Improving Data Association Based on Finding Optimum Innovation Applied to Nearest Neighbor for Multi-Target Tracking in Dense Clutter Environment
In this paper, a new method, named optimum innovation data association (OI-DA), is proposed to give the nearest neighbor data association the ability to track maneuvering multi- target in dense clutter environment. Using the measurements of two successive scan and depending on the basic principle of moving target indicator (MTI) filter, the proposed algorithm avoids measurements in the gate size of predicted target position that are not originated from the target and detects the candidate measurement with the lowest probability of error. The finding of optimum innovation corresponding to the candidate valid measurement increases the data association performance compared to nearest neighbor (NN) filter. Simulation results show the effectiveness and better performance when compared to conventional algorithms as NNKF and JPDAF.
Keywords: Data Association, Multi-Target Tracking (MTT), Moving Target Indicator (MTI) Filter, Nearest Neighbor Kaman Filter (NNKF), Joint Probabilistic Data Association Algorithm (JPDA)
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