Adaptive hybrid target tracking method in a wireless sensor network

Abstract

The invention provides an adaptive hybrid target tracking method in a wireless sensor network, Based on particle swarm optimization and genetic algorithm, Chaotic dynamic adaptive strategy is introduced to adjust the inertia weight of particle swarm optimization algorithm, and then the optimized particle swarm optimization algorithm and double mutation adaptive genetic algorithm are combined to improve the searching effect and realize fast target tracking in WSN, especially in occlusion, noise and other disturbances. The problem of local optimal premature convergence and tracking accuracy aresolved to improve the accuracy and real-time performance of target tracking. When the infinite sensor network is used in target tracking, the target moves quickly and is disturbed by the surrounding environment, such as occlusion. The trajectory of the target is complex and diverse, with uncertainty and time-varying.

Description

technical field [0001] The invention belongs to the technical field of wireless sensor network cluster intelligence, in particular to an adaptive mixed target tracking method in a wireless sensor network. Background technique [0002] Wireless Sensor Networks (WSN, Wireless Sensor Networks) is a distributed sensor network, the end of which is a sensor that can perceive and inspect the outside world, and forms a multi-hop self-organizing network through wireless communication. With the development of wireless sensing technology, the research of target tracking in WSN has been widely concerned, and it is of great significance to study the target tracking method suitable for WSN. [0003] The target tracking state system is a complex nonlinear system that needs to continuously capture target features. It can be understood as a space-time combined target state estimation based on the initial state of the target and the extracted target features. At present, the particle filter ...

AI Technical Summary

Problems solved by technology

[0005] First, for WSN, since a single sensor node has energy and processing capacity limitations, and is susceptible to interference from the surrounding environment, the tracking task usually requires real-time performance. Many traditional target tracking methods are not suitable for WSN-based target tracking.

[0006] Second, a large number of particles are required for target tracking, so the current tracking method has a large amount of calculation, and the input of the target system is a high-dimensional vector, so it is a difficult problem to achieve real-time performance

[0007] Third, due to occlusion, noise, and particle degradation, that is, the impact of poor diversity, the tracking effect becomes poor, and it is easy to fall into a local optimum, which affects the accuracy of tracking. Accurate tracking is the main research content at present, and it has not yet been fully resolved. problem

Images