Target Tracking Using SePDAF under Ambiguous Angles for Distributed Array Radar
AbstractDistributed array radar can improve radar detection capability and measurement accuracy. However, it will suffer cyclic ambiguity in its angle estimates according to the spatial Nyquist sampling theorem since the large sparse array is undersampling. Consequently, the state estimation accuracy and track validity probability degrades when the ambiguous angles are directly used for target tracking. This paper proposes a second probability data association filter (SePDAF)-based tracking method for distributed array radar. Firstly, the target motion model and radar measurement model is built. Secondly, the fusion result of each radar’s estimation is employed to the extended Kalman filter (EKF) to finish the first filtering. Thirdly, taking this result as prior knowledge, and associating with the array-processed ambiguous angles, the SePDAF is applied to accomplish the second filtering, and then achieving a high accuracy and stable trajectory with relatively low computational complexity. Moreover, the azimuth filtering accuracy will be promoted dramatically and the position filtering accuracy will also improve. Finally, simulations illustrate the effectiveness of the proposed method. View Full-Text
Share & Cite This Article
Long, T.; Zhang, H.; Zeng, T.; Chen, X.; Liu, Q.; Zheng, L. Target Tracking Using SePDAF under Ambiguous Angles for Distributed Array Radar. Sensors 2016, 16, 1456.
Long T, Zhang H, Zeng T, Chen X, Liu Q, Zheng L. Target Tracking Using SePDAF under Ambiguous Angles for Distributed Array Radar. Sensors. 2016; 16(9):1456.Chicago/Turabian Style
Long, Teng; Zhang, Honggang; Zeng, Tao; Chen, Xinliang; Liu, Quanhua; Zheng, Le. 2016. "Target Tracking Using SePDAF under Ambiguous Angles for Distributed Array Radar." Sensors 16, no. 9: 1456.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.