知识辅助的多模型机动目标跟踪算法
Knowledge-Aided Multi-model Maneuvering Target Tracking Algorithm

多模型机动目标跟踪技术是一种先进的目标跟踪算法。由于目标类型越来越多、运动环境越来越复杂，仅使用位置量测进行目标跟踪变得越来越难以满足应用要求。除位置量测之外，引入目标和环境相关的知识，对多模型算法中的模型集、转移概率矩阵和模型概率这3个关键因素进行自适应调整，可以有效提高机动目标跟踪性能。本文对知识辅助多模型机动目标跟踪算法的原理和方法等进行了分析。按照知识作用的对象（模型集、转移概率矩阵和模型概率）和作用方式（智能法和非智能法）分别介绍了该类算法的原理及其特点,最后对该类算法下一步的研究方向和发展趋势进行了展望。
Multi-model algorithm is the state-of-the-art approach to maneuvering target tracking. Due to the increasing types of targets and complication of motion environment, it is more and more difficult to meet the target tracking requirements by only using the position measurement. In addition to the position measurement, the knowledge about targets and their environment can be adopted to adaptively adjust the three key factors, i.e., the model set, transition probability matrix and model probability in the multi-model algorithm to achieve better performance. This paper analyzes the principles and methods of knowledge-aided multiple-model maneuvering target tracking algorithm. According to the subjects (model set, transition-probability matrix and model probability) that the knowledge being used to adjust, the adjustment manner (intelligent methods and non-intelligent methods), the principles and characteristics of adjustments are introduced, respectively. Finally, future research of knowledge-aided multi-model maneuvering target tracking algorithm is given.