and device based on patrol flight sensor data and medium

Abstract

The invention discloses an association matching method, system and device based on patrol flight sensor data and a medium, and the method comprises the following steps: synchronously collecting observation data, including arrival time information and arrival angle information, of a target through a plurality of patrol flight sensor sensors; processing the observation data; selecting a sensor association path according to the sensor type configuration; if the correlation path of the homologous sensor is selected, multi-target topological correlation between arrival angle homologous sensor projectiles is executed; if the correlation path of the heterogeneous sensor is selected, executing inter-missile multi-target topological correlation of the heterogeneous sensor; and based on an association matching result, estimating position coordinates of the targets by adopting an optimization algorithm, and generating an optimal association group and position information of the multiple targets. According to the method, the problems of high computing resource consumption, high communication load and low association success rate in multi-patrolling-missile cooperative target positioning are solved.

Description

Technical Field

[0001] This invention relates to the field of multi-UAV collaborative sensing and positioning technology, specifically to a method, system, device, and medium for association matching based on loitering sensor data. Background Technology

[0002] In multi-UAV collaborative operation scenarios such as low-altitude economic operations, emergency rescue, and industrial inspection, accurate positioning of dynamic targets (such as logistics drones, people trapped at disaster sites, and abnormal equipment in factories) is a core prerequisite for achieving mission closure. The correlation and matching of "sensor observation data - real target" is a crucial preliminary step in the positioning process—only when it is clear that the observation data from different UAV sensors correspond to the same target can subsequent positioning calculations output valid results. In the current multi-UAV collaborative positioning technology system, Time of Arrival (TOA) and Angle of Arrival (AOA) are two of the most basic and widely used observation technologies. Their technical principles, advantages, and limitations together constitute the core technical background for the development of hybrid target correlation methods, directly determining the design direction of the correlation strategy.

[0003] Global correlation is a problem that arises in scenarios where multiple missiles and their heterogeneous sensors observe multiple targets, requiring the simultaneous use of information observed by these multiple missiles for global target correlation. The main challenges of global correlation are as follows: First, simultaneous global correlation of multiple missiles consumes significant computational resources due to the massive amount of target and observation information to be processed. Second, the large amount of observation information that needs to be shared between missiles during global correlation places high demands on the communication quality and load capacity between them. Especially when the distance between missiles is large, the resulting spatial topological differences in the observation information can actually reduce the correlation success rate. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, device and medium for association matching based on loitering sensor data, so as to solve the problems of high computational resource consumption, high communication load and low association success rate in the cooperative target positioning of multiple loitering missiles.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A correlation matching method based on loitering sensor data includes the following steps: S1: Simultaneously collect target observation data through multiple loitering munition sensors. The observation data includes arrival time information collected by the TOA (Time of Arrival) sensor and arrival angle information collected by the AOA (Angle of Arrival) sensor. Perform outlier processing, noise filtering, and data standardization on the collected observation data. S2: Based on the sensor type configuration, select either the same-source sensor association path or the different-source sensor association path; if all the loitering munition's sensor types are AOA angle of arrival sensors, select the same-source sensor association path and perform multi-target topology association between loitering munitions with the same-source angle of arrival sensors; if the loitering munition's sensor types include a mixture of AOA angle of arrival sensors and TOA time of arrival sensors, select the different-source sensor association path and perform multi-target topology association between loitering munitions with different-source sensors. S3: Based on the association matching results, an optimization algorithm is used to estimate the target's position coordinates and generate the optimal association group and position information of multiple targets.

[0006] To optimize the above technical solution, the specific limitations also include: In step S2, the same-source sensor association path uses at least two AOA (Angle of Arrival) sensors; the different-source sensor association path uses at least two TOA (Time of Arrival) sensors and one AOA (Angle of Arrival) sensor.

[0007] Preferably, in step S2, the multi-target topology association between the same source sensors of the angle of arrival includes calculating the line-of-sight distance between any measurements of the AOA angle of arrival sensors and comparing it with a set critical distance value. When the line-of-sight distance is less than the critical distance value, the corresponding measurement is added to the association group set, and then the measurement group with the smallest line-of-sight distance is selected as the optimal association group from the association group set.

