Battlefield Situation Consistency Processing System

Abstract

The battlefield situation consistency processing system relates to the field of aircraft situation consistency processing. The present invention is to solve the problem that the existing situation consistency processing method still has a low accuracy in analyzing the battlefield situation due to information loss. The present invention includes: a situation information acquisition module: fusing the target tracking result information to obtain the track situation information of the fusion node; the situation information processing module includes: a track association unit, a track status value unification unit, and a track label unification unit; the track association unit: associating multiple tracks obtained at the current moment with the tracks at the previous moment t, and changing the track labels of the successfully associated tracks to be the same as those at the previous moment t; the track status value unification unit: unifying the track status values between different fusion nodes of the associated tracks; the track label unification unit: unifying the labels between different fusion nodes and correctly corresponding them to the unified track status values. The present invention is used for consistency processing of battlefield situation information.

Description

Technical Field

[0001] The present invention relates to the field of aircraft situation consistency processing, and particularly to a battlefield situation consistency processing system. Background Art

[0002] In modern battlefields, it is often necessary for multiple combat units to conduct coordinated operations to complete the same combat mission. This requires that the relevant battlefield situation information faced and relied on by each combat unit in the coordinated plan and coordinated actions for the same combat mission must be consistent, that is, to maintain the consistency of the battlefield situation of common concern. This is the key to achieving consistent coordinated plans and synchronized coordinated actions.

[0003] The consistency of the battlefield situation can be divided into broad and narrow concepts. The broad sense of situation consistency refers to that the battlefield situation information obtained by each combat unit is consistent with the corresponding real battlefield state or the error is within the allowable range, which is the situation consistency in the absolute sense; while the narrow sense of situation consistency refers to that the situation information of the jointly concerned combat area obtained by different combat units is consistent or the error is within the allowable range, which is the situation consistency in the relative sense. In the actual complex and changeable battlefield environment, considering that the real battlefield situation is often unknown or difficult to obtain, it is often difficult to measure the broad sense of situation consistency. Therefore, the narrow concept of battlefield situation consistency, that is, the relative situation consistency, has more important research significance and application value. The specific manifestation forms of relative situation consistency mainly include the following two aspects: First, whether the motion states are consistent: whether the speeds, headings, and positions of the dynamic targets in the jointly concerned combat area among the situations of different combat units are consistent; Second, whether the identifications are consistent: whether the target identifications of the dynamic targets in the jointly concerned combat area among the situations of different combat units are consistent.

[0004] Currently, the consistency processing technology based on target tracking and fusion mainly aims at the state value differences and label errors caused by measurement information differences, tracking errors, etc., and maintains the consistency and accuracy of the situation information transmitted to each fusion node through consistency processing. However, the existing technology does not consider the problems of target track loss and interruption caused by communication and other issues, as well as the problem of label loss, especially the situation where there is no situation information of a certain track that should exist in all fusion nodes. This leads to the situation that although the obtained situation information can maintain consistency, there will be problems of information loss, thus resulting in a low accuracy rate of battlefield situation analysis. Summary of the Invention

[0005] The purpose of the present invention is to solve the problem that the existing situation consistency processing method still has a low accuracy rate of battlefield situation analysis due to information loss when the target track is missing or interrupted, and a battlefield situation consistency processing system is proposed.

[0006] Battlefield situation consistency processing system, including: situation information acquisition module, situation information processing module;

[0007] The situation information acquisition module is used to fuse the multi-sensor target tracking result information to obtain the track situation information of each fusion node; wherein, each fusion node is a combat unit in the battlefield;

[0008] Each fusion node has a track library, which includes: local track, remote track, public track;

[0009] The track situation information includes: track status value, track label;

[0010] The track status value includes: spatial position coordinates, speed value;

[0011] The situation information processing module includes: track association unit, track status value unification unit, track label unification unit;

[0012] The track association unit is used to associate the multiple tracks obtained at the current moment with the tracks at the previous moment t respectively, and change the track labels of the successfully associated tracks to be consistent with the previous moment t;

[0013] The track status value unification unit is used to unify the track status values between different fusion nodes of the associated tracks;

[0014] The track label unification unit is used to unify the labels between different fusion nodes and correctly correspond to the unified track status values, and execute simultaneously with the track status value unification unit.

[0015] Further, the situation information acquisition module is used to fuse the multi-sensor target tracking result information to obtain the track situation information of each fusion node, which is implemented by using the GCI fusion criterion.

[0016] Further, the track association unit is used to associate the multiple tracks obtained at the current moment with the tracks at the previous moment t respectively, and change the track labels of the successfully associated tracks to be consistent with the previous moment t, which is implemented by using the local optimal track association algorithm.

[0017] Further, the local optimal track association algorithm includes the following steps:

[0018] Step 1: Obtain the number of tracks m and n in two groups of tracks to be associated;

[0019] Step 2: Obtain the Euclidean distance d between every two tracks i,j :

[0020]

[0021] wherein, xi , y i , z i and x j , y j , z j respectively represent the status values of two tracks, namely the corresponding x, y, and z axis coordinates, d i,j represents the Euclidean distance between these two tracks, and i and j are the two tracks respectively;

[0022] Step 3: According to d obtained in Step 2 i,j Obtain the m*n track association matrix D:

[0023]

[0024] Step 4: Set the track association threshold M, compare the minimum Euclidean distance value of each row in the track association matrix D with the track association threshold M. If the minimum Euclidean distance value of each row in D is less than or equal to M, the association is successful; if the minimum Euclidean distance value of each row in D is greater than M, the association fails;

[0025] Step 5: Change the track label of the current moment with successful association to be the same as the corresponding track label of the previous moment, and use the track with failed association as a new track to re-execute Step 1.

