Study On Passive Location Method Based On Non-Moving Observer

Passive localization and tracking system plays an important role in the electronic reconnaissance, as it works silently without electromagnetic radiation and covering larger region. Single observer passive localization and tracking (SOPLAT) system avoid time synchronization and data fusion. So it attracts more and more research focus. The basic problem in target motion analysis (TMA) is to estimate the trajectory of an object from noise corrupted sensor data. In the practical applications, the position and velocity of target possibly can be obtained by a single moving observation platform using measurement data.In this dissertation, several technology problems of passive localization and tracking are discussed, such as observability analysis, model development, localization principle, and filtering algorithm. Specially, some advanced localization theory with higher precision and fast speed is introduced. It significantly improved the performance of localization and tracking system.Firstly, background of