Joint tracking and discrimination of exoatmospheric active decoys using nine-dimensional parameter-augmented EKF

Authors:
Bin Rao;Shunping Xiao;Xuesong Wang
Affiliations:
School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, People's Republic of China;School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, People's Republic of China;School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, People's Republic of China
Venue:
Signal Processing
Year:
2011

Citing 7
Cited 0

Cascading SLB and SLC devices

Signal Processing
Electronic Warfare in the Information Age

Electronic Warfare in the Information Age
Estimation with Applications to Tracking and Navigation

Estimation with Applications to Tracking and Navigation
Radar fusion to detect targets-Part II

Signal Processing
Radar fusion to detect targets: part III

Signal Processing
Detection algorithms to discriminate between radar targets and ECM signals

IEEE Transactions on Signal Processing
Nonlinear Kalman filtering with semi-parametric Biscaydistributions

IEEE Transactions on Signal Processing

Quantified Score

Hi-index	0.08

Visualization

Abstract

This paper describes a nine-dimensional extended Kalman filter (EKF) to jointly track and discriminate exoatmospheric active decoys in real time using motion features. By introducing an auxiliary variable as relative range delay ratio (RRDR), explicit nonlinear motion model of decoys in the East-North-Up coordinate system (ENU-CS) and the Jacobian matrix needed for the EKF are derived. The discrimination is then implemented according to the estimated deception range and its associated variance. The discrimination performance and the tracking accuracy compared to the traditional six-dimensional EKF are evaluated by Monte Carlo simulations. The advantage of the new algorithm lies in its real-time integration of tracking and discrimination.