[0026] Furthermore, for setting the track association threshold M in Step 4, comparing the minimum Euclidean distance value of each row in the track association matrix D with the track association threshold M. If the minimum Euclidean distance value of each row in D is less than or equal to M, the association is successful; if the minimum Euclidean distance value of each row in D is greater than M, the association fails, as shown in the following formula:

[0027]

[0028] Furthermore, the track status value unification unit is used to unify the track status values between different fusion nodes of the associated tracks, including the following steps:

[0029] S1: Obtain the moment when the triggering event occurs:

[0030] Among them, the moment when the number of tracks changes is the moment when the triggering event occurs;

[0031] S2: Sequentially identify whether there is a triggering event within a fixed time step. If a triggering event occurs at a certain fusion node within the time step l, execute S3. At the same time, for the moments when no triggering event occurs within the time step l, output the processing result of the track association unit; if no triggering event occurs at any fusion node within the time step l, directly output the processing result of the track association unit;

[0032] S3. Define the fusion node with the largest number of targets at the moment when the trigger event occurs as the reference node. Use the local optimal track association algorithm to associate the reference node with other fusion nodes respectively. For the successfully associated tracks, form a common track by the weighted average method, correspond the track label to the label of the reference node, and send the state value and label of the formed common track to each fusion node. If there are tracks that fail to be associated successfully, it indicates that there is a lack of target tracks in one or some fusion nodes. Supplement the state value and label of the unassociated tracks into the track library of the fusion node where the corresponding track is missing, and obtain the consistent fusion node situation information at the current moment;

[0033] S4. Obtain the type of the trigger event:

[0034] Fit the motion model of the tracks at the previous moment to obtain the fitting value at the current moment, and associate the fitting value with the tracks at the current moment;

[0035] If two tracks are successfully associated, it means that these two tracks represent the same target, and unify the track label with the previous moment;

[0036] If a certain track h fails to be associated successfully with other tracks, judge whether track h comes from the previous moment or the current moment. If it comes from the previous moment, it means that a track termination trigger event occurs for track h; if it comes from the current moment, it means that a track start trigger event occurs for track h, and record the time when the event occurs and the corresponding fusion node;

[0037] S5. Process the tracks of the fusion node where the trigger event occurs according to the type of the trigger event obtained in S4, and obtain the unified track state value.

[0038] Further, the process of processing the tracks of the fusion node where the trigger event occurs according to the type of the trigger event obtained in S4 to obtain the unified track state value includes the following steps:

[0039] S501. If the trigger event that occurs is track start, judge whether there is track termination before the moment when the track start trigger event occurs. If there is track termination, perform track association on the starting track and the terminating track. If the track association is successful, it means that a track interruption occurs during this period, then supplement the interrupted track and obtain the unified track state value. If the track association fails or there is no track termination, it means that the track is newly started, and use the newly started track to obtain the unified track state value;

[0040] S502. If the trigger event that occurs is track termination, it indicates that there is no target track after this trigger moment. Then, use the local optimal track association algorithm to determine whether there is a track of the corresponding target in other fusion nodes. If there is, supplement the remote track information to the local track library. If no fusion node has a track of the corresponding target, it is regarded as track termination, and record the moment of track termination for the judgment of track interruption.

[0041] Further, if the track is not successfully associated or there is no track termination in S501, it indicates that this track is a newly started track. Then, use the newly started track to obtain the unified track state value. Specifically:

[0042] Store the new track as a local track in the track library, and at the same time, determine whether there is only one fusion node where this new track appears at the arrival moment of the new track; if there is only one fusion node where this new track appears, supplement this new track to the track libraries of other fusion nodes to obtain the unified track state value; if there is more than one fusion node where this new track appears, then perform weighted processing on this new track to form a common track, and send this common track to each fusion node to obtain the unified track state value.

[0043] Further, if the track is successfully associated, it indicates that a track interruption has occurred during this period. Then, complete the interrupted track and obtain the unified track state value specifically as follows:

[0044] Check whether a track interruption also occurs in other fusion nodes at the track interruption moment a. If no interruption occurs, assign the track without interruption to the track with track interruption to obtain the unified track state value; if there is a track interruption of a certain track in all fusion nodes at time a, then use the backtracking method to compensate for the missing track to obtain the unified track state value.

[0045] Further, the backtracking method is implemented using the following model:

[0046]

[0047] Among them, (x’, y’, z’) represents the coordinates in the motion model, and the one with subscript 0 represents the coordinates of the initial position of the model, v x' , v y' , v z' represents the velocity values in three directions, and T represents the model process time;

[0048] Perform backtracking on the target according to the above target motion model. The state of the target at time k can be expressed as:

[0049] X(k) = F · X(k - 1) (5)

[0050]

[0051] Among them, F represents the state transition matrix, and X(k) and X(k - 1) represent the state values of the target at the current moment and the previous moment, respectively.

