关键词:

多传感器, 数据融合, 粒子群优化算法, 惯性权值, 权值递减策略

Abstract:

Particle swarm optimization (PSO) is an effective evolutionary method which is used to search the function extreme. It is simple and has fast convergence, but the convergence accuracy of this algorithm is not high, and it can easily fall into the local extreme points. The effect of inertia weight in PSO was analyzed. Motivated by the idea of power function, a new non-linear strategy for decreasing inertia weight (DIW) was proposed based on the existing linear DIW . Then a novel hierarchical multisensor data fusion algorithm adopting this strategy was presented and the weight factor of the data fusion was estimated. The distinctive feature of this algorithm was its capability of fusing data in a near optimal manner when no information about the reliability of the information sources, the degree of redundancy/complementarities of the information sources and the hierarchy structure is available. It obtained the effective information from the fusion data, removed the noise disturbance successfully and got the favorable results.

Key words:

多传感器, 数据融合, 粒子群优化算法, 惯性权值, 权值递减策略