Deployment and cooperation scheme evaluation method for multi-source heterogeneous sensors of mobile platform

Abstract

The invention provides a deployment and cooperation scheme evaluation method for multi-source heterogeneous sensors of a mobile platform, and the method comprises the steps: non-cooperative detection target modeling, mobile platform and sensor modeling, deployment and cooperation scheme evaluation system construction, and evaluation weight and parameter updating based on a simulation deduction result. Finally, a deployment and cooperation scheme evaluation result is generated, objective evaluation of effectiveness of a deployment and cooperation use scheme of the mobile platform in a non-cooperative detection task is realized, and quantitative evaluation and optimization iteration of comprehensive efficiency of a mobile platform deployment and cooperation algorithm and system are supported.

Description

Technical Field

[0001] The present invention belongs to the field of tracking technology, and in particular relates to a method for evaluating the deployment and coordination scheme of multi-source heterogeneous sensors on a mobile platform. Background Art

[0002] For target tracking, how to combine and use multi-source heterogeneous sensors is directly related to the timeliness, stability and accuracy of situational awareness. In order to adapt to the changing and complex detection environment, more and more sensors are installed on mobile platforms. How the platform can flexibly select detection points and combinations according to detection needs, and how the integrated multi-source heterogeneous sensors can maximize their synergistic effectiveness, all put forward higher requirements for the deployment of multi-source heterogeneous sensors on mobile platforms and the effectiveness of the evaluation method of the synergistic solution.

[0003] Among domestic and foreign patents, the current focus is on general sensor deployment evaluation methods, which are mainly based on single sensor multi-station deployment, and the evaluation dimensions mainly consider target coverage, detection probability, continuous tracking duration, etc. However, in most detection scenarios, the target and the sensor are in a non-cooperative relationship, and the target position, speed, characteristics, etc. are uncertain. No research has considered full coverage of scene uncertainties, and there is no sensor deployment and collaborative solution evaluation method for non-cooperative detection scenarios.

[0004] For non-cooperative target detection, the core problem that needs to be solved is to ensure stable target discovery, accurate trajectory tracking, and complete feature collection when the target's position, flight trajectory, and characteristic intensity are uncertain. The command factors that need to be decided include: (1) Deployment plan: how many mobile detection platforms need to be mobilized and the utilization rate of sensor resources; (2) Collaboration plan: which sensors are enabled on each mobile platform and how to link the platform and cross-platforms to achieve the effect of 1+1>2; The human-machine efficiency factors that need to be considered include: the configuration flexibility, response time, and interface friendliness of the deployment and collaboration plan. Summary of the invention

[0005] To this end, the present invention proposes a method for evaluating the deployment and collaborative scheme of multi-source heterogeneous sensors on mobile platforms. By constructing a scene boundary for uncertain detection tasks constrained by a non-cooperative detection target set, a mobile platform and sensor model is constructed to characterize the capabilities of a single sensor, the constraints between sensors within the mobile platform, and the collaborative detection effectiveness of sensors across mobile platforms. The method comprehensively evaluates the deployment and collaborative scheme of multi-source heterogeneous sensors on mobile platforms from the dimensions of deployment effectiveness, collaborative effectiveness, and human-machine effectiveness. The method supports updating evaluation weights and parameters through simulation and professional evaluation, and generates deployment and collaborative scheme evaluation results with non-cooperative scene requirements and mobile platform and sensor capabilities as input, thereby achieving an objective evaluation of the effectiveness of collaborative use schemes of mobile platforms in non-cooperative detection tasks. Specifically, the following steps are included:

[0006] (1) Non-cooperative detection target modeling

[0007] A detection target model is constructed based on the currently available information, which consists of a target set and constrains the boundary range of all uncertain target trajectories and multi-source features extracted from the currently available information.

[0008] (2) Mobile platform and sensor modeling

[0009] A mobile platform set is established in the observation space, which is composed of a mobile platform capability set, a sensor model set, and a sensor constraint set. It characterizes the capability of each single sensor in the mobile platform set, the constraints between sensors within the mobile platform, and the collaborative detection effectiveness of sensors across mobile platforms.