[0052] The beneficial effects of the present invention are as follows:

[0053] The present invention defines the start and end of the track as trigger events, and processes the situation information accordingly when encountering trigger events, reducing the communication volume in the system process while maintaining the accuracy of the results. In addition, for the unification of situation information, the present invention proposes a track association method, which can not only identify trigger events, but also unify the track labels of the same target and find missing targets, improving the accuracy of the analysis of battlefield situation information. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] FIG. 1(a) is a processing result diagram of node 1 in target 1;

[0055] FIG. 1(b) is a processing result diagram of node 2 in target 1;

[0056] FIG. 1(c) is a processing result diagram of node 3 in target 1;

[0057] FIG. 1(d) is a consistency processing result diagram of target 1;

[0058] FIG. 2(a) is a processing result diagram of node 1 in target 2;

[0059] FIG. 2(b) is a processing result diagram of node 2 in target 2;

[0060] FIG. 2(c) is a processing result diagram of node 3 in target 2;

[0061] FIG. 2(d) is a consistency processing result diagram of target 2;

[0062] FIG. 3(a) is a processing result diagram of node 1 in target 3;

[0063] FIG. 3(b) is a processing result diagram of node 2 in target 3;

[0064] FIG. 3(c) is a processing result diagram of node 3 in target 3;

[0065] FIG. 3(d) is a consistency processing result diagram of target 3;

[0066] FIG. 4(a) is a processing result diagram of node 1 in target 4;

[0067] FIG. 4(b) is a processing result diagram of node 2 in target 4;

[0068] FIG. 4(c) is a processing result diagram of node 3 in target 4;

[0069] Figure 4(d) is the result graph of target 4 consistency processing;

[0070] Figure 5(a) is the result graph of node 1 in target 5;

[0071] Figure 5(b) is the result graph of node 2 in target 5;

[0072] Figure 5(c) is the result graph of node 3 in target 5;

[0073] Figure 5(d) is the result graph of target 5 consistency processing;

[0074] Figure 6(a) is the result graph of node 1 in target 6;

[0075] Figure 6(b) is the result graph of node 2 in target 6;

[0076] Figure 6(c) is the result graph of node 3 in target 6;

[0077] Figure 6(d) is the result graph of target 6 consistency processing;

[0078] Figure 7(a) is the result graph of node 1 in target 7;

[0079] Figure 7(b) is the result graph of node 2 in target 7;

[0080] Figure 7(c) is the result graph of node 3 in target 7;

[0081] Figure 7(d) is the result graph of target 7 consistency processing;

[0082] Figure 8(a) is the result graph of node 1 in target 8;

[0083] Figure 8(b) is the result graph of node 2 in target 8;

[0084] Figure 8(c) is the result graph of node 3 in target 8;

[0085] Figure 8(d) is the result graph of target 8 consistency processing;

[0086] Figure 9(a) is the result graph of node 1 in target 9;

[0087] Figure 9(b) is the result graph of node 2 in target 9;

[0088] Figure 9(c) is the result graph of node 3 in target 9;

[0089] Figure 9(d) is the result graph of target 9 consistency processing;

[0090] Figure 10 It is the result graph of node 1 fusion;

[0091] Figure 11It is the fusion result diagram of Node 2;

[0092] Figure 12 It is the fusion result diagram of Node 3;

[0093] Figure 13 It is the fusion result diagram after processing;

[0094] Figure 14 It is the flow chart of the present invention. Detailed implementation manners

[0095] The collaborative operation task studied by the present invention is multi-sensor multi-target tracking and information fusion. The generalized labeled multi-Bernoulli filter is applied for target tracking and several different fusion centers or nodes are established through the distributed fusion idea. Each node represents a combat unit in the battlefield and contains various situation information. There may be differences in the situation information between different nodes, and due to communication failures or other reasons, a certain node or some nodes may have missing or disordered situation information, resulting in problems such as missing or interrupted fusion result tracks, as well as missing, duplicate, or wrongly corresponding track labels. To solve the above problems, a situation consistency system is applied to process the fusion nodes to maintain the consistency of various important information of dynamic targets.

[0096] At the level of the processing purpose of the technology, the existing consistency processing technologies based on target tracking and fusion mainly aim at the state value differences and label errors caused by measurement information differences, tracking errors, etc., and maintain the consistency and accuracy of the situation information transmitted to each fusion node through consistency processing. However, they do not consider the problems of missing and interrupted target tracks caused by communication and other issues, as well as the problem of missing labels, especially the situation where there is no situation information of a certain expected track in all fusion nodes. This leads to the situation that although the obtained situation information can maintain consistency, there will be problems of missing information, which will have a certain impact on the analysis of the battlefield situation in practical applications.

[0097] At the specific algorithm level of the technology, in the existing technologies, the situation information is basically processed for consistency at each moment. This method requires communication between nodes at all times, which will generate a large amount of redundant information and require a large amount of communication. In the actual battlefield background, the communication module will bear a great deal of pressure, and information overload or disorder may occur. In this paper, the method of triggering events is applied. The start and end of the track are defined as triggering events. When a triggering event is encountered, the situation information is processed accordingly. This approach can better reduce the communication volume in the system while maintaining the accuracy of the results, meeting the requirements of the actual application background. In addition, for the unification of situation information, this paper applies a track association method, which can not only identify triggering events, but also unify the track labels of the same target and find missing targets. This association method plays a decisive role in the situation consistency processing system and has important research significance in practical applications. Next, the present invention will be described in conjunction with specific embodiments.

