Multisensor-Multitarget Bias Estimation for General

This paper provides the exact solution for the bias estimation problem in multiple asynchronous sensors using common targets of opportunity. The target data reported by the sensors are usually not time-coincident or synchronous due to the di#erent data rates. Since the bias estimation requires time-coincident target data from di#erent sensors, a novel scheme is used to transform the measurements from the di#erent times of the sensors into pseudomeasurements of the sensor biases with additive noises that are zero-mean, white and with easily calculated covariances. These results allow bias estimation as well as the evaluation of the Cramer-Rao Lower Bound (CRLB) on the covariance of the bias estimate, i.e., the quantification of the available information about the biases in any scenario. Monte Carlo simulation results show that the new method is statistically e#cient, i.e., it meets the CRLB