[0008] Furthermore, the line-of-sight distance is the minimum spatial distance between the rays observed by the two AOA arrival angle sensors, and the calculation formula is as follows:

[0009] in, and Let be the position coordinates and direction cosine of the first AOA reaching the angle sensor; and These are the position coordinates and direction cosines of the second AOA reaching the angle sensor.

[0010] Preferably, in step S2, the multi-target topological association between heterogeneous sensors includes determining the homogeneity of the TOA time of arrival sensor measurements and generating a spatial circular curve; calculating the minimum distance of the spatial circular curve observed by the AOA angle of arrival sensor as a heterogeneous association metric; setting a critical distance value and performing association filtering based on the heterogeneous association metric, and then outputting the optimal association group set.

[0011] Furthermore, the measurements of the TOA arrival time sensor are determined to have a common origin correlation using a common origin correlation determination factor:

[0012] in, , These are the position coordinates of the first TOA arriving at the time sensor and the position coordinates of the second TOA arriving at the time sensor, respectively. The first TOA sensor measured the same as the first... Distance to each target It is the second TOA sensor that measured the same as the first. The distance to each target; when When the value is 0, it is determined that there is no correlation between the TOA arrival time sensors. When the value is 1, it is determined that there may be a correlation between the TOA arrival time sensors, and there is a spatial circular curve.

[0013] Furthermore, the heterogeneous correlation metric is solved using a convex optimization problem:

[0014] in The minimum distance characterizing the spatial circular curve from the observation line of the AOA angle of arrival sensor to the TOA time of arrival sensor. The smaller the value, the greater the likelihood that the observations from the AOA angle of arrival sensor and the TOA time of arrival sensor originate from the same target; Represents the optimization variable. and These are the position coordinates and direction cosines of the AOA reaching the angle sensor.

[0015] This invention also proposes an association matching system based on loitering sensor data, comprising: The data acquisition and preprocessing module is used to synchronously acquire target observation data through multiple loitering munition sensors. The observation data includes arrival time information acquired by the TOA (Time of Arrival) sensor and arrival angle information acquired by the AOA (Angle of Arrival) sensor. The module performs outlier processing, noise filtering, and data standardization on the acquired observation data. The inter-missile multi-target topology association module is used to select either a same-source sensor association path or a different-source sensor association path based on the sensor type configuration. When a same-source sensor association path is selected, inter-missile multi-target topology association based on the same-source sensor is performed. When a different-source sensor association path is selected, inter-missile multi-target topology association based on the different-source sensor is performed. Specifically, if all the loitering munition's sensor types are AOA (Angle of Arrival) sensors, a same-source sensor association path is selected. If the loitering munition's sensor types include a mixture of AOA and TOA (Time of Arrival) sensors, a different-source sensor association path is selected. The position estimation and output module is used to estimate the position coordinates of the target based on the association matching results and to generate the optimal association group and position information of multiple targets using an optimization algorithm.

[0016] The present invention also proposes an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the association matching method based on the loitering sensor data as described above.

[0017] The present invention also proposes a computer-readable storage medium storing a computer program that causes a computer to execute the association matching method based on loitering sensor data as described above.

[0018] Compared with the prior art, the beneficial effects of the present invention are: This invention provides an association matching method based on loitering sensor data. By configuring the actual deployed sensor types, it dynamically selects association paths for similar or dissimilar sensors, leveraging the technical advantages of Time of Arrival (TOA) and Angle of Arrival (AOA) sensors to improve the overall performance of multi-target topology association. This solves the problem of insufficient applicability of traditional methods in complex scenarios, providing a flexible, efficient, and reliable solution for multi-loitering missile cooperative positioning systems. By selecting a small number of similar or dissimilar sensors with short distances for multi-target association through local association, it reduces the consumption of computing resources and the requirements for communication load capacity. Furthermore, the similar spatial topology of multi-missile observation information improves the success rate of association.