[0098] Specific Embodiment 1: As Figure 14 shown, the battlefield situation consistency processing system of this Embodiment 1 includes: a situation information acquisition module and a situation information processing module;

[0099] The situation information acquisition module is used to fuse the multi-sensor target tracking result information to obtain the track situation information of each fusion node; wherein, each fusion node is each combat unit in the battlefield;

[0100] The situation information acquisition module is used to fuse the multi-sensor target tracking result information to obtain the track situation information of each fusion node by implementing the GCI fusion criterion;

[0101] Each fusion node includes a track library, including: local tracks, remote tracks, and common tracks;

[0102] The local track is the target track obtained by the current fusion node, the remote track is the target track detected by other fusion nodes, and the common track is the track generated after the fusion of the local track and the remote track. In the system, the information of the local track and the remote track will be judged and processed, and finally a consistent common track will be obtained;

[0103] The track situation information includes: track status value, track label;

[0104] The situation information processing module includes: a track association unit, a track status value unification unit, and a track label unification unit;

[0105] The track association unit is used to associate the multiple tracks obtained at the current moment with the tracks at the previous moment t respectively, and change the track labels of the successfully associated tracks to be consistent with the previous moment t;

[0106] The track status value unification unit is used to unify the track status values between different fusion nodes of the associated track; the track status values include: spatial position coordinates, speed values;

[0107] The track label unification unit is used to unify the labels between different fusion nodes and correctly correspond to the unified track status values;

[0108] The track label unification unit and the track status value unification unit are executed simultaneously.

[0109] In this embodiment, the architecture of the situation generation system is completed, that is, the target tracking and information fusion processes are completed, and the situation information of the corresponding nodes is obtained. For multi-sensor target tracking, in order to improve the accuracy and stability of tracking, information fusion is performed on the tracking results. The commonly used information fusion technologies are generally divided into two types: centralized and distributed. In the centralized fusion structure, the integrity is emphasized more. The measurements of each sensor will directly perform relevant filtering in the fusion node. Such a process can minimize the information loss. However, since there is no independent tracking of a single sensor, there will be relatively complex data association problems, and the computational complexity is relatively high, which affects the accuracy of the algorithm. In the distributed fusion structure, the processing results of each sensor are transmitted, and the results are sent to the fusion node for further fusion and association operations. There is no complex data association process. The advantages of the distributed fusion structure are small communication delay, low computational complexity, and strong reliability. When the accuracy of a sensor is affected or it cannot work, its observation results will not have a great impact on the overall fusion structure. Therefore, considering that the distributed fusion has advantages such as strong survivability, high fault tolerance rate, and high stability, which is more in line with the application requirements in the actual complex environment, a distributed fusion system is used for information fusion processing, and a robust sub-optimal distributed fusion criterion, that is, the GCI fusion criterion, is used to process the fusion system. Through the processing of distributed fusion in the situation generation system, the situation information of different nodes is obtained, laying a foundation for subsequent processing work.

[0110] Specific embodiment two: The track association unit is used to associate the multiple tracks obtained at the current moment with the tracks at the previous moment t respectively, and is implemented by using the local optimal track association algorithm, specifically including:

[0111] Step 1: Obtain the number of tracks m and n in two groups of tracks to be associated;

[0112] Step 2: Obtain the Euclidean distance d between each two tracks i,j :

[0113]

[0114] where x i , y i, z i and x j , y j , z j respectively represent the status values of two tracks, namely the corresponding x, y, and z axis coordinates, and d i,j represents the Euclidean distance between these two tracks, and i and j are the two tracks respectively;

[0115] Step 3: According to the d obtained in Step 2 i,j Obtain an m*n track association matrix D:

[0116]

[0117] Step 4: Set a track association threshold M, and compare the minimum Euclidean distance value of each row in the track association matrix D with the track association threshold M. If the minimum Euclidean distance value of each row in D is less than or equal to M, the association is successful; if the minimum Euclidean distance value of each row in D is greater than M, the association fails:

[0118]

[0119] Step 5: Change the track label of the current moment with successful association to be the same as the corresponding track label of the previous moment, and use the track with failed association as a new track to continue processing at the next moment, that is, re-execute Step 1;

[0120] In this implementation, compared with the global optimal association method, this local optimal method sets a fixed threshold, which can better solve the situation where the Euclidean distances between two groups of tracks are relatively close but do not belong to the same target, improving the accuracy of association. This association method has good processing effects on track label misalignment, duplicate names, and missing, and solves the problem of maintaining the consistency of fusion node labels.

[0121] The track situation information of different nodes can be mainly divided into two categories, namely the status value of the track and the label of the track. On this basis, the situation consistency system will face the following types of problems:

[0122] (1) The status values of tracks between different nodes (including spatial position coordinates and speed values) are different, and it is necessary to use the consistency system to unify the information.

[0123] (2) Due to problems such as communication failures, the status value of a certain or some nodes is missing, that is, target loss or track interruption occurs. It is necessary to complete the information of the nodes with this problem through the consistency system.

[0124] (3) There are problems with the label information of the tracks in the nodes, specifically manifested as incorrect correspondence between the labels and the actual tracks, duplicate names and multiple names of the labels, and the absence of labels. Therefore, a consistency system is needed to solve the above problems, unify the labels between different nodes, and correctly correspond them to the processed status value information.

