A distributed integrated sensor tracking resource management method, medium and device

Abstract

The application provides a distributed integrated sensor tracking resource management method, medium and device, the method comprises the following steps: step 1, the active detection measurement of a distributed integrated sensor system is used to measure the initial track; step 2, the PCRLB information increment of the target is calculated; step 3, based on the PCRLB information increment, the distributed integrated sensor tracking resource is distributed; step 4, the track is updated based on Kalman filtering; step 5, according to the PCRLB information increment maximum criterion, the integrated sensor is switched in the next time. According to the PCRLB information increment, only one integrated sensor active measurement is selected to update the tracking track each time, multiple integrated sensors do not need to interrupt the existing work plan and work in the detection tracking mode at the same time, the simultaneous multi-functional capability of the distributed integrated sensor system is not affected, the number of active radiation of the system is reduced, and the response capability of the distributed integrated system to the complex electromagnetic environment is improved.

Description

Technical Field

[0001] This invention relates to the field of sensor resource management technology, and more specifically, to a distributed integrated sensor tracking resource management method, medium, and device. Background Technology

[0002] When using the active detection capabilities of distributed multi-platform systems to continuously monitor and track targets, there are currently two multi-sensor collaborative working modes: one is that each sensor independently detects and tracks targets in space, and then the fusion center fuses the track tracking results of multiple sensors to form a unified track; the other is that multiple sensors simultaneously detect and track the same target, and directly fuse the point track data obtained by multiple sensors.

[0003] Multifunctional integrated sensors possess reconnaissance, communication, and detection capabilities. If the distributed integrated sensor operates in the first multi-sensor collaborative mode, it merely fuses track results, offering limited improvement in tracking accuracy and failing to enhance the overall system's target tracking capacity. If the distributed integrated sensor operates in the second multi-sensor collaborative mode, fusing the tracks from multiple sensors can improve tracking accuracy. However, multiple integrated sensors require time-coordinated alignment and must interrupt their current tasks and plans to simultaneously operate in detection mode for target tracking and monitoring. This increases the system's radio frequency exposure risk, limits the multifunctional simultaneous operation capability of the distributed integrated sensor system, and fails to meet the simultaneous detection and communication requirements in complex electromagnetic environments. Summary of the Invention

[0004] The present invention aims to provide a distributed integrated sensor tracking resource management method, medium and device to solve the above-mentioned problems.

[0005] This invention provides a distributed integrated sensor tracking resource management method, comprising the following steps:

[0006] Step 1: Measure the initial trajectory using the active detection of a distributed integrated sensor system;

[0007] Step 2: Calculate the PCRLB information increment of the target;

[0008] Step 3: Allocate distributed integrated sensor tracking resources based on PCRLB information increments;

[0009] Step 4: Update the track based on Kalman filtering;

[0010] Step 5: In the next moment, switch the integrated sensor according to the criterion of maximizing PCRLB information increment.

[0011] Furthermore, in step 2, calculating the target's PCRLB information increment includes:

[0012] Calculate the information entropy at time k+1:

[0013]

[0014] Calculate the information entropy at time k:

[0015]

[0016] in,

[0017] Define the PCRLB information increment as:

[0018]

[0019] in, For the corresponding PCRLB at time k, This is the PCRLB corresponding to time k+1.

[0020] Furthermore, in step 3, the strategy for allocating distributed integrated sensor tracking resources is as follows:

[0021] (1) Prioritize the targets that need to be tracked and processed in the next moment according to their priority or threat level;

[0022] (2) Based on the target priority ranking results, calculate the PCRLB information increment of each sensor for the target at time k in turn;

[0023] (3) Allocate the target tracking sensor resources, that is, select the integrated sensor that maximizes the PCRLB information increment of the target as the tracking sensor.

[0024] (4) When multiple targets select the same sensor, the target with higher priority will select the sensor resource first, and the other targets will select the second-best sensor as the tracking sensor.

[0025] (5) Perform the iteration at time k+1.

[0026] Furthermore, when the number of tracked targets exceeds the number of sensors, a time-sharing strategy is used to handle simultaneous task requests from multiple targets.

