基于高斯混合概率假设密度滤波的扫描型光学传感器像平面多目标跟踪算法

Gaussian-mixture Probability Hypothesis Density Filter Based Multitarget Tracking Algorithm for Image Plane of Scanning Optical Sensor

高斯混合概率假设密度(GM-PHD)滤波是一种基于随机有限集理论的次优贝叶斯多目标跟踪方法,本文研究了该算法在扫描型光学传感器像平面的多目标跟踪问题。针对典型的锥扫模式和推扫模式,根据其扫描特性建立目标的运动模型和测量模型。介绍高斯混合概率假设密度滤波的基本原理,针对原算法在强杂波环境中的低效率问题,借鉴传统多目标跟踪方法中的波门技术,提出相应的改进措施。最后构建包含交叉目标、并行目标和相对运动目标等情况的多目标场景,对两种扫描模式分别进行了Monte Carlo仿真,结果表明本文提出的算法能够适应目标个数变化,抑制杂波的能力强,且改进后算法效率提高了约10倍。

Gaussian-mixture probability hypothesis density（GM-PHD） filter is a suboptimal Bayesian method for multitarget tracking based on random finite Set theory.This article proposes a GM-PHD based multitarget tracking algorithm for the image plane of a scanning optical sensor.By analyzing the scan characteristics,a target dynamic model and an observation model are established respectively for the typical cone scan type and shave scan type.The principle of GM-PHD is introduced.To handle the low efficiency problem of the original GM-PHD in high clutter density circumstances,some improvements are presented by using the gating technology in traditional multitarget tracking methods.Finally,A multitarget scenario is set up,which contains crossing targets,parallel targets and approaching targets,Monte Carlo simulations are used for the two scan types mentioned above,and the results show that the new algorithm is able to adapt to time-varying number of targets,depress clutters strongly,and enhance efficiency by 10 times as compared with the original method.