Advanced Technique based on Nearest Neighbor for Tracking Closed Spaced Targets in Clutter | ||||
| Journal of the ACS Advances in Computer Science | ||||
| Article 3, Volume 9, Issue 1, 2018, Page 44-63 PDF (717.98 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/asc.2018.158381 | ||||
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| Abstract | ||||
| The In this paper, a new technique named optimum nearest neighbor data association (ONNDA) is proposed to overcome the tracking issue of closed spaced moving targets in dense clutter environment. The proposed algorithm detects the measurements that represent the valid targets from all measurements in the cluttered gate. A new virtual gate is assigned to the detected valid measurements. The center of this gate is represented by the last point of the tracked target position. In this new gate the nearest neighbor data association algorithm is used to select the true measurement that represent the moving target. The ONNDA detects the candidate measurement with the lowest probability of error, increases the data association performance compared to nearest neighbor (NN) filter, and detects the closed moving targets in more background clutter. Simulation results show the effectiveness and better performance when compared to conventional algorithm as NNKF. | ||||
| Keywords | ||||
| kalman filter; multi-target tracking; moving target indicator; nearest neighbor data association | ||||
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