Asynchronous multi-sensor bias estimation with sensor location uncertainty

Authors:
Suo Xiaofeng;Chen Li;Sheng Andong
Affiliations:
Automation School, Nanjing University of Science & Technology, Nanjing;Automation School, Nanjing University of Science & Technology, Nanjing;Automation School, Nanjing University of Science & Technology, Nanjing
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

Citing 2
Cited 0

Estimation with Applications to Tracking and Navigation

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Simultaneous registration and fusion of multiple dissimilar sensors for cooperative driving

IEEE Transactions on Intelligent Transportation Systems

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Abstract

In multi-sensor systems, a practical problem is that the target data reported by the sensors are usually not time-coincident or synchronous due to the different data rates. In addition, for mobile sensors, their location might not be perfectly known. This paper presents a new algorithm for multisensor bias estimation in asynchronous sensors with sensor location uncertainty. This algorithm is based on a Kalman filter combined with pseudo-measurement and equivalent bias to estimate both the range and azimuth biases. The Simulation results show the Cramer-Rao Lower Bound (CRLB) is achievable. This means the proposed estimation algorithm is statistically efficient.