[0125] To solve the above three types of problems, the situation consistency processing system is applied to identify, screen, and process the information obtained by the situation generation system. This part is the key research content of the present invention. Under the condition of maintaining low communication volume, to solve the problems of target loss and track interruption caused by abnormal information transmission of nodes, the start and end of the track are defined as trigger events, and the trigger events are identified once every fixed time in the system process. Corresponding processing is performed on the nodes in the face of different situations to complete the unification of node information. Next, it will be described in combination with the specific embodiment three:

[0126] Specific embodiment three: The track status value unification unit is used to unify the track status values between different nodes, and specifically includes the following steps:

[0127] S1. Obtain the moment when the trigger event occurs:

[0128] The moment when the number of tracks changes is the moment when the trigger event occurs;

[0129] The trigger events include: track start, termination;

[0130] S2. Sequentially identify whether there is a trigger event within a fixed time step. If a trigger event occurs in a certain fusion node within the time step l, then execute S3. At the same time, for the moments when no trigger event occurs within the time step l, output the processing result of the track association unit; if no trigger event occurs in the fusion node within the time step l, directly output the processing result of the track association unit;

[0131] S3. Define the fusion node with the largest number of targets at the moment when the trigger event occurs as the reference node, use the local optimal track association algorithm to associate the reference node with other fusion nodes respectively, form a common track for the successfully associated tracks by using the weighted average method, the track label is the same as the label of the reference node, and send the status value and label of the formed common track to each fusion node; if there are unassociated tracks, it means that there is a lack of target tracks in one or some fusion nodes, and supplement the status value and label of this track to the track library of the fusion node where the corresponding track is missing to obtain the consistent fusion node situation information at the current moment;

[0132] S4. Obtain the type of the trigger event:

[0133] First, fit the track at the previous moment to a motion model (basic uniform linear motion model) to obtain the fitted value at the current moment. Associate this fitted value with the track at the current moment. If two tracks are successfully associated, it means these two tracks represent the same target, and there is no trigger event in the target track at the current moment. Then, keep the track label the same as that at the previous moment. If a certain track h fails to be associated with any other track, determine whether track h comes from the previous moment or the current moment. If it comes from the previous moment, it indicates a track termination trigger event, and track h terminates. If it comes from the current moment, it indicates a track start trigger event, and record the time when the event occurs and the corresponding fusion node.

[0134] S5. Process the tracks of the fusion nodes where trigger events occur according to the trigger event types obtained in S4 to obtain the unified track status value:

[0135] S501. If the trigger event that occurs is track start, determine whether there is a track termination before the moment when the track start trigger event occurs. If there is a track termination, perform track association on the starting track and the terminating track. If the track association is successful, it means a track interruption has occurred during this period, and then execute b). If the association fails or there is no track termination, it means the track is a newly started track, and then execute a).

[0136] a). Store the new track as a local track in the track library, and at the same time, determine whether there is only one fusion node where this new track appears at the arrival moment of the new track. If there is only one fusion node where this new track appears, supplement this new track to the track libraries of other fusion nodes to obtain the unified track status value. If there is more than one fusion node where this new track appears, perform weighted processing on this new track to form a common track, and send this common track to each fusion node to obtain the unified track status value.

[0137] b). Check whether a track interruption also occurs at the track interruption moment a in other fusion nodes. If no interruption occurs, assign the track without interruption to the track with track interruption to obtain the unified track status value. If there is a track interruption for a certain track in all fusion nodes at moment a, use the backtracking method to compensate for the missing track to obtain the unified track status value.

[0138] S502. If the trigger event that occurs is track termination, it means there is no target track after this trigger moment. Then, use the local optimal track association algorithm to determine whether there is a track corresponding to the target in other fusion nodes. If there is, supplement the remote track information to the local track library. If there is no track corresponding to the target in all fusion nodes, it is regarded as track termination, and record the moment of track termination for track interruption judgment.

[0139] In this embodiment, in the situation consistency system, after receiving the situation information of the situation generation system, the identification of trigger events needs to be carried out first. The start and end of the track are defined as trigger events. This method can clearly know the time when the track starts and ends and the position where the interruption occurs. There are clear time nodes in the processing process, which improves the accuracy and stability of the system. At the same time, considering that tracking errors often occur at certain moments within a single node, resulting in inconsistent track labels at different times, it is necessary to preprocess the track labels. The method applied here is the association between tracks at every two moments within the node, that is, for each fusion node, the track results obtained at each moment during the processing are associated with the results of the previous moment, and the same label is maintained for the same target track at different times while identifying trigger events. If the trigger event type is a track start event, it can be divided into two cases. One is the first start of a new target, and this target track has not appeared before the trigger moment; the second is that an existing target starts to be interrupted at a certain moment and reappears at the trigger moment. The processing methods for the two cases are different, so it is necessary to make a judgment first. The specific method is to judge whether there is a track end trigger event before the track start trigger moment. If it exists, the track starting at this moment and the track at the track end moment are associated. Since the time of track end and the state value of the track have been recorded, the association of the two groups of tracks can be completed. If the association is successful, it means that a track interruption has occurred during the period from the track end moment to the track start moment, and it is impossible to complete the track by communicating between different nodes, and it needs to be processed by the backtracking method; if the association is not successful, it means that this track is a newly started track. If there is no track end before the trigger moment, it also represents that this track is a newly started track.