[0019] Furthermore, by using topological association between multiple targets from the same source sensor for angle of arrival (AOA), the angular resolution advantage of the AOA sensor is leveraged to improve target discrimination capability. The calculation formula for line-of-sight distance is optimized to improve computational efficiency and enhance numerical stability. Conversely, by using topological association between multiple targets from different source sensors, the distance measurement advantage of the TOA sensor is leveraged to effectively increase the detection range. Redundancy is provided through hybrid paths to improve system reliability. Additionally, a common-source association determination factor is introduced to reduce invalid calculations, quickly eliminate impossible association combinations, and improve system resource utilization. Finally, a convex optimization method is used to solve for the heterogeneous association metric, ensuring optimality of the solution and improving solution efficiency. Attached Figure Description

[0020] Figure 1 : A flowchart illustrating the association and matching method based on loitering sensor data of the present invention.

[0021] Figure 2 : A schematic diagram of the TOA (Time of Arrival) sensor positioning method based on the association matching method of the loitering sensor data of the present invention.

[0022] Figure 3 A schematic diagram of AOA (Angle of Arrival) sensor positioning based on the association matching method of the loitering sensor data of the present invention.

[0023] Figure 4 : A schematic diagram of the multi-target topology association process between projectiles with the same source sensor arrival angle based on the association matching method of the present invention based on loitering sensor data.

[0024] Figure 5 : A schematic diagram illustrating the heterogeneous topology association description of the association matching method based on loitering sensor data of the present invention.

[0025] Figure 6 : A schematic diagram of the multi-target topology association process between heterogeneous sensors based on the association matching method of loitering sensor data of the present invention. Detailed Implementation

[0026] The present invention will be further described in detail below through specific embodiments, but it should not be construed as limiting the scope of the subject matter of the present invention to the following embodiments. All technologies implemented based on the above content of the present invention fall within the scope of the present invention.

[0027] The following is an explanation of some of the terms used in this invention: Service chain (Segment Routing over IPv6 Service Function Chain, SRv6 SFC) is a technology that guides packets through application layer service devices sequentially along a specified path by adding SRv6 path information to the original packets.

[0028] Service Classifier (SC): Located at the edge of the SRV6 SFC service chain network, it is the source node of the service chain path. SC can use different traffic redirection methods to introduce service data into the SRV6 TE Policy tunnel for forwarding.

[0029] Loitering munition: A lightweight loitering unmanned aerial vehicle equipped with sensors.

[0030] Time of Arrival (TOA): A signal with known characteristics is emitted by a transmitter, propagates through space, and reaches the target. The receiver records the timestamp of the received signal and compares it with the signal using its local clock. The time difference between the signal's arrival at the receiver from the transmitter is measured as the time of arrival information. Based on the principle of constant signal propagation speed, the distance between the transmitter and receiver is calculated, thereby achieving target localization.

[0031] Angle of Arrival (AOA): A localization technique that determines the location of a signal source by measuring the azimuth angle of a wireless signal arriving at the receiver. It utilizes the phase difference or time difference generated when an antenna array receives a signal to calculate the angle of arrival, and then combines this with geometric methods such as triangulation to deduce the target's location.

[0032] The technical solution of the present invention will be further described in detail below with reference to specific embodiments: In one embodiment, this invention proposes an association matching method based on loitering sensor data, the flowchart of which is shown below. Figure 1 As shown, the entire method includes the following steps: S1: Simultaneously collect target observation data through multiple loitering munition sensors. The observation data includes arrival time information collected by the TOA (Time of Arrival) sensor and arrival angle information collected by the AOA (Angle of Arrival) sensor. Perform outlier processing, noise filtering, and data standardization on the collected observation data. S2: Based on the sensor type configuration, select either the same-source sensor association path or the different-source sensor association path; if all the loitering munition's sensor types are AOA angle of arrival sensors, select the same-source sensor association path and perform multi-target topology association between loitering munitions with the same-source angle of arrival sensors; if the loitering munition's sensor types include a mixture of AOA angle of arrival sensors and TOA time of arrival sensors, select the different-source sensor association path and perform multi-target topology association between loitering munitions with different-source sensors. S3: Based on the association matching results, an optimization algorithm is used to estimate the target's position coordinates and generate the optimal association group and position information of multiple targets.

