An Improved Nearest Neighbor Data Association Method for Underwater Multi-Target Tracking
نویسندگان
چکیده
Nearest Neighbor (NN) data association method is the most popular and widely used algorithm for target tracking in the presence of clutter, due to its acceptable performance and low computational complexity. Despite of the good performance of this algorithm in single target tracking and even Multi-Target Tracking (MTT) with non-crossing paths scenarios, its performance degrades significantly in the cases of MTT with crossing paths. A new version of this algorithm, Inertial NN (INN) is proposed in this paper. INN uses inertial property of the target motion to make a better estimation of the observation in each time interval. The results of applying the new algorithm to practical STT and MTT with noncrossing paths and also simulated MTT with crossing paths scenarios show a significant improvement in the performance and robustnessity in different situations with a slight increase in the complexity. Because of the considerable improvements of INN, it is strongly suggested to be used instead of its former version in both single and multi target tracking with crossing or non-crossing paths. Although the dependence of INN to the inertial property of the target motion made it suitable for underwater target tracking, it is also applicable for other scenarios such as radar, speech processing, and etc.
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