Exact Multisensor Dynamic Bias Estimation with Local Tracks

This paper provides the exact solution of multiple sensor bias estimation problem based on local tracks. It is shown that the bias estimate can be obtained dynamically using the outputs of the local (biased) state estimators. This is accomplished by manipulating the local state estimates such that they yield pseudomeasurements of the sensor biases with additive noises that are zero-mean, white and with easily calculated covariances. These results allow 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 simulations show that this method has significant improvement of performance with reducing the RMS errors about 60–80% comparing to the commonly used decoupled Kalman filtering method. fur-thermore, the new method is shown be statistically efficient, i.e., it meets the CRLB. The extension of the new technique for dynamically varying biases is also presented.