On multiple-model extended target multi-Bernoulli filters

In this paper, we propose a multiple-model (MM) version of the extended target multi-Bernoulli (ET-MB) filter for estimating multiple maneuvering extended targets. A Gaussian mixture (GM) implementation of the MM-ET-MB filter for linear Gaussian models and a sequential Monte Carlo (SMC) implementation of the MM-ET-MB filter for nonlinear models are presented. Two numerical examples are provided to verify the effectiveness of the MM-ET-MB filter for estimating multiple maneuvering extended targets.     

Extensions of the CBMeMBer filter for joint detection,tracking, and classification of multiple maneuvering targets

This paper addresses the problem of joint detection, tracking and classification (JDTC) of multiple maneuvering targets in clutter. The multiple model cardinality balanced multi-target multi-Bernoulli (MM-CBMeMBer) filter is a promising algorithm for tracking an unknown and time-varying number of multiple maneuvering targets by utilizing a fixed set of models to match the possible motions of targets, while it exploits only the kinematic information. In this paper, the MM-CBMeMBer filter is extended to incorporate the class information and the class-dependent kinematic model sets. By following the rules of Bayesian theory and Random Finite Set (RFS), the extended multi-Bernoulli distribution is propagated recursively through prediction and update. The Sequential Monte Carlo (SMC) method is adopted to implement the proposed filter. At last, the performance of the proposed filter is examined via simulations.     

图像目标检测前跟踪的广义多伯努利滤波算法

针对目标影响区域重叠时的图像目标检测前跟踪问题,推导了基于多伯努利滤波器的多目标联合检测与跟踪算法.在分析多个目标叠加条件下观测似然函数的基础上,利用预测得到的目标状态对观测似然函数进行估计,从而消除目标叠加对观测更新带来的影响.该方法在目标预测与跟踪阶段皆保持了目标状态的多伯努利分布特性,是较为严格意义上的多伯努利多目标滤波器,可应用于一般图像观测条件下(目标重叠或非重叠)的目标检测前跟踪.给出了该算法的实现步骤,并通过加标签的方法,更准确地实现目标轨迹提取和虚假目标剔除,最后通过计算机仿真实验验证了所提算法的有效性.     

An efficient measurement-driven sequential Monte Carlo multi-Bernoulli filter for multi-target filtering

We propose an efficient measurement-driven sequential Monte Carlo multi-Bernoulli （SMC-MB） filter for multi-target filtering in the presence of clutter and missing detection. The survival and birth measurements are distinguished from the original measurements using the gating technique. Then the survival measurements are used to update both survival and birth targets, and the birth measurements are used to update only the birth targets. Since most clutter measurements do not participate in the update step, the computing time is reduced significantly. Simulation results demonstrate that the proposed approach improves the real-time performance without degradation of filtering performance.     

弱目标箱粒子标签多伯努利多目标检测与跟踪算法

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%。