Sensor bias estimation in the presence of data association uncertainty

Abstract

This paper considers the problem of dynamic residual bias estimation in the presence of measurement association uncertainty using common targets of opportunity under a decentralized information processing architecture i.e. independent trackers at each sensor. This is done by extending the scope of the synchronous version of the bias estimation algorithm presented by Lin, Bar-Shalom and Kirubarajan in Multisensor-Multitarget Bias Estimation for General Asynchronous Sensors to develop approaches to bias estimation in the presence of measurement association uncertainty. We consider the computational complexity and the sensor-to-fusion-center communication requirements of each of these approaches and compare their simulated performance in terms of RMSE and consistency. Though the simulations are performed with synchronous polar measurements having additive biases, the algorithm may easily be extended to the case with asynchronous measurements in other coordinate systems having both additive and multiplicative biases.