Adaptive hybrid target tracking method in a wireless sensor network

A wireless sensor and mixed target technology, applied in the field of wireless sensor network cluster intelligence, can solve the problems of being easily disturbed by the surrounding environment, large amount of calculation, and tracking accuracy, etc., achieve strong global search ability and local search ability, and improve accuracy performance and real-time performance, improving search performance

Pending Publication Date: 2019-02-15
JIANGSU OPEN UNIV
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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,

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  • Adaptive hybrid target tracking method in a wireless sensor network
  • Adaptive hybrid target tracking method in a wireless sensor network
  • Adaptive hybrid target tracking method in a wireless sensor network

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[0023] The following description and drawings fully illustrate specific embodiments of the present invention to enable those skilled in the art to practice them. Other implementations may include structural, logical, electrical, process, and other changes. The examples only represent possible changes. Unless explicitly required, individual components and functions are optional, and the order of operations can be changed. Parts and features of some embodiments may be included in or substituted for parts and features of other embodiments. The scope of the embodiments of the present invention includes the entire scope of the claims, and all available equivalents of the claims.

[0024] Such as figure 1 As shown, in some demonstrative embodiments, an adaptive hybrid target tracking method in a wireless sensor network is provided, including:

[0025] 101: Establish a particle swarm algorithm model and initialize the parameters of the particle swarm algorithm.

[0026] The particle swa...

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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 ...

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Application Information

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IPC IPC(8): G06N3/00G06N3/12H04W4/029
CPCG06N3/006G06N3/126H04W4/029
Inventor 姜春艳陈希钱琦勾荣刘伟彦
Owner JIANGSU OPEN UNIV
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