群目标联合检测与估计误差界的递推形式

连峰; 吕宁; 韩崇昭

doi:10.16383/j.aas.2015.c140794

群目标联合检测与估计误差界的递推形式

doi: 10.16383/j.aas.2015.c140794

1.
西安交通大学电子与信息工程学院智能网络与网络安全教育部重点实验室西安 710049

基金项目:

国家重点基础研究发展计划(973计划)(2013CB329405),国家自然科学基金(61473217),陕西省自然科学基金(2014JQ8333)

详细信息

作者简介:
吕宁西安交通大学综合自动化研究所博士研究生. 主要研究方向为信息融合理论及应用.E-mail: lvning1108@aliyun.com

通讯作者:
连峰西安交通大学综合自动化研究所副教授.主要研究方向为为信息融合与目标跟踪.本文通信作者.

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出版历程
- 收稿日期: 2014-11-18
- 修回日期: 2015-06-13
- 刊出日期: 2015-12-20

The Recursive Form of Error Bound for Joint Detection and Estimation of Groups

1.
Ministry of Education Key Laboratory for Intelligent Networks and Network Security (MOE KLINNS), School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an 710049

Funds:

Supported by National Basic Research Program of China (973 Program) (2013CB329405), National Natural Science Foundation of China (61473217), and Natural Science Foundation of Shaanxi Province (2014JQ8333)

摘要

摘要: 在随机有限集框架下给出了当杂波和漏检存在时,群目标联合检测与估计(Joint detection and estimation, JDE)误差界的递推形式. 首先,将多个群目标运动过程建模为一个多Bernoulli过程, 并采用连续个体目标数假设建模群目标观测似然函数; 其次,采用最优子模式分配距离定义群目标JDE误差; 最终,利用信息不等式推导获得了建议的误差界. 仿真实验在不同杂波密度和检测概率场景下利用群势概率假设密度和群势平衡多目标多Bernoulli滤波器对该误差界的有效性进行了验证.
- 误差界 /
- 群目标跟踪 /
- 联合检测与估计 /
- 随机有限集
Abstract: Within the random finite set framework, this paper derives the recursive form of error bound for joint detection and estimation (JDE) of groups in the presence of clutter and missed detection. First, the dynamic of multiple groups is modeled as a multi-Bernoulli process and the group observation likelihood is modeled based on the concept of continuous individual target number. Then, the optimal sub-pattern assignment distance is used to define the JDE error of the groups. Finally, the proposed bound is derived in terms of information inequality. Given various clutter density and probabilities of detection, the effectiveness of the bound is verified through simulation by indicating the performance limitations of the cardinalized probability hypothesis density and cardinality balanced multi-target multi-Bernoulli filters for the groups.
- Error bounds /
- groups tracking /
- joint detection and estimation /
- random finite set

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参考文献(17)

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