[0027] Furthermore, in step 4, updating the track based on Kalman filtering includes:

[0028] (1) One-step prediction of the state:

[0029]

[0030] (2) One-step prediction of covariance:

[0031] P(k|k-1)=F(k-1)P(k-1|k-1)F(k-1) T +Q(k)

[0032] (3) If the measurement at the next moment is an active detection measurement by sensor i, then the one-step prediction of the measurement is as follows:

[0033]

[0034] (4) Obtain the state update and covariance update:

[0035] New covariance:

[0036] S(k)=H i (k)P(k|k-1)H i (k) T +R(k)

[0037] The gain is:

[0038] K(k)=P(k|k-1)H i (k) T S(k) -1

[0039] State update equation:

[0040]

[0041] Covariance update equation:

[0042] P(k|k)=[IK(k)H i (k)]P (k|k-1)[I+K(k)H i (k)] T -K(k)R(k)K(k) T

[0043] Where F(k) is the linearized state transition matrix, H i Q(k) is the linearized measurement matrix; Q(k) is the error covariance matrix of the process noise; and R(k) is the error covariance matrix of the observation noise.

[0044] The present invention also provides a computer terminal storage medium storing computer terminal executable instructions, which are used to execute the above-described distributed integrated sensor tracking resource management method.

[0045] The present invention also provides a computing device, comprising:

[0046] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described distributed integrated sensor tracking resource management method.

[0047] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0048] This invention selects only one integrated sensor to actively measure and update the tracking track each time based on the PCRLB information increment. This does not require multiple integrated sensors to interrupt their existing work plans and work simultaneously in detection and tracking mode. It does not affect the multi-functionality of the distributed integrated sensor system and reduces the number of active radiations in the system, which is more conducive to improving the distributed integrated system's ability to cope with complex electromagnetic environments. Attached Figure Description

[0049] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a flowchart of a distributed integrated sensor tracking resource management method in an embodiment of the present invention.

[0051] Figure 2 This is a diagram illustrating the resource allocation results of multi-sensor tracking based on PCRLB information increment, which is an application example in an embodiment of the present invention.

[0052] Figure 3 This is a comparison diagram of tracking trajectories of different tracking methods in an application example of this invention.

[0053] Figure 4 This is a comparison chart of tracking position errors of different tracking methods in an application example of this invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0055] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0056] Example

[0057] This embodiment proposes a distributed integrated sensor tracking resource management method based on the incremental information of the posterior Cramér-Rao Lower Bound (PCRLB).

[0058] PCRLB provides a measure of the optimal tracking accuracy achievable by a system, and can be calculated independently based on target and sensor predictions and measurements. The specific definition of PCRLB is as follows:

[0059] Suppose the motion equation of a target within the monitored area, after linearization and discretization, is as follows:

[0060] X(k+1)=F(k)X(k)+V(k)

[0061] The measurement equation for the i-th integrated sensor has also been processed accordingly to be:

[0062] Z i (k)=H i (k)X(k)+W i (k)

[0063] Where F(k) is the linearized state transition matrix, V(k) is the state noise, and H... i (k) is the linearized measurement matrix, W i (k) represents the observation noise.

[0064] Z k It is the set of radar measurements accumulated up to time k. To determine the state x of the target at time k based on this measurement set. k An efficient unbiased estimate. PCRLB provides a lower bound on the error covariance matrix between this estimate and the true value, namely:

[0065]

[0066] Where E[ ] represents (x k Z k The above formula shows the expectation of ). J is a positive semi-definite matrix. k Known as the Fisher information matrix, This is PCRLB. Jk The calculation can be performed iteratively using the following formula:

[0067]

[0068] For the linearized state equation and observation equation, we have:

[0069]

[0070]

[0071]

[0072]

[0073] In the formula, Q k With R k+1 These are the error covariance matrices of process noise and observation noise, respectively. The recursive formula is then derived as follows:

[0074]

[0075] In the formula, J x,k+1 J represents the prior information of the target state at time k+1. z,k+1 This information comes from current measurements. Clearly, the better the observation geometry between the radar and the target, the better J... k+1 The larger and more uniform the feature values ​​are, the greater the amount of information the target obtains, and the smaller the corresponding tracking error.