[0140] Specific Embodiment 4: The backtracking method is specifically as follows:

[0141] When all fusion nodes of a certain track are interrupted within a period of time, it is impossible to unify the information through the communication of several fusion nodes at this time. Therefore, the method of track backtracking is considered. The relatively common uniform linear motion model is used here, and the target motion state is modeled as shown in Equation (4)

[0142]

[0143] where (x’, y’, z’) represents the coordinates in the motion model, and the subscript with 0 represents the coordinates of the initial position of the model, v x' , v y' , v z' represents the velocity values in the three directions, and T represents the model process time;

[0144] Backtrack the target according to the above target motion model. The state of the target at time k can be expressed as

[0145] X(k) = F·X(k - 1) (5)

[0146]

[0147] where F represents the state transition matrix, and X(k) and X(k - 1) represent the state values of the target at the current time and the previous time respectively;

[0148] Through the above formula, the state of the target at each moment during the interruption period can be deduced recursively to complete the complement of the interruption correlation.

[0149] Embodiment: Verify the beneficial effects of the present invention through simulation experiments as follows:

[0150] Set the actual background of target tracking and fusion in the simulation experiment. There are three fusion nodes in the actual background. During the fusion process of each node, first, five sensors track the target separately, and then the distributed fusion structure is applied to send the results to the fusion center to complete the subsequent information fusion process. There are a total of nine targets in the experiment, and sampling is performed every 1 second during the tracking and fusion process. The simulation lasts for 55 seconds in total. In order to solve the trigger events generated during the communication process and maintain the consistency of the information of the fusion nodes during the target fusion process, the nodes are communicated once every 5s. If a trigger event is found during the communication process, the event is processed to maintain the consistency of the node information. During the simulation process, the state parameters of the target are six-dimensional vectors [x, v x , y, v y , z, v z , representing the positions and velocities in the x, y, and z directions respectively. The initial states are as follows:

[0151] x1 = [1000m; -35m / s; 750m; -33m / s; 400 + 5m; 10m / s;]

[0152] x2 = [690m; -40m / s; 1060m; -30m / s; 400 + 5m; 10m / s;]

[0153] x3 = [-1020m; 28m / s; 730m; -42m / s; 400 - 5m; 10m / s;]

[0154] x4 = [-720m; 38m / s; 1030m; -32m / s; 400 + 5m; 10m / s;]

[0155] x5 = [-980m; 22m / s; -770m; 48m / s; 400 + 5m; 10m / s;]

[0156] x6 = [-780 m; 44 m / s; -970 m; 26 m / s; 400 + 5 m; 10 m / s;]

[0157] x7 = [960 m; -40 m / s; -790 m; 30 m / s; 400 + 5 m; 10 m / s;]

[0158] x8 = [700 m; -40 m / s; -1050 m; 29 m / s; 400 + 5 m; 10 m / s;]

[0159] x9 = [1250 m; -40 m / s; 0 m; 0 m / s; 400 + 5 m; 10 m / s;]

[0160] There are trigger events in the target tracking fusion process corresponding to each fusion node, including situations such as track loss and interruption. The specific settings are shown in Table 1. The moments in the table represent the times when the trigger events occur. Target 2 represents the moments when the target track exists, and the times corresponding to the other targets represent the times of target track loss and interruption. Additionally, the track of target 6 is complete without track loss problems.

[0161] Table 1

[0162] Node Target 1 Target 2 Target 3 Target 4 Target 5 Target 6 Target 7 Target 8 Target 9 1 10-15s 1-30s 16-40s 10-20s 15-32s —— 25-36s 29-40s 15-40s 2 25-30s 21-40s 20-35s 15-30s 20-34s —— 25-38s 27-40s 20-35s 3 40-45s 36-55s 18-30s 25-35s 25-31s —— 25-30s 25-40s 25-30s

[0163] As shown in the figure Figure 1(a) - Figure 9(d) They respectively represent the fusion results of each target by three fusion nodes before being processed by trigger events and the fusion results after being processed by trigger events using the consistency algorithm.

[0164] As Figure 1(a) - Figure 1(d) is the comparison diagram of the processing results of target 1. For target 1, the tracks of the three nodes are all interrupted. The interruption of node 1 is in the first half, the interruption of node 2 is in the middle part, and the interruption of node 3 is in the second half. There is no time intersection at the three interruption positions. After processing, it is found that the entire track can be completed.

[0165] As Figure 2(a) - Figure 2(d) is the comparison diagram of the processing results of target 2. For target 2, the track of node 1 only exists in the first half, the track of node 2 only exists in the middle part, and the track of node 3 only exists in the second half. After processing, it is found that the entire track can be completed.

[0166] As Figure 3(a) - Figure 3(d) is the comparison diagram of the processing results of target 3. For target 3, the tracks of the three nodes are all interrupted, and there is a time intersection at the three interruption positions. And during the processing of a certain 5 s, there are track termination trigger events at three different positions simultaneously. After processing, it is found that the entire track can be completed.

[0167] As Figure 4(a) - Figure 4(d) is the comparison chart of the processing results of target 4. For target 4, the tracks of the three nodes are all interrupted, and there is a time overlap between every two of the three interruption positions. After processing, it is found that the entire track can be completed.

