Target Positioning Accuracy Improvement Method Based on Adaptive Genetic Algorithms Particle Filter
In order to solve the problem that traditional genetic algorithm particle filter is easy to fall into local optimization due to constant genetic operation parameters,an adaptive method is introduced into genetic algorithm particle filter,and an adap-tive genetic algorithm particle filter is proposed.The principle of dominant inheritance changes the probability of crossover and muta-tion with the change of particle fitness,so as to retain the dominant particles as much as possible while generating new dominant par-ticles more effectively,jumping out of local optimum.The adaptive genetic algorithm particle filter is applied to the established dy-namic state space model,compared with the performance of the genetic algorithm particle filter by simulation.The results show that the introduction of adaptive method can increase the effective particle number of the algorithm,effectively solve the problem of pre-mature algorithm and improve the filtering accuracy,which is very effective for improving the accuracy of dynamic target position-ing.
dynamic state space modeladaptivetarget positioninggenetic algorithmparticle filter
国家科技期刊平台
NSTL
万方数据