[0033] The Time of Arrival (TOA) sensor converts the measured parameters into the distance required for positioning by measuring the time between the loitering munition and its arrival at the target object. Figure 2 As shown, the specific steps are as follows: Obtain the TOA arrival time sensor position coordinates , The range is from 1 to N, where N is the number of loitering munitions with a Time to Arrive (TOA); the distance between the loitering munition and the target object is... , ,in At the speed of light, The TOA (Time of Arrival) is the arrival time of the loitering munition; the location coordinates of the target object are... ; For each TOA (Time of Arrival) sensor, a spherical equation can be established to locate the target object:

[0034] The N position equations concerning the TOA arrival time sensor are expanded and expressed as matrices:

[0035] Where A is the coefficient matrix:

[0036] Position matrix for locating the target object:

[0037] b is a constant vector:

[0038] The least squares method is used to determine the target position of the object. .

[0039] The AOA (Angle of Arrival) sensor sets its origin near the loitering munition, ensuring that the ray it emits will pass through the target object. This method requires two or more AOA loitering munitions; the rays emitted by the munitions intersect at a single point, which is the location of the target object. Figure 3 As shown, the specific steps are as follows: Obtain the position coordinates of the AOA arrival angle sensor. , Take values ​​from 1 to N, where N is the number of AOA (Area of ​​Arrival) loitering munitions; Obtain the azimuth angle of each AOA sensor in world polar coordinates. and pitch angle And convert it into direction cosine: , , ; The position coordinates of the target object are: Then, the geometric relationship between the AOA arrival angle sensor and the target object can be obtained:

[0040] The N position equations concerning the AOA arrival angle sensor are expanded and expressed as matrices:

[0041] Wherein, the coefficient matrix G：

[0042] Position matrix for locating the target object:

[0043] H For constant vectors:

[0044] This problem is solved by using total least squares to find the coordinates of the spatial point closest to the two direction finding lines, and using these coordinates as the optimal target position estimate.

[0045] In step S1, the raw sensor data may contain outliers and missing values ​​due to equipment malfunctions, electromagnetic interference, environmental obstructions, etc. For outliers, data deviating from the mean by more than three standard deviations under a normal distribution are identified and removed. For missing values, if the missing percentage is less than 5%, interpolation of adjacent time points is used to supplement them; if the missing percentage is too high, the data set is discarded to avoid interference from invalid data in subsequent positioning calculations. The TOA (Time of Arrival) and AOA (Angle of Arrival) data acquired by multi-cruise aircraft sensors are susceptible to environmental noise and internal equipment noise. For TOA time data, a Kalman filter algorithm is used to suppress time measurement noise. Through an iterative prediction-update process, the time series data is smoothed, reducing the impact of random noise on time difference measurements. For AOA angle data, a moving average filter algorithm is used to eliminate angle fluctuations caused by instantaneous impulse noise, improving the stability of angle measurements. Multi-cruise aircraft may carry different types of sensors, resulting in inconsistent formats and units for the collected TOA (time of arrival) and AOA (time of altitude) data, making them unsuitable for direct collaborative computation. Data standardization steps include: unifying units by converting AOA angle data to radians and TOA time data to preliminary distance estimates; and unifying coordinate systems by converting observation data collected from local coordinate systems by each cruise aircraft into a unified world coordinate system using a coordinate transformation matrix, ensuring spatial consistency across multiple data sources. In step S2, if all loitering munition sensors are AOA angle of arrival sensors, a common-source sensor association path is selected, and multi-target topological association between common-source AOA sensors is performed. When representing and associating target point information using sensor observation information, at least four dimensions of observation information are required to accurately describe the three-dimensional position of the target and establish associations between targets. The information observed by the two AOA angle of arrival sensors happens to have four-dimensional features; therefore, these two AOA angle of arrival sensors are used as the smallest subset for associating AOA common-source observation information.