[0010] (3) Construction of deployment and collaborative solution evaluation system

[0011] A three-level deployment and coordination scheme evaluation system is formed in a multi-dimensional and multi-level manner. The first-level indicator is the deployment and coordination scheme evaluation index S applied to multi-source heterogeneous sensors on mobile platforms. It has three second-level performance indicators, including deployment effectiveness E1, coordination effectiveness E2, and human-machine effectiveness E3, covering the command elements and human-machine effectiveness requirements that require decision-making. Each second-level indicator is quantified into third-level attribute indicators, and specific data is set to characterize the rationality of command elements and the applicability of human-machine effectiveness. All third-level indicators are normalized to the interval [0,1], and the weighted method is used to construct the deployment and coordination scheme evaluation system.

[0012] (4) Evaluation weights and parameter updates based on simulation results

[0013] Based on the simulation system, the detection scenario that meets the target feature boundary range is simulated, the generated deployment and coordination plan is executed, the simulation data is extracted and the indicators at all levels are calculated, and the weight values ​​of each layer are adjusted by comprehensively considering the domain knowledge, task points, user concerns, etc., as well as the three-level indicator items.

[0014] The beneficial effects of the present invention are

[0015] It achieves an objective evaluation of the effectiveness of collaborative use of mobile platforms in non-cooperative detection tasks, and can support the quantitative evaluation and optimization iteration of deployment and collaborative algorithms. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flowchart of the method of the present invention. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] The problem to be solved by the present invention is to propose a deployment and coordination scheme evaluation method for multi-source heterogeneous sensors on a mobile platform. The method uses non-cooperative scenario requirements and mobile platform and sensor capabilities as inputs to generate deployment and coordination scheme evaluation results. The main steps include: (1) non-cooperative detection target modeling, (2) mobile platform and sensor modeling, (3) deployment and coordination scheme evaluation system construction, and (4) evaluation weight and parameter update based on simulation results. The specific implementation methods of the four steps are as follows:

[0019] (1) Non-cooperative detection target modeling

[0020] The detection target model is constructed based on the currently acquired target information, which consists of the target set Obj. Contains N O targets, each target Obj i It consists of a target trajectory feature set and a target multi-source feature set, wherein the target trajectory feature set includes a time-series position set, a time-series velocity set, etc., and the target multi-source feature set includes a time-series electromagnetic feature set, a time-series optical radiation feature set, a time-series radio signal feature set, etc., which constrain the boundary range of all uncertain target features extracted from the currently available information.

[0021] (2) Mobile platform and sensor modeling

[0022] In the observation space, a mobile platform set MP is established, where the mobile platform set Contains N M Mobile platforms, each with MP j = {PC j ,Sen j ,SR j}By integrating the capabilities of mobile platforms with PC j , sensor model set Sen j , inter-sensor constraint set SR j Composition, including PC j Including position, orientation parameters, etc.; sensor model set Including mobile platform MP j all The model of each sensor is j,kIt includes the sensor position on the platform, the target visible azimuth range, the target visible elevation range, the power model, the accuracy model, the target detection model, etc.; the constraint set SR between sensors j It includes guidance constraint models and electromagnetic compatibility constraint models between related sensors, which characterize the capabilities of each single sensor in the mobile platform, the constraints between sensors within the mobile platform, and the collaborative detection effectiveness of sensors across mobile platforms.

[0023] (3) Construction of deployment and collaborative solution evaluation system

[0024] As shown in Table 1, a deployment and coordination scheme evaluation system is formed in a multi-dimensional and multi-level manner, wherein the first-level indicator is the deployment and coordination scheme evaluation index S applied to multi-source heterogeneous sensors on mobile platforms, and there are 3 second-level performance indicators, including deployment effectiveness E1, coordination effectiveness E2, and human-machine effectiveness E3. Under each second-level indicator, relevant third-level attribute indicators are quantified, and specific data is set to characterize the rationality of command elements and the applicability of human-machine effectiveness. In this embodiment, a total of 1 first-level indicator, 3 second-level indicators, and 11 third-level indicators are set;

[0025] Table 1 Evaluation system for deployment and coordination of multi-source heterogeneous sensors on mobile platforms

[0026]

[0027] Among them, the deployment efficiency E1 is decomposed into the number of mobile platforms used A11 and the sensor resource utilization rate A12, wherein the number of mobile platforms used A11 refers to the number of mobile platforms participating in this solution, and the sensor resource utilization rate A12 refers to the average value of the ratio of the number of sensors actually participating in the detection among all mobile platforms participating in this solution to the number of sensor models of each mobile platform.

