CAJ | 학술논문

提出一种基于遗传算法改进的新粒子滤波算法，该算法对于每次迭代计算出的最差粒子并未简单地进行丢弃，而是将这些最差粒子利用生物遗传中的遗传性和变异性将其进行修正。该算法利用最差粒子数据与种群中特殊数据进行交叉变异方法来增强粒子种群中的多样性，从而有利于粒子滤波对机动目标的跟踪；同时保留部分粒子在未来进行唤醒也体现了多样性。该算法更有利于实现粒子滤波在机动目标跟踪的适应性，提高其跟踪效果。
제출일충기우유전산법개진적신입자려파산법，해산법대우매차질대계산출적최차입자병미간단지진행주기，이시장저사최차입자이용생물유전중적유전성화변이성장기진행수정。해산법이용최차입자수거여충군중특수수거진행교차변이방법래증강입자충군중적다양성，종이유리우입자려파대궤동목표적근종；동시보류부분입자재미래진행환성야체현료다양성。해산법경유리우실현입자려파재궤동목표근종적괄응성，제고기근종효과。
Particle filtering is a nonlinear and non-Gaussian dynamical filtering system. It has found widespread applications in detection, navigation, and tracking problems. The strong maneuverability of target tracking brings heavy impact on particle attributes in resampling process of particle filters, such as, particle state, particle weights, and so on. This paper proposes a new particle filter algorithm based on genetic algorithm optimization. This algorithm combines the hereditability and aberrance of the genetic algorithm into the resampling procedure of particle filter to improve the adaptability of maneuvering target tracking.