Research On Algorithms For Single Observer Passive Tracking With The Information Of Spatial-Frequency Domain

Conventional detection systems, such as radar and sonar, have encountered more and more threats with the increasing complexity of circumstances in modern battlefield. Passive localization and tracking technology has been paid more and more attention because of its significant advantage in self-hiding, far-distance detection and extensive applicability. In this dissertation, some critical issues on single observer passive tracking are touched based on the observed information of spatial-frequency domain. These issues include localization model and observability conditions, tracking algorithms, maneuvering target tracking, and lower bound analysis of tracking errors, etc. Both theory and algorithms presented in the dissertation are validated using simulated data or real measurement data.System model and observability are the basis for passive localization and tracking, and the state of the target can be estimated only when the system is observable. As for the observability analysis, much attention has been paid to motion target with constant velocity; however, analysis of observability for maneuvering target is still a blank to some extent. In view of these facts, observability analysis for two classes of conventional maneuvers (constant acceleration and constant turn rate) are investigated in Chapter II, and some meaningful conclusions are drawn too, which lays the base for maneuvering target tracking discussed in Chapter V.Passive target tracking is in essence the problem of nonlinear optimal filtering, i.e., the aim is to estimate the state (including position, velocity, and acceleration etc) of the target based on nonlinear measurements, and the key is to obtain the distribution of desired posterior state. On the premise of Gaussian analytic approximation to posterior distribution, Chapter III begins with the discussion of filtering algorithms and their potential drawbacks under the framework of extended Kalman filtering (EKF), on which basis the tracking algorithms are fully investigated under unscented Kalman filtering (UKF) framework: 1) in view of the large initial error and weak observability of passive system, an iterated UKF is proposed to improve the convergence speed and tracking precision; 2) a simplified UKF is proposed to reduce the computional complexity of standard UKF, which makes the algorithm more suitable for real-time application; 3) considering the particularity of CT (constant turn) model, a joint estimation algorithm referred to as JEUKF is proposed to estimate the maneuvering parameter (turn rate) and target state simutaniously, making it possible to track CT target successfully with only one single model.Another way to approximate the posterior state distribution is Monte Carlo simulation. The recently developed particle filtering technology provides a general framework for nonlinear problem, and posterior state distribution is approximated by weighted particles which are generated through Monte Carlo simulation. Chapter IV concentrates on passive tracking algorithms based on particle filtering, and the work is done from three main aspects: 1) in view of the inefficiency of general particle filter which samples the particles directly from the prior, an improved algorithm is proposed using optimal function approximation; 2) a modified unscented particle filter (MUPF) is proposed to address the numerical problem and performance deterioration found in conventional UPF; 3) considering the real characteristic of passive tracking system, the marginalized particle filtering approach is presented. Under the background of passive tracking, the initial estimate error is usually very large, and the system is subject to weak observability. In this case, the particle filtering methods exhibit obvious advantage in robustness and convergent speed because of the decentralization of particles.In the application of military background, the target may exhibit different motions from time to time. In such case, it is of great significance to investigate the problem of maneuvering target tracking, and the work in Chapter V is just done under this requirement. Usually, track of maneuvering target is realized by adaptively adjusting the model and filter parameters. In this chapter, three tracking approaches have been discussed. These include the model matched adaptation method, the noise covariance adaptation method, and the neural network adaptation method. Following the discussion, two novel algorithms, i.e., neural network algorithm with dynamic correction ability and neural network algorithm integrated by interacting multiple model (IMM), are proposed to improve the tracking performance. Compared to conventional methods used in maneuvering target tracking, the proposed algorithms are not subject to detection delay and have the advantage of high stability, prompt response, so they have better applicability in passive tracking circumstance.Under nonlinear condition, the optimal filtering algorithm is generally difficult to construct, so in real application all kinds of suboptimal algorithm are used instead. The well known Cramer-Rao lower bound (CRLB) gives an indication of performance limitation which is independent upon specified algorithm, and it is usually used to determine whether improved performance requirements are realistic for any suboptimal algorithm. Chapter VI is aimed at providing a unified framework for performance assessment of tracking algorithms by investigating the tracking error CRLB. Firstly, for the uniform velocity target, the tracking accuracy is evaluated by analyzing the general CRLB of parameter estimate; secondly, error lower bound calculation for maneuvering target tracking is solved by dividing the trajectory into multiple segments; lastly, by introducing the concept of posterior Cramer-Rao bound (PCRB), the tracking accuracy of near uniform velocity target is analyzed.The dissertation concludes with a summary of the accomplished work and future research recommendations.