Research On Target Tracking Techniques Based On Passive Multi-Sensor Detection

The techniques of passive target tracking are important topics of the research on multi-sensor data fusion. Because of their wide applications in both military and civil areas, much attention has been paid to their developments by worldwide researchers and engineers. Aiming at the techniques of target tracking based on passive multi-sensor detection, this dissertation mainly involves some significant aspects, such as the optimal design of system parameters, target tracking algorithms etc. Some novel efficient methods have been proposed. The main contributions of the dissertation are as follows:1 For the problem of the optimal design of system parameters, an optimal design method based on the analysis of target tracking accuracies is proposed, which involves passive target tracking achieved by introducing extended Kalman filter (EKF) into the multiple-senor centralized fusion scheme, and the Cramer-Rao lower bounds of tracking errors are deducted. Furthermore, the geometrical dilution tracking error (GDTE) in the surveillance area is given. Some efficient approaches to improve system performance are brought forward on the basis of the analysis of the influences of the system parameters to GDTE.2 For the problem of single target tracking based on noisy passive measurements, an unscented Kalman filter (UKF) based passive target tracking algorithm is proposed, in which the unscented transformation (UT) is introduced into Kalman filter with the scheme of multi-senor centralized fusion, by which the numerical accuracies are enhanced with little additional computational costs because of the avoidance of errors from the linearization in extended Kalman filter (EKF). To solve the problem of state coupling in single coordinate based filters, a hybrid coordinates UKF (HC-UKF) based passive target tracking algorithm is proposed, which emploies UT to achieve the nonlinear coordinate transformation. The numerical accuracies of target tracking are improved due to the depressed target's state coupling. Furthermore, a quasi Monte Carlo sampling Gaussian particle filter (QMC-GPF) based passive target tracking approach is proposed to solve the problem of heavy computational costs for particle filter (PF) based methods. The algorithm introduces quasi Monte Carlo technique into Gaussian particle filter recursion, by which the computational costs are reduced because of the reduced number of samples, and the numerical accuracies are enhanced.3 To solve the problem of maneuvering target tracking, a reweighted variable structure interacting multiple model (RVSIMM) algorithm is proposed. The method introduces reweighted steps into the interacting of multiple models. Moreover, the model sets in each recursion is modified to avoid the blights from the invalid models. Consequently, both of computational costs and numerical accuracies are improved. Furthermore, a robust two stage EKF (RTSEKF) for passive maneuvering target tracking is proposed. The algorithm estimates both the target's state and its maneuver bias input online, where the estimation of target's state is modified by the estimation of the maneuver bias. At the same time, the noise statistics parameters are estimated to revise the models of filtering. Consequently, the numerical accuracies are enhanced without too much additional computational costs.4 For the problem of passive multi-sensor multi-target tracking, when the time varying number of targets maneuver and the measurements are mixed with the interferences of clutters, the methods based on random finite set (RFS) are addressed. Then, an IMM probability hypothesis density (IMM-PHD) filter based passive multiple maneuvering targets tracking approach is proposed. The algorithm involves modeling both of the time varying number of targets'states and passive measurements as RFSs. Besides, the interacting multiple model (IMM) is embedded into the filtering recursions to avoid the loss of maneuvering targets. Consequently, tracking accuracies are improved.