[0168] As Figure 5(a) - Figure 5(d) is the comparison chart of the processing results of target 5. For target 5, the tracks of the three nodes are all interrupted, and there is a time intersection among the three interruption positions. Also, during the processing of a certain 5s, there are track start trigger events at three different positions simultaneously. After processing, it is found that the entire track can be completed.

[0169] As Figure 6(a) - Figure 6(d) is the comparison chart of the processing results of target 6. For target 6, the tracks of the three nodes are all complete tracks, without problems of missing and interruption, and the processing result is still a complete track.

[0170] As Figure 7(a) - Figure 7(d) is the comparison chart of the processing results of target 7. For target 7, the tracks of the three nodes are all interrupted, and there is a time intersection among the three interruption positions. Also, during the processing of a certain 5s, there are track start trigger events at two different positions simultaneously. After processing, it is found that the entire track can be completed.

[0171] As Figure 8(a) - Figure 8(d) is the comparison chart of the processing results of target 8. For target 8, the tracks of the three nodes are all interrupted, and there is a time intersection among the three interruption positions. Also, during the processing of a certain 5s, there are track termination trigger events at two different positions simultaneously. After processing, it is found that the entire track can be completed.

[0172] As Figure 9(a) - Figure 9(d) is the comparison chart of the processing results of target 9. For target 9, the tracks of the three nodes are all interrupted, and the three interruption positions have a containment relationship in time. The specific containment relationship is that 1 contains 2 contains 3. After processing, it is found that the entire track can be completed.

[0173] As Figures 10 - 12 respectively represent the overall fusion results after the trigger event processing of the three fusion nodes is not performed. It can be found that each target in each node has different track missing and interruption situations.

[0174] As Figure 13 represents the overall fusion result after the trigger event is processed by the consistency algorithm. Comparing with the result charts of the three nodes without processing, it can be clearly seen that a large number of track missing and interruption problems in the fusion node tracks are successfully solved, and a relatively complete fusion result can be obtained. At the same time, the three fusion nodes obtain the same state value results after processing, ensuring the consistency of the target motion state information.

[0175] To maintain the consistency of the fusion node labels, the state values and label values of the tracks are analyzed and processed through the association of aircraft tracks. At the same time, the consistency rates of the labels before and after processing are calculated and compared. Table 2 shows the consistency rates of the labels corresponding to 9 targets before and after processing. It can be clearly seen that except for the relatively high consistency of targets 1 and 2 among the unprocessed labels, the consistency rates of the labels of the remaining targets are very low, indicating that the labels are almost completely different. After the consistency processing, the consistency rates of the labels corresponding to each target have been significantly improved. Therefore, this algorithm can not only maintain the consistency of the state information between nodes, but also greatly improve the consistency of the label information between nodes.

[0176] Table 2 Results of Label Consistency Rate

[0177]

[0178]

[0179] The above experiments prove that the battlefield situation consistency system applying the present invention can well complete the consistency of the target motion state information and label information. At the same time, when problems such as target loss and track interruption occur at nodes, the present invention can also complete the missing or interrupted positions to maintain the consistency of information. The situation consistency system applying trigger events can not only improve the accuracy and stability of the system, but also reduce the communication volume of the system, improve the system operation speed and processing efficiency, which is a relatively perfect way to realize situation consistency for the battlefield based on target tracking and information fusion.

Claims

1. A battlefield situation consistency processing system, characterized in that The system includes: a situation information acquisition module, and a situation information processing module; The situation information acquisition module is used to fuse the multi-sensor target tracking result information to obtain the track situation information of each fusion node; wherein, each fusion node is a combat unit in the battlefield; Each fusion node has a track library, which includes: local tracks, remote tracks, and common tracks; The track situation information includes: a track status value, and a track label; The track status value includes: a spatial position coordinate, and a speed value; The situation information processing module includes: a track association unit, a track status value unification unit, and a track label unification unit; The track association unit is used to associate the multiple tracks obtained at the current moment with the tracks at the previous moment t respectively, and change the track labels of the successfully associated tracks to be the same as those at the previous moment t; The track status value unification unit is used to unify the track status values between different fusion nodes of the associated tracks, including the following steps: S1. Obtain the moment when the trigger event occurs: Among them, the moment when the number of tracks changes is the moment when the trigger event occurs; S2. Sequentially identify whether there is a trigger event within a fixed time step. If a trigger event occurs in a certain fusion node within the time step l, then execute S3. At the same time, for the moments when no trigger event occurs within the time step l, output the processing result of the track association unit; if no trigger event occurs in any fusion node within the time step l, directly output the processing result of the track association unit; S3. Define the fusion node with the largest number of targets at the moment when the trigger event occurs as the reference node, use the local optimal track association algorithm to associate the tracks of the reference node with the tracks of other fusion nodes respectively, form a common track for the successfully associated tracks by using the weighted average method, make the track label correspond to the label of the reference node, and send the status value and label of the formed common track to each fusion node; if there are tracks that are not successfully associated, it means that there is a lack of target tracks in one or some fusion nodes, and supplement the status value and label of the tracks that are not successfully associated into the track library of the fusion node where the corresponding track is missing, to obtain the consistent fusion node situation information at the current moment; S4. Obtain the type of the trigger event: Fit the motion model of the tracks at the previous moment to obtain the fitting value at the current moment, and associate the fitting value with the tracks at the current moment; If two tracks are successfully associated, it means that these two tracks belong to the same target, and unify the track label with the previous moment; If a certain track h is not successfully associated with any other track, judge whether track h comes from the previous moment or the current moment. If it comes from the previous moment, it means that a termination trigger event occurs for track h; if it comes from the current moment, it means that a start trigger event occurs for track h, and record the time when the event occurs and the corresponding fusion node; S5. Process the tracks of the fusion node where the trigger event occurs according to the type of the trigger event obtained in S4 to obtain the unified track status value; The track label unification unit is used to unify the labels between different fusion nodes and correctly correspond them to the unified track status value, and execute simultaneously with the track status value unification unit.

