General solution for asynchronous sensors bias estimation

In multisensor systems, the measurements reported by local sensors are usually not time aligned or synchronous due to different data rates. A novel algorithm, based on Kalman filter combined with pseudomeasurement and equivalent bias, is proposed to solve a general bias estimate problem in asynchronous sensors systems. The pseudomeasurement equation of sensor biases is obtained by linearizing the last measurements provided by asynchronous sensors to remove the target state. The equivalent bias equation in each sampling interval of fusion center is derived from the bias dynamic equation of asynchronous sensors with different rates. Monte Carlo simulation results show that the Cramer-Rao lower bound (CRLB) is achievable, i.e., the new algorithm is statistically efficient.