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.