Out of Sequence Measurements 환경에서의 MPDA 성능분석

Abstract

In a multi-sensor multi-target tracking systems, the local sensors have the role of tracking the target and transferring the measurements to the fusion center. The measurements from the same target can arrive out of sequence called the out-of-sequence measurements(OOSMs). Out-of-sequence measurements can arise at the fusion center due to communication delay and varying preprocessing time for different sensor platforms. In general, the track fusion occurs to enhance the tracking performance of the sensors using the measurements from the sensors at the fusion center. The target informations can arrive at the fusion center with the clutter informations in cluttered environment. In this paper, the OOSM update step with MPDA(Most Probable Data Association) is introduced and tested in several cases with the various clutter density through the Monte Carlo simulation. The performance of the MPDA with OOSM update step is compared with the existing NN, PDA, and PDA-AI for the air target tracking in cluttered and out-of-sequence measurement environment. Simulation results show that MPDA with the OOSM has compatible root mean square errors with out-of-sequence PDA-AI filter and the MPDA is sufficient to be used in out-of-sequence environment.