CAJ | 학술논문

针对经典自举粒子滤波中的重要性函数选取和重采样所导致的样本枯竭问题，提出了一种基于进化裂变的改进粒子滤波算法。该算法首先采用无迹卡尔曼滤波算法产生重要性函数，然后对重要性采样粒子进行裂变通过进化策略更新粒子集以增加粒子多样性，从而克服经典自举滤波重采样过程中的粒子退化问题。仿真实验表明，该算法能有效地提高跟踪精度，跟踪性能优于经典粒子滤波算法。
침대경전자거입자려파중적중요성함수선취화중채양소도치적양본고갈문제，제출료일충기우진화렬변적개진입자려파산법。해산법수선채용무적잡이만려파산법산생중요성함수，연후대중요성채양입자진행렬변통과진화책략경신입자집이증가입자다양성，종이극복경전자거려파중채양과정중적입자퇴화문제。방진실험표명，해산법능유효지제고근종정도，근종성능우우경전입자려파산법。
In order to overcome the choice of the importance function and the sample impoverishment after resampling,an improved particle filter algorithm based on evolution fission is presented.The algorithm uses the UKF to generate the importance function and updates the samples based on evolution fission, which could increase the diversity of the samples and overcome the degeneration of the typical particle filter. Simulation results prove that the presented algorithm has a high tracking accuracy and a good tracking performance compared to the typical particle filter.