弱目标箱粒子标签多伯努利多目标检测与跟踪算法
投稿时间:2018-08-03  修订日期:2018-10-15  点此下载全文
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作者单位E-mail
蔡如华 桂林电子科技大学数学与计算科学学院 934019492@qq.com 
杨 标 桂林电子科技大学数学与计算科学学院 13677736552@163.com 
吴孙勇 桂林电子科技大学数学与计算科学学院 wusunyong121991@163.com 
李 瞳 桂林电子科技大学数学与计算科学学院  
孙希延 广西精密导航技术与应用重点实验室  
基金项目:国家自然科学基金(61561016);广西自然科学基金(2016GXNSFAA380073, 2014GXNSFAA118352, 2014GXNSFBA118280)
中文摘要:针对红外弱目标追踪问题,提出箱粒子标签多伯努利多目标检测与追踪(Box particle Labeled Multi-Bernoulli Detection and Tracking, BOX-LMB-DT)算法,该算法首先通过使用均值滤波对获得的灰度图像进行降噪处理;其次,通过将所有像素处依强度大小进行排序,选出强度较大的区域作为当前时刻的区间量测;最后利用箱粒子标签多伯努利滤波(Box-Labeled Multi-Bernoulli Filter, Box-LMB)器对目标进行跟踪。仿真结果表明,本文所提箱粒子标签多伯努利多目标检测与追踪算法能够对多目标的航迹和状态进行稳定有效的跟踪,且在相同条件下,相较于区间量测下的LMB粒子滤波,达到相同的追踪性能时BOX-LMB滤波运算效率提升了22.59%。
中文关键词:多目标追踪  红外图像量测  箱粒子滤波  标签多伯努利滤波  均值滤波
 
Weak Targets Box Particle Labeled Multi-bernoulli Multi-target Detection and Tracking Algorithm
Abstract:For the problem of tracking infrared weak targets, a box particle labeled multi-bernoulli multi-target detection and tracking algorithm is proposed. To begin with, the algorithm using the mean filter to denoise the grayscale image, Then, the region with higher intensity is selected as the interval measurement at current time by sorting the intensity of all the pixels, Finally, the box particle labeled multi-bernoulli filter is applied to tracking. Simulation are presented to demonstrate that the BOX-LMB-DT algorithm has stable, effective performance. In the same conditions, compared with the LMB particle filter under interval measurement, the operation efficiency of the BOX-LMB filtering is improved by 22.59% when the same tracking performance is achieved.
keywords:Muti-Target  Tracking, Infrared  Image Measurement, Box  Particle Filter, Labeled  Multi-Bernoulli  Filter, Mean  Filter
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