[0076] This invention calculates the difference in information increment resulting from the tracking behavior of different sensors based on PCRLB, and uses this as the basis for sensor selection. The distributed integrated sensor tracking resource management method of this invention includes the following steps:

[0077] Step 1, Starting track:

[0078] The initial trajectory is measured and filtered using an active detection system of a distributed integrated sensor system to obtain the initial state. And the initial covariance P(2|2).

[0079] Step 2, calculate the target's PCRLB information increment:

[0080] The corresponding PCRLB at time k+1 is Assuming that the tracking filter will make X(k+1|k+1) effective and unbiased, then X(k+1|k+1) will follow a path with the true value X(k+1) as the expectation. Given the standard distribution of variance, the corresponding information entropy is calculated as follows:

[0081]

[0082] Meanwhile, the information entropy at time k is:

[0083]

[0084] Define the information increment as:

[0085]

[0086] The greater the information increment brought about by a resource allocation scheme, the better the scheme is, and this is used as a performance indicator for evaluating resource scheduling or allocation schemes.

[0087] Step 3: Allocate distributed integrated sensor tracking resources based on PCRLB information increments:

[0088] To maximize the PCRLB information increment of the target at the next moment, a strategy for allocating distributed integrated sensor tracking resources based on the PCRLB information increment is designed.

[0089] For tasks requiring simultaneous tracking of multiple targets in the next moment, simultaneous task requests can be handled based on the priority of different targets. Higher-priority targets are given priority in selecting sensor resources, while other targets can choose the sensor corresponding to the second-highest PCRLB information increment to complete the tracking task. When the number of targets to be tracked exceeds the number of sensors, simultaneous task requests can be handled using a time-sharing strategy based on the priority of different targets. The specific processing flow of the strategy for allocating distributed integrated sensor tracking resources is as follows:

[0090] (1) Prioritize the targets that need to be tracked and processed in the next moment according to their priority or threat level;

[0091] (2) Based on the target priority ranking results, calculate the PCRLB information increment of each sensor for the target at time k in turn;

[0092] (3) Allocate the target tracking sensor resources, that is, select the integrated sensor that maximizes the PCRLB information increment of the target as the tracking sensor.

[0093] (4) When multiple targets select the same sensor, the target with higher priority selects the sensor resource first, and the other targets select the second best sensor as the tracking sensor; when the number of tracking targets is greater than the number of sensors, a time-sharing strategy is used to handle the simultaneous task requests of multiple targets.

[0094] (5) Perform the iteration at time k+1.

[0095] Thus, the problem of simultaneous tracking of multiple targets by multiple sensors is simplified to the problem of choosing the largest increment of PCRLB information from multiple sensors, which does not require complex optimization algorithms to solve.

[0096] Step 4, update the track based on Kalman filtering:

[0097] (1) One-step prediction of the state:

[0098]

[0099] (2) One-step prediction of covariance:

[0100] P(k|k-1)=F(k-1)P(k-1|k-1)F(k-1) T +Q(k)

[0101] (3) If the measurement at the next moment is an active detection measurement by sensor i, then the one-step prediction of the measurement is as follows:

[0102]

[0103] (4) Obtain the state update and covariance update:

[0104] New covariance:

[0105] S(k)=H i (k)P(k|k-1)H i (k) T +R(k)

[0106] The gain is:

[0107] K(k)=P(k|k-1)H i (k) T S(k) -1

[0108] State update equation:

[0109]

[0110] Covariance update equation:

[0111] P(k|k)=[IK(k)H i (k)]P (k|k-1)[I+K(k)H i (k)] T -K(k)R(k)K(k) T

[0112] Step 5: In the next moment, switch the integrated sensor according to the PCRLB information increment criterion, and return to step 2 to repeat the above steps.

[0113] Furthermore, in some embodiments, a computer terminal storage medium is proposed, storing computer terminal executable instructions for executing the distributed integrated sensor tracking resource management method as described in the preceding embodiments. Examples of computer storage media include magnetic storage media (e.g., floppy disks, hard disks, etc.), optical recording media (e.g., CD-ROMs, DVDs, etc.) or memory such as memory cards, ROMs, or RAMs. The computer storage medium can also be distributed across a network-connected computer system, for example, as an application store.

[0114] Furthermore, in some embodiments, a computing device is proposed, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the distributed integrated sensor tracking resource management method as described in the foregoing embodiments. Examples of computing devices include PCs, tablets, smartphones, or PDAs.

