摘要
为了加快无源定位的速度,提高定位精度,针对标准粒子滤波中的重要性函数和重采样所导致的样本枯竭问题,本文结合遗传算法和粒子滤波算法,提出一种改进的的粒子滤波算法,该算法优化了粒子在状态空间的分布特性,增加了样本的多样性,克服了重采样过程中的粒子退化问题,并针对二维平面机动模型进行仿真。仿真实验表明,本文算法能够适用于机载无源定位系统,能够有效的提高滤波精度,跟踪性能优于经典的粒子滤波算法。
In order to accelerate the rate of passive positioning, improve positioning accuracy for classical particle filter in the sample depletion importance sampling function and punch caused, this paper genetic algorithm and particle filter algorithm, an improved particle filtering algorithm the algorithm optimizes the distribution characteristics of the particles in the state space, increasing the diversity of the sample to overcome the re-sampling process of particle degradation, and the simulation model for the two-dimensional plane maneuvering. Simulation results show that the algorithm can be applied to airborne passive positioning system that can effectively improve the filtering accuracy, tracking performance is better than the classical particle filter.
出处
《电子设计工程》
2016年第5期107-109,共3页
Electronic Design Engineering
关键词
粒子滤波
重采样
遗传算法
无源定位
particle fiher
re-sampling
genetic algorithms
passive location
