Research On Multi-sensor Optimization Deployment Technology For Ground-based Reconnaissance System

Sensor deployment aims to finish specific combat tasks and adjusts the deployment position of sensors in real-time based on certain optimization criteria to obtain the optimal measurement values of required indicators,such as coverage range,effective coverage,detection probability,etc.Compared with general sensor management,the focus of sensor deployment is to control the geographic location of the sensor at each moment,including the direction and distance of the next moment’s maneuver.This paper focuses on the collaborative deployment issue of multiple sensors within the multi-sensor management framework of the ground reconnaissance system,based on complex operational environments and large-scale mission areas.The goal of this study is to further enrich and optimize the deployment methods and content of multiple sensors in the ground reconnaissance system,in order to obtain maximum operational effectiveness when sensor resources are limited.This research holds significant theoretical and engineering practical value for the future construction of integrated command and control systems.The main work and innovation points are as follows:(1)Analyze and summarize the relevant theories of multi-sensor deployment in ground-based investigation systems.Firstly,the basic theory of sensor deployment is summarized including the typical sensors in reconnaissance system,the basic framework of deployment and the principle of deployment.Then,the common optimization algorithm used in sensor scheduling are analyzed,mainly including particle swarm optimization algorithm,quantum particle swarm optimization algorithm,etc.(2)A fast visual domain analysis algorithm based on angular resolution(AR-FVDA)under complex terrain is proposed.Aiming at the problem of deployment geographic environment complexity by sensors,a sensor detection model is designed with real complex terrain,and a fast viewshed analysis algorithm for determine sensor coverage region based on angular resolution is built.Experiment results show that compared with the traditional viewshed analysis algorithm,the improved algorithm can greatly improve the solving speed at high precision,and be closer to the actual battlefield environment,more in line with the actual working state of sensors,and more practical.(3)A multi-scale quantum particle swarm method for multi-sensor deployment(MS-QPSO)for area coverage is proposed.Aiming at the problem of high computational complexity and time-consuming by multi sensor optimized deployment in large combat area,a sensor deployment model is designed with geographical environment limitations,mobility limitations,and deployment distance limitations.Then,a phased optimized strategy based on multi-scale is presented and a new multiple sensors deployment optimization method is proposed.The simulation results show that the accuracy and real-time indicators of coverage calculation of the proposed method are significantly improved when the reconnaissance mission area is large,and the sensor deployment scheme is more scientific and reasonable.(4)An adaptive MS-QPSO method is put forward.In view of the problem that there are differences in the working performance of different sensor and different deployment schemes in actual combat,an adaptive sensor deployment strategy based on sensor performance is proposed,and a multi-sensor and multi-scale adaptive optimization deployment method is realized.Experiment results show that the improved method can be well applied to large-scale high-precision DEM terrain data,and the obtained multi-sensor deployment scheme is scientific and reasonable.At the same time,it is also closer to the actual combat scenario and has higher practical application value.In summary,this paper aims at the optimal deployment of land-based multi-sensors in actual combat,and fully considers the geographical environmental impact.Based on equipment performance and combat requirements,three multi-sensor deployment method(AR-FVDA,MS-QPSO and adaptive MS-QPSO)were proposed,and the effectiveness of the proposed methods are verified by simulation experiments.The research results have important reference value for the improvement of the theoretical methods of sensor deployment and the auxiliary decision-making of sensor deployment in actual combat.