Online Target Tracking and Sensor Registration using Sequential Monte Carlo Methods

In tracking applications, the target state (e.g., position, velocity) can be estimated by processing the measurements collected from all deployed sensors at a central node. The estimation performance significantly relies on the accuracy of the sensor positions/rotations when data fusion is conducted. Since in practice precise knowledge of this sensor information is seldom available, in this paper we propose a Sequential Monte Carlo (SMC) approach to jointly estimate the target state and resolve the sensor position uncertainty.