Comparison between Kalman Filter and PHD Filter in Multi-target Tracking | ||||
| The International Conference on Electrical Engineering | ||||
| Article 75, Volume 8, 8th International Conference on Electrical Engineering ICEENG 2012, May 2012, Page 1-14 PDF (242.3 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/iceeng.2012.31375 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| M. Nabil1; H. Kamal2; M. Hassan2 | ||||
| 1M.Sc. student, Military Technical College, Cairo, Egypt. | ||||
| 2Department of Radar staff (Ph.D.), Military Technical College, Cairo, Egypt. | ||||
| Abstract | ||||
| Tracking a maneuvering target weakens the performance of predictive-model-based Bayesian state estimators (Kalman Filter). Therefore, the Probability Hypothesis Density (PHD) filter was proposed to overcome this problem. In this paper, the performance of Kalman filter, modified Kalman filter, and PHD filter in tracking a highly maneuverable target is shown. All three algorithms to track a maneuverable target are applied. Monte Carlo simulation showed that the PHD filter provides promising performance compared to Kalman filter. In particular, the algorithm is capable of tracking multiple crossing maneuvering targets. | ||||
| Keywords | ||||
| Multi-target Tracking; Kalman filter; Probability Hypothesis Density (PHD Filter) | ||||
|
Statistics Article View: 143 PDF Download: 219 |
||||
