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.
Nabil, M., Kamal, H., & Hassan, M. (2012). Comparison between Kalman Filter and PHD Filter in Multi-target Tracking. The International Conference on Electrical Engineering, 8(8th International Conference on Electrical Engineering ICEENG 2012), 1-14. doi: 10.21608/iceeng.2012.31375
MLA
M. Nabil; H. Kamal; M. Hassan. "Comparison between Kalman Filter and PHD Filter in Multi-target Tracking". The International Conference on Electrical Engineering, 8, 8th International Conference on Electrical Engineering ICEENG 2012, 2012, 1-14. doi: 10.21608/iceeng.2012.31375
HARVARD
Nabil, M., Kamal, H., Hassan, M. (2012). 'Comparison between Kalman Filter and PHD Filter in Multi-target Tracking', The International Conference on Electrical Engineering, 8(8th International Conference on Electrical Engineering ICEENG 2012), pp. 1-14. doi: 10.21608/iceeng.2012.31375
VANCOUVER
Nabil, M., Kamal, H., Hassan, M. Comparison between Kalman Filter and PHD Filter in Multi-target Tracking. The International Conference on Electrical Engineering, 2012; 8(8th International Conference on Electrical Engineering ICEENG 2012): 1-14. doi: 10.21608/iceeng.2012.31375
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