[0046] The multi-target topology association between projectiles from the same source sensor of angle of arrival (AOA) includes calculating the line-of-sight distance between any measurements of the AOA sensor and comparing it with a set critical distance value. When the line-of-sight distance is less than the critical distance value, the corresponding measurement is added to the association group set. Then, the measurement group with the smallest line-of-sight distance in the association group set is selected as the optimal association group.

[0047] Define the position coordinates of the two AOAs reaching the angle sensor as follows: and The two AOA arrival angle sensors measured the first The azimuth and elevation angles are denoted as follows: , and ,

[0048] Based on the position of the sensor and a set of azimuth angles it measures and pitch angle A straight line in three-dimensional space can be defined by the following equation:

[0049] Direction Cosine With azimuth and pitch angle The relationship is represented as: , , ; Similarly, for another loitering munition sensor, a set of azimuth angles measured by it and pitch angle and the location of the sensor This also determines that a direction cosine is a straight line in three-dimensional space.

[0050] The distance between two straight lines (the distance along the line of sight) is expressed as:

[0051] like Figure 4 As shown, let M1 be the number of measurements obtained by the first AOA arrival angle sensor and M2 be the number of measurements obtained by the second AOA arrival angle sensor. Initialize the optimal association set to store the final results; [The text then abruptly shifts to a different topic:] ...the number of measurements obtained by the first AOA arrival angle sensor... Group measurement with another sensor The line-of-sight distance between groups of measurements is compared to a critical distance value. If it is less than the critical distance value, then... join in The set of associated measurement groups Finally, select the set of associated measurement groups. The measurement group with the smallest critical distance value is taken as the optimal association of the measurement group. This process continues until all measurement groups of the first AOA arrival angle sensor have been traversed, and finally a set of optimal association groups for multiple ground targets is obtained.

[0052] If the loitering munition's sensor types include a mix of AOA (Angle of Arrival) and TOA (Time of Arrival) sensors, select the heterogeneous sensor association path and perform multi-target topology association between heterogeneous sensors. For example... Figure 5 As shown, since three-dimensional target point information and one-dimensional correlation information require at least four dimensions of observation information, the dimensional limitations of sensor observation information need to be considered when correlating local heterogeneous sensor loitering munitions. The observation information of the TOA time of arrival sensor is one-dimensional, and the observation information of the AOA angle of arrival sensor is two-dimensional. Therefore, it is necessary to ensure that there are at least two TOA time of arrival sensors and one AOA angle of arrival sensor.

[0053] Define the position coordinates of the first TOA arrival time sensor as . , and the first The distance between the targets is Therefore, the change determines a spherical surface in three-dimensional space, and the surface equation is expressed as:

[0054] Similarly, for the second TOA arrival time sensor, the position of the sensor... and the measured distance to the target This also determines a spherical surface in three-dimensional space. A homology measurement is performed on the TOA arrival time sensor measurements:

[0055] in, , These are the position coordinates of the first TOA arriving at the time sensor and the position coordinates of the second TOA arriving at the time sensor, respectively. The first TOA sensor measured the same as the first... Distance to each target It is the second TOA sensor that measured the same as the first. The distance to each target; when When the value is 0, it is determined that there is no correlation between the TOA arrival time sensors. When the value is 1, it is determined that there may be a correlation between the TOA arrival time sensors, and there is a spatial circular curve.

[0056] Intersecting spatial circular curves are represented as:

[0057] Furthermore, the position coordinates of the AOA reaching the angle sensor are defined as follows: Then, a straight line in three-dimensional space is determined by it and a set of measured azimuth and elevation angles:

[0058] in, The direction cosine of the AOA reaching the angle sensor.

[0059] The heterogeneous correlation metric is represented as the minimum value from a point on a circular curve in space to a straight line in three-dimensional space, and is expressed as the result of a conditional convex optimization problem:

[0060] Its constraints are:

[0061] in The minimum distance characterizing the spatial circular curve from the observation line of the AOA angle of arrival sensor to the TOA time of arrival sensor. The smaller the value, the greater the likelihood that the observations from the AOA angle of arrival sensor and the TOA time of arrival sensor originate from the same target; Represents the optimization variable. and These are the position coordinates and direction cosines of the AOA reaching the angle sensor.

