Paper Info
Title
Target Tracking Using SePDAF under Ambiguous Angles for Distributed Array Radar.
Abstract
Distributed 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.
Year
DOI
Venue
2016
10.3390/s16091456
SENSORS
Keywords
Field
DocType
distributed array radar,direction-of-arrival (DOA) estimation,ambiguous angles,tracking,probability data association filter (PDAF)
Continuous-wave radar,Radar,Radar engineering details,Computer vision,Sparse array,Extended Kalman filter,Radar tracker,Undersampling,Electronic engineering,Artificial intelligence,Engineering,Low probability of intercept radar
Journal
Volume
Issue
ISSN
16
9.0
1424-8220
Citations 
PageRank 
References 
0
0.34
6
Authors
6
Name
Order
Citations
PageRank
Teng Long138761.41
Honggang Zhang200.34
Tao Zeng312025.21
Xinliang Chen4102.94
Quanhua Liu54012.64
Le Zheng6849.88