2. The battlefield situation consistency processing system according to claim 1, wherein: The situation information acquisition module is used to fuse the multi-sensor target tracking result information to obtain the track situation information of each fusion node, which is realized by using the GCI fusion criterion.

3. The battlefield situation consistency processing system according to claim 2, characterized in that: The track association unit is used to associate the multiple tracks obtained at the current moment with the tracks at the previous moment t respectively, and change the track labels of the successfully associated tracks to be the same as those at the previous moment t, which is realized by using the local optimal track association algorithm.

4. The battlefield situation consistency processing system according to claim 3, wherein: The local optimal track association algorithm includes the following steps: Step 1: Obtain the number of tracks m and n in two groups of tracks to be associated. Step 2: Obtain the Euclidean distance d between every two tracks i,j : where x i , y i , z i and x j , y j , z j represent the state values of two tracks, namely the corresponding x, y, and z-axis coordinates, and d i,j represents the Euclidean distance between these two tracks, and i and j are the two tracks respectively; Step 3. Obtain d according to Step 2 i,j Obtain an m×n track association matrix D: Step 4: Set the track association threshold M, and compare the minimum Euclidean distance value of each row in the track association matrix D with the track association threshold M. If the minimum Euclidean distance value of each row in D is less than or equal to M, the association is successful; if the minimum Euclidean distance value of each row in D is greater than M, the association fails. Step 5: Change the track label of the currently associated successful track to be the same as the corresponding track label at the previous moment, and take the track with the failed association as a new track and re-execute Step 1.

5. The battlefield situation consistency processing system according to claim 4, characterized in that: In Step 4, set the track association threshold M, and compare the minimum Euclidean distance value of each row in the track association matrix D with the track association threshold M. If the minimum Euclidean distance value of each row in D is less than or equal to M, the association is successful; if the minimum Euclidean distance value of each row in D is greater than M, the association fails, as shown in the following formula:

6. The battlefield situation consistency processing system according to claim 5, wherein: In S5, process the tracks of the fusion node where the trigger event occurs according to the trigger event type obtained in S4 to obtain the unified track state value, including the following steps: S501: If the trigger event that occurs is track initiation, judge whether there is a track termination before the moment when the track initiation trigger event occurs. If there is a track termination, perform track association on the initiated track and the terminated track. If the track association is successful, it means that a track interruption has occurred during this period, then complete the interrupted track and obtain the unified track state value. If the track association is not successful or there is no track termination, it means that this track is a newly initiated track, and use the newly initiated track to obtain the unified track state value. S502: If the trigger event that occurs is track termination, it means that there is no target track after the moment when the current trigger time occurs. Then, judge whether there is a track corresponding to the target in other fusion nodes through the local optimal track association algorithm. If there is, supplement the remote track information to the local track library. If there is no track corresponding to the target in all fusion nodes, it is regarded as track termination, and record the moment of track termination for the judgment of track interruption.

7. The battlefield situation consistency processing system according to claim 6, wherein: In S501, if the track association is not successful or there is no track termination, it means that this track is a newly initiated track, and use the newly initiated track to obtain the unified track state value. Specifically: Store the new track as a local track in the track library, and at the same time, determine whether there is only one fusion node where this new track appears at the arrival time of the new track; if there is only one fusion node where this new track appears, supplement this new track to the track libraries of other fusion nodes to obtain the unified track status value; if there is more than one fusion node where this new track appears, perform weighted processing on this new track to form a common track, and send this common track to each fusion node to obtain the unified track status value.

8. The battlefield situation consistency processing system according to claim 7, characterized in that: If the track association is successful, it indicates that a track interruption has occurred during this period. Then, complete the interrupted track and obtain the unified track status value. Specifically: Check whether a track interruption also occurred at the track interruption time a in other fusion nodes. If no interruption occurred, assign the track without interruption to the track with interruption to obtain the unified track status value; if there is a track interruption for a certain track in all fusion nodes at time a, use the backtracking method to compensate for the missing track to obtain the unified track status value.

9. The battlefield situation consistency processing system according to claim 8, wherein: The backtracking method is implemented using the following model: Among them, (x’, y’, z’) represents the coordinates in the motion model, and those with subscript 0 represent the coordinates of the initial position of the model, v x' , v y' , v z' represent the velocity values in three directions, and T represents the model process time; Perform backtracking on the target according to the above target motion model. The state of the target at time k can be expressed as: X(k) = F · X(k - 1) (5) Where, F represents the state transition matrix, and X(k) and X(k - 1) represent the state values of the target at the current time and the previous time, respectively.

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