[0115] An application example:

[0116] Consider that our side has three integrated sensors located at (10km, 0), (20km, 0), and (38km, 0) in the two-dimensional plane along the X and Y axes, respectively, while the target platform's initial position is (40km, 250km). From 0 to 50s, the enemy platform moves at a constant velocity of (100, -100) m / s in the X and Y directions towards the southeast; from 51 to 100s, the enemy platform turns with an acceleration of 2 (turning rate 0.0141) along the tangential direction; from 101 to 200s, the enemy platform moves at a constant velocity in a straight line.

[0117] The distributed integrated sensor system is configured with a distance noise root mean square error of 100m and an angle noise root mean square error of 0.09° for active detection; the system's sampling interval is 0.5s.

[0118] The target tracking methods used in the comparison include:

[0119] (1) The proposed tracking algorithm based on PCRLB information increment.

[0120] (2) Active detection and tracking of the target using only sensor 1, sensor 2 and sensor 3.

[0121] (3) The three sensors simultaneously measure the target and fuse the tracking results.

[0122] Figure 2This is the result of multi-sensor resource allocation based on PCRLB information increment. It can be seen that the tracking resource management method of this invention alternately selects sensor 1 and sensor 3. This is because sensor 1 and sensor 3 have the greatest observational geometric difference from the target, meaning that the amount of information obtained from tracking the target is the greatest, and the corresponding tracking error is the smallest.

[0123] Figure 3 and Figure 4 This is the tracking result of the method of the present invention on the target. In terms of tracking performance, the method of the present invention is similar to the performance of multi-sensor simultaneous collaborative tracking methods, and far superior to the tracking performance of a single sensor. However, the method of the present invention does not require multiple sensors to operate simultaneously in detection and tracking modes, which is beneficial for the simultaneous application of multiple functions and enhances the system's security.

[0124] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A distributed integrated sensor tracking resource management method, characterized in that, Includes the following steps: Step 1: Measure the initial trajectory using the active detection of a distributed integrated sensor system; Step 2: Calculate the PCRLB information increment of the target; Step 3: Allocate distributed integrated sensor tracking resources based on PCRLB information increments; Step 4: Update the track based on Kalman filtering; Step 5: In the next moment, switch the integrated sensor according to the criterion of maximizing the PCRLB information increment; In step 2, the calculation of the target's PCRLB information increment includes: calculate Information entropy at any given moment: calculate Information entropy at any given moment: in, Define the PCRLB information increment as: in, for The corresponding PCRLB at the time, for The corresponding PCRLB at each moment; In step 4, updating the track based on Kalman filtering includes: (1) One-step prediction of the state: (2) One-step prediction of covariance: (3) If the measurement at the next moment is by the sensor For active detection measurements, the next step in the measurement prediction is: (4) Obtain the state update and covariance update: New covariance: The gain is: State update equation: Covariance update equation: in, It is a linearized state transition matrix. It is the linearized measurement matrix; The error covariance matrix of the process noise. Let be the error covariance matrix of the observation noise.

2. The distributed integrated sensor tracking resource management method according to claim 1, characterized in that, In step 3, the strategy for allocating distributed integrated sensor tracking resources is as follows: (1) Prioritize the targets that need to be tracked and processed in the next moment according to their priority or threat level; (2) Calculate sequentially based on the target priority ranking results. The incremental PCRLB information of each sensor on the target at any given time; (3) Allocate the target tracking sensor resources, that is, select the integrated sensor that maximizes the PCRLB information increment of the target as the tracking sensor. (4) When multiple targets select the same sensor, the target with higher priority will select the sensor resource first, and the other targets will select the second-best sensor as the tracking sensor. (5) Perform Iteration of moments.

3. The distributed integrated sensor tracking resource management method according to claim 2, characterized in that, When the number of tracked targets is greater than the number of sensors, a time-sharing strategy is used to handle simultaneous task requests from multiple targets.

4. A computer terminal storage medium storing computer terminal executable instructions, characterized in that, The computer terminal can execute instructions for performing the distributed integrated sensor tracking resource management method as described in any one of claims 1-3.

5. A computing device, characterized in that, include: At least one processor; The system also includes a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the distributed integrated sensor tracking resource management method as described in any one of claims 1-3.

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