Bayesian bootstrap filtering for multiple mobile position determination using LEO satellites

We propose a new mobile position determination algorithm based on the bootstrap filtering from time difference of arrival (TDOA) measurements using low Earth orbiting (LEO) satellites. The proposed algorithm is quite capable of handling data drop-outs and outliers in an automated manner and in addition, imposing nonlinear kinematic constraints on the state estimates without destabilizing the algorithm. Such constraints can be most naturally incorporated in the Bayesian bootstrap filtering framework (treating the constraints as perfect measurements without measurement noise). To verify the proposed algorithm, simulation is performed and the result demonstrates that our algorithm is more robust than the extended Kalman filter or the bootstrap filter without constraints