[0062]

[0063] In addition, the homologous association metric is represented by the minimum heterologous association metric under the current three-dimensional spatial circle used for determination.

[0064] like Figure 6 As shown, for the first n The number of measurement targets obtained by the TOA arrival time sensors is M. Tn The number of measurement targets obtained by the AOA arrival angle sensor is M. A The correlation between any two measurements from two TOA arrival time sensors is calculated to determine their common origin. and spatial circles The main parameters are calculated, and the distance from the line of sight of all observed targets of the AOA arrival angle sensor to any spatial circle is also calculated. Minimum value of the upper point As a measure of heterogeneous association, a first critical distance value is set. DT D Secondly, the distance measured by the AOA arrival angle sensor is compared with the spatial circle. The minimum critical distance value is compared; if it is less than the first critical distance value, then the AOA is considered to have reached the first critical distance value of the angle sensor.i A Group measurements belong to heterogeneous possible association sets In heterogeneous possible association sets The smallest heterogeneous association metric is selected as the homogeneous association metric. Set the second critical distance value DT S If the homology metric is less than the second critical distance value, then the second TOA arrives at the i-th time sensor. T2 Group measurements belong to the homologous possible association set .

[0065] Finally, a set of homologous correlation measurement groups was selected. T i1 The measurement group with the smallest common-source correlation metric is taken as the optimal correlation of the three sensor measurement groups. This process continues until all measurements of the first TOA arrival time sensor have been traversed. At this point, the set of optimal correlation groups for ground multi-target sensors from different sources is obtained.

[0066] This invention also proposes an association matching system based on loitering sensor data, comprising: The data acquisition and preprocessing module is used to synchronously acquire target observation data through multiple loitering munition sensors. The observation data includes arrival time information acquired by the TOA (Time of Arrival) sensor and arrival angle information acquired by the AOA (Angle of Arrival) sensor. The module performs outlier processing, noise filtering, and data standardization on the acquired observation data. The inter-missile multi-target topology association module is used to select either a same-source sensor association path or a different-source sensor association path based on the sensor type configuration. When a same-source sensor association path is selected, inter-missile multi-target topology association based on the same-source sensor is performed. When a different-source sensor association path is selected, inter-missile multi-target topology association based on the different-source sensor is performed. Specifically, if all the loitering munition's sensor types are AOA (Angle of Arrival) sensors, a same-source sensor association path is selected. If the loitering munition's sensor types include a mixture of AOA and TOA (Time of Arrival) sensors, a different-source sensor association path is selected. The position estimation and output module is used to estimate the position coordinates of the target based on the association matching results and to generate the optimal association group and position information of multiple targets using an optimization algorithm.

[0067] The present invention also proposes an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the above-mentioned association matching based on the loitering sensor data.

[0068] The present invention also proposes a computer-readable storage medium storing a computer program that enables a computer to execute the above-described association matching method based on loitering sensor data.

[0069] In the embodiments disclosed in this application, a computer storage medium may be a tangible medium that may contain or store programs for use by or in conjunction with an instruction execution system, apparatus, or device. The computer storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of computer storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0070] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any simple modifications, equivalent substitutions, and improvements made by those skilled in the art to the above embodiments without departing from the scope of the technical solution of the present invention, based on the technical essence of the present invention, shall still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for association matching based on loitering sensor data, the method comprising: The method comprises the following steps: S1: synchronously collecting observation data of a target by multiple cruise missile sensors, the observation data comprising arrival time information collected by a TOA arrival time sensor and arrival angle information collected by an AOA arrival angle sensor; performing outlier processing, noise filtering and data standardization processing on the collected observation data; S2: selecting a homogenous sensor association path or a heterogeneous sensor association path according to sensor type configuration; if the cruise missile sensor types are all AOA arrival angle sensors, selecting the homogenous sensor association path and performing arrival angle homogenous sensor inter-missile multi-target topological association; if the cruise missile sensor types comprise a mixture of AOA arrival angle sensors and TOA arrival time sensors, selecting the heterogeneous sensor association path and performing heterogeneous sensor inter-missile multi-target topological association; S3: based on the association matching result, estimating the position coordinates of the target by using an optimization algorithm, and generating an optimal association group and position information of the multi-target.