[0028] Among them, the synergistic effectiveness E2 is decomposed into target interception probability A21, tracking time A22, tracking position accuracy A23, tracking speed accuracy A24, multi-source feature collection matching degree A25, and platform-to-platform guidance success rate A26. All three-level indicators under the synergistic effectiveness are the coverage results of the platform and equipment status given by the mobile platform set MP on the trajectory and multi-source feature boundaries determined by the target set Obj. Among them, the target discovery probability A21 refers to the minimum value of the discovery probability of this scheme for the target set Obj, the tracking time A22 refers to the minimum value of the tracking time of this scheme for the target set Obj, the tracking position accuracy A23 and the tracking speed accuracy A24 respectively refer to the average values ​​of the position and speed accuracy of the whole detection of the target set Obj by this scheme, the multi-source feature collection matching degree A25 refers to the ratio of the cumulative number of multi-source features that can be collected by this scheme for the target set Obj to the number of detection requirements, and the platform-to-platform guidance success rate A26 refers to the minimum value of all handover guidance success rates when this scheme relays detection of the target set Obj across mobile platforms.

[0029] Among them, the human-machine efficiency E3 is decomposed into the time consumption for generating a plan A31, the flexibility of plan configuration A32, and the friendliness of interface operation A33. The time consumption for generating a plan A31 refers to the time from the beginning of formulating a deployment and collaboration plan to the generation of an emergency plan. The flexibility of plan configuration A32 refers to the user's evaluation of the target parameter selection and construction, mobile platform and sensor selection and configuration supported by the deployment and collaborative planning software obtained through a questionnaire. The friendliness of interface operation A33 refers to the user's evaluation of the ease of use, display clarity, process completeness, etc. of the deployment and collaborative planning software obtained through a questionnaire.

[0030] All the three-level indicators are normalized to the interval [0,1], and the weighted method is used to build a deployment and coordination scheme evaluation system:

[0031] S=w1E1+w2E2+w3E3

[0032]

[0033] (4) Evaluation weights and parameter updates based on simulation results

[0034] Based on the simulation system, the Monte Carlo method is used to simulate the detection scenario target set Obj that meets the target feature boundary range, execute the generated deployment and coordination plan, extract the simulation data and calculate the indicators at all levels, and comprehensively consider the domain knowledge, task points, user concerns, etc. to adjust the weight values ​​w and k of each layer, as well as the three-level indicator items.

[0035] The present invention is not limited to the above specific implementations, and the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made to the above implementations based on the technical essence of the present invention shall be included in the protection scope of the present invention.

Claims

1. A deployment and coordination scheme evaluation method for multi-source heterogeneous sensors on a mobile platform, characterized by: Using non-cooperative scenario requirements and mobile platform and sensor capabilities as input, generate deployment and collaboration scheme evaluation results; The specific steps include: (1) Non-cooperative detection target modeling Construct a detection target model based on the currently available information, which consists of a target set and constrains the boundary range of all uncertain target trajectories and multi-source features extracted from the currently available information; (2) Mobile platform and sensor modeling Establish a mobile platform set in the observation space, which consists of a mobile platform capability set, a sensor model set, and a sensor constraint set, to characterize the capabilities of each single sensor in the mobile platform set, the constraints between sensors within the mobile platform, and the collaborative detection effectiveness of sensors across mobile platforms; (3) Construction of deployment and collaborative solution evaluation system A three-level deployment and coordination scheme evaluation system is formed in a multi-dimensional and multi-level manner. The first-level indicator is the deployment and coordination scheme evaluation index S applied to multi-source heterogeneous sensors on mobile platforms. It has three second-level performance indicators, including deployment effectiveness E1, coordination effectiveness E2, and human-machine effectiveness E3, covering the command elements and human-machine effectiveness requirements that require decision-making. Under each second-level indicator, the third-level attribute indicators are quantified, and specific data is set to characterize the rationality of the command elements and the applicability of human-machine effectiveness. All the third-level indicators are converted into the [0,1] interval through the normalization method, and the deployment and coordination scheme evaluation system is constructed using the weighted method. (4) Evaluation weights and parameter updates based on simulation results Based on the simulation system, the detection scenario that meets the target feature boundary range is simulated, the generated deployment and coordination plan is executed, the simulation data is extracted and the indicators at all levels are calculated, and the weight values ​​of each layer are adjusted by comprehensively considering the domain knowledge, task points, user concerns, etc., as well as the three-level indicator items.

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