CAJ | 학술논문

针对基于粒子群优化的粒子滤波(PSO-PF)算法精度不高,实时性差,难以满足雷达机动目标跟踪的需求,提出一种基于动态邻域自适应粒子群优化的粒子滤波(DPSO-PF)算法.该算法可以动态调整粒子邻域环境,其中每个粒子按照邻域的环境和自身的位置信息自适应地调整相互间的邻域粒子数量,使邻域粒子数量更为合理,达到寻优能力与收敛速度的最佳平衡.最后利用不同模型对该算法进行了仿真实验,实验结果表明所提出的算法能够提高雷达机动目标跟踪的实时性和精确性.
침대기우입자군우화적입자려파(PSO-PF)산법정도불고,실시성차,난이만족뢰체궤동목표근종적수구,제출일충기우동태린역자괄응입자군우화적입자려파(DPSO-PF)산법.해산법가이동태조정입자린역배경,기중매개입자안조린역적배경화자신적위치신식자괄응지조정상호간적린역입자수량,사린역입자수량경위합리,체도심우능력여수렴속도적최가평형.최후이용불동모형대해산법진행료방진실험,실험결과표명소제출적산법능구제고뢰체궤동목표근종적실시성화정학성.
A particle filter algorithm based on dynamic neighborhood adaptive particle swarm optimization(DPSO-PF) is presented in order to solve the problem of the low precision and complicated calculation of particle filter based on particle swarm optimization(PSO-PF) algorithm. This algorithm can dynamically adjust the particle neighborhood environment, where each particle can adjust the number of particles in the neighborhood based on self-adaptation basis according to the neighborhood environment and their own position information, accordingly a best balance is achieved between optimal seeking and convergence rate. Finally, different models are used for simulation experiment and the results show that the proposed algorithm improves the real-time performance and the precision of maneuvering target tracking by using radar.