Distributed sequential nearest neighbour multitarget tracking algorithm

An efficient distributed multitarget tracking algorithm is proposed. This distributed tracker consists of two main components: local sensor level trackers and a track fuser. In the track fuser, local tracks from sensors are first transformed to a common co-ordinate system, and synchronised by a linear Kalman filter. A track correlation technique called sequential minimum normalised distance nearest neighbour (SMNDNN) method with the majority decision making (MDM) logic is used to correlate tracks from different sensors. The correlated tracks are fused using a sequential minimum mean square error (MMSE) fusion approach. The SMNDNN correlation converts the multisensor track correlation problem to a one-to-one nearest neighbour assignment, and the sequential MMSE fuser with the MDM logic combines the tracks optimally if the majority of sensors report similar tracks. Simulated data under various tracking conditions are used to evaluate the feasibility and effectiveness of this proposed distributed tracker.