LAUSR

Distributed radar network systems (DRNS) have been shown to provide significant performance improvement. With the recent development, radar network has become an attractive platform for target tracking. In practice, the netted radars in DRNS are supposed to maximize their transmitting power to achieve better target tracking performance, which may be in contradiction with low probability of intercept (LPI). This paper investigates the problem of adaptive resource scheduling based on time difference of arrival (TDOA) cooperation for target tracking by DRNS consisting of a dedicated radar netting station and multiple netted radars. Firstly, the standard interacting multiple model (IMM) algorithm incorporating extended Kalman filter (EKF) is improved by modifying the Markov transition probability with current measurements. Then, a novel resource scheduling strategy based on TDOA cooperation is presented, in which the LPI performance for target tracking in DRNS is improved by optimizing the radar revisit interval and the transmitted power for a predefined target tracking accuracy. The comparison of the predictive error covariance matrix and the expected error covariance matrix is utilized to control the radar netting station under intermittent-working state with TDOA cooperation. Due to the lack of analytical closed-form expression for receiver operating characteristics (ROC), we utilize several popular information-theoretic criteria, namely, Bhattacharyya distance, Kullback-Leibler (KL) divergence, J-divergence, and mutual information (MI) as the metrics for target detection performance in target tracking process. The resulting optimization problems which are associated with different information-theoretic criteria are unified under a common framework. The nonlinear programming (NP) based genetic algorithm (GA) or else known as NPGA is employed to encounter with the highly nonconvex and nonlinear optimization problems in the framework. Numerical results demonstrate that the proposed algorithm not only has excellent target tracking accuracy, but also has better LPI performance comparing to other methods.