扩展目标联合检测与估计的误差界

连峰; 马冬冬; 韩崇昭

doi:10.7652/xjtuxb201404002

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扩展目标联合检测与估计的误差界

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- 扩展目标联合检测与估计的误差界
- Error Bound for Joint Detection and Estimation for Extended Target
- 西安交通大学学报 2014年48卷第4期页码：8-14
- 作者机构：
  
  西安交通大学智能网络与网络安全教育部重点实验室,西安,710049
- 作者简介：
- 基金信息：
  
  : 2013-10-10。作者简介: 连峰(1981—),男,副教授。基金项目: 国家重点基础研究发展规划资助项目(2013CB329405);国家自然科学基金优秀创新研究群体资助项目(61221063);国家自然科学基金资助项目(61004087);中国博士后科学基金特别资助项目(2012T50806);中央高校基本科研业务费专项资金资助项目(xjj2011033)
- DOI：10.7652/xjtuxb201404002
  中图分类号： TP393
- 网络首发：2014-04-10，
  
  纸质出版：2014
- 稿件说明：
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连峰, 马冬冬, 韩崇昭. 扩展目标联合检测与估计的误差界[J]. 西安交通大学学报, 2014,48(4):8-14.

Error Bound for Joint Detection and Estimation for Extended Target[J]. 2014, 48(4): 8-14.
连峰, 马冬冬, 韩崇昭. 扩展目标联合检测与估计的误差界[J]. 西安交通大学学报, 2014,48(4):8-14. DOI： 10.7652/xjtuxb201404002.

Error Bound for Joint Detection and Estimation for Extended Target[J]. 2014, 48(4): 8-14. DOI： 10.7652/xjtuxb201404002.

摘要

针对杂波和漏检环境下单个扩展目标联合检测与估计的性能评价问题

提出了一种基于随机有限集(Random Finite Set

RFS)的误差分析方法。该方法通过将扩展目标的状态和观测分别建模为Bernoulli RFS和Poisson RFS

在RFS框架下推导获得了采用最大后验概率检测器和无偏估计器的均方误差界

并给出了其在目标确定存在以及无杂波条件下的简化形式。实验结果表明

建议的均方误差界能够有效地反映扩展目标联合检测与估计算法所能达到的最优性能

利用该误差界可以对不同的扩展目标联合检测与估计算法的性能进行有效的衡量

误差在5%以内。推导过程和结论仅关注于单传感器扩展目标联合检测与估计的静态问题

并假设其状态和测量均为标量。

Abstract

An error analysis strategy with random finite set(RFS)is proposed to address the performance evaluation for joint detection and estimation of an extended target with clutters and missed detections. Modeling the state and observations of the extended target as the Bernoulli RFS and Poisson RFS respectively

a mean square error(MSE)bound restricted to maximum a posterior detector and unbiased estimator is derived. Experiments show that the proposed MSE bound can effectively indicate the performance limits of the extended-target joint detection-estimation algorithms and the error is less than 5%.

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Keywords

references

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