Time-varying Bias Estimation for Asynchronous Multi-sensor Multi-target Tracking Systems Using STF

Time-varying bias estimation problem was studied for multi-target tracking systems with asynchronous sensors without knowing bias dynamics. We considered general situations, where the number of sensors was arbitrary as well as their sampling rates and initial sampling instants. A two-layer fusion structure was adopted. For each target, a pseudo-measurement of sensor bias was generated by fusing sensor measurements of this target. To make the pseudo-measurement decoupled from the target state, the fusion coefficient matrix was determined to be a basis for the left null space of an augmented observation matrix. The bias estimation algorithm was proposed based on the Strong tracking filter (STF) by fusing pseudomeasurements. The proposed algorithm makes use of all available sensor information, has strong tracking ability to abrupt changes, and avoids the matrix inversion for large dimensional matrices. Finally, the performance of the proposed algorithm is illustrated by the numerical simulation.