Asynchronous Multi-sensor Bias Estimation with Sensor Location Uncertainty
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.
asynchronous sensors sensor location uncertainty bias estimate pseudo-measurement equivalent bias Kalman filter.
Suo Xiaofeng Chen Li Sheng Andong
Automation School, Nanjing University of Science & Technology, Nanjing 210094
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
4317-4322
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）