2. The method of claim 1, wherein: In step S2, the homogenous sensor association path uses at least two AOA arrival angle sensors; and the heterogeneous sensor association path uses at least two TOA arrival time sensors and one AOA arrival angle sensor.

3. The method of claim 1, wherein: In step S2, the arrival angle homogenous sensor inter-missile multi-target topological association comprises calculating the boresight distance between any measurements of the AOA arrival angle sensors, and comparing the boresight distance with a set critical distance value; when the boresight distance is smaller than the critical distance value, the corresponding measurement is added to an association group set, and then the measurement group with the smallest boresight distance in the association group set is selected as the optimal association group.

4. The method of claim 3, wherein: The boresight distance is the minimum spatial distance between the observation rays of the two AOA arrival angle sensors, and the calculation formula is as follows: wherein, and are respectively the position coordinates and the direction cosine of the first AOA angle of arrival sensor; and are respectively the position coordinates and the direction cosine of the second AOA angle of arrival sensor.

5. The method of claim 1, wherein: In step S2, the heterogeneous sensor inter-missile multi-target topological association comprises performing homogenous association determination on the measurements of the TOA arrival time sensors to generate a spatial circle curve; calculating the minimum distance from the spatial circle curve observed by the AOA arrival angle sensor as a heterogeneous association metric; setting a critical distance value, and performing association screening based on the heterogeneous association metric, and then outputting an optimal association group set.

6. The method of claim 5, wherein: The homogenous association determination on the measurements of the TOA arrival time sensors is determined by a homogenous association determination factor: in, , These are the position coordinates of the first TOA arriving at the time sensor and the position coordinates of the second TOA arriving at the time sensor, respectively. The first TOA sensor measured the same as the first... Distance to each target It is the second TOA sensor that measured the same as the first. The distance to each target; when When the value is 0, it is determined that there is no correlation between the TOA arrival time sensors. When the value is 1, it is determined that there may be a correlation between the TOA arrival time sensors, and there is a spatial circular curve.

7. The method of claim 6, wherein: The heterogeneous association metric is solved by a convex optimization problem: wherein characterizing the minimum distance of the AOA angle of arrival sensor observation straight line to the TOA time of arrival sensor spatial circle curve, The smaller the more likely that the AOA angle of arrival sensor and the TOA time of arrival sensor observed the same target; representing the optimization variables, and are the position coordinates and direction cosines of the AOA angle of arrival sensor, respectively.

8. A correlation matching system based on loitering sensor data, characterized by, The method comprises: a data collection and preprocessing module, configured to synchronously collect observation data of a target by multiple cruise missile sensors, the observation data comprising arrival time information collected by a TOA arrival time sensor and arrival angle information collected by an AOA arrival angle sensor; and perform outlier processing, noise filtering and data standardization processing on the collected observation data; The inter-missile multi-target topology correlation module is configured to select a homogenous sensor correlation path or a heterogeneous sensor correlation path according to a sensor type configuration; when the homogenous sensor correlation path is selected, an angle-of-arrival homogenous sensor inter-missile multi-target topology correlation is performed; when the heterogeneous sensor correlation path is selected, a heterogeneous sensor inter-missile multi-target topology correlation is performed; wherein, if the sensor types of the loitering missiles are all angle-of-arrival (AOA) sensors, the homogenous sensor correlation path is selected; if the sensor types of the loitering missiles include a mixed type of AOA sensors and time-of-arrival (TOA) sensors, the heterogeneous sensor correlation path is selected; The position estimation and output module is configured to estimate position coordinates of the targets based on the correlation matching result, and to generate an optimal correlation group and position information of the multi-targets by using an optimization algorithm.

9. An electronic device, comprising: The computer program product comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the correlation matching method based on the loitering sensor data according to any one of claims 1-7. The computer program product enables a computer to execute the correlation matching method based on the loitering sensor data according to any one of claims 1-7.

10. A computer readable storage medium having stored thereon a computer program, characterized in that: ​

Images