摘要
针对传统粒子滤波器的粒子退化和贫乏问题,提出基于进化思想的新遗传粒子滤波算法.将算术相加,快速Metropolis-Hastings移动作为遗传算法的交叉和变异算子,与赌轮选择一起作为粒子滤波重采样的一类方法.将新遗传粒子滤波用于红外弱小目标的跟踪与检测,利用目标的幅度特性和运动特性,进行多帧图像滤波输出的似然比假设检验,来判断目标是否存在.仿真实验结果表明,基于快速Metropolis-Hastings变异的遗传重采样方法可以快速提高粒子的多样性,避免粒子退化;所提出的检测方法在虚警率为10-3,信噪比为2.0时,检测概率可以达到98.5%,检测性能明显优于传统重采样粒子滤波算法.
Crucial issues in a particle filter（PF） are to remove the degeneracy phenomenon and alleviate the sample impoverishment problem. In this paper, by using techniques from a genetic algorithm we propose some modifications to solve these problems simultaneously. A genetic algorithm with arithmetic crossover, fast Metropolis-Hastings mutation and the roulette wheel selection improves resampling procedures for the standard particle filter. The new particle filter is developed for IR dim target tracking and detection. By using multi-frame target amplitude and motion features some values of the filter＇s output are used to approximately construct the likelihood ratio for hypothesis test in the detection stage. Simulation results show that genetic resampling based on fast Metropolis-Hastings can produce various particles and remove the degeneracy phenomenon. For some actual image sequences with an SNR of 2.0, successful detection probability reaches 98. 5% with 10^-3 false alarm probability. Experimental results show that the performance of the proposed algorithm is better than that of traditional resampling particle filter algorithms.
出处
《西安电子科技大学学报》
EI
CAS
CSCD
北大核心
2009年第4期619-623,644,共6页
Journal of Xidian University
基金
国家自然科学基金资助(60677040)
