  The problem of multisensor multitarget state estimation in the presence of
constant but unknown sensor biases is investigated. The classical approach to
this problem is to augment the state vector to include the states of all the
targets and the sensor biases, and then implement an augmented state Kalman
filter (ASKF). In this paper, we propose a novel decoupled Kalman filtering
algorithm. The decoupled Kalman filtering first processes each target in a
separate branch, namely the single-target Kalman filtering branch, where the
single-target states and the sensor biases are estimated. Then the bias
estimate is refined by fusing the former bias estimates across all the
single-target Kalman filtering branches. Finally, the refined bias estimate is
fed back to each single-target Kalman filtering branch to improve the target
state estimation. We prove that the proposed decoupled Kalman filtering is
exactly equivalent to the ASKF in terms of the estimation results under a usual
initial condition. The equivalence is also confirmed via the numerical example.
Moreover, we further validate the proposed algorithm using the field
experimental data of a multistatic passive radar.
