A Cramer-Rao Lower Bound for the Estimation of Bias with a Single Bearing-Only Sensor

被引：0
|
作者：
Martin, Sean R. [1 ]
Abernathy, Matthew R. [1 ]
Moshtagh, Nima [1 ]
机构：
[1] Johns Hopkins Univ, Appl Phys Lab, Laurel, MD 20723 USA
来源：
PROCEEDINGS OF 2020 23RD INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2020) | 2020年
关键词：
bias; Kalman filter; Cramer-Rao Lower Bound; Observability; Multi-target Tracking;
D O I：
10.23919/fusion45008.2020.9190625
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a metric for finding optimal sensor and target geometries that provide accurate estimates of bias during target tracking with a single sensor taking measurements of bearing. Since the bias cannot be measured directly, it is shown how to manipulate the equations of a Kalman filter to produce a pseudo measurement of bias and its associated measurement error covariance. These measurement error covariances are used to form a Cramer-Rao lower bound (CRLB) on the bias estimation variance as a function of sensor and target geometries. It is shown that highly accurate estimates of bias can be produced using a single sensor, even if the kinematic state estimate of the target is poor.
引用
收藏
页码：677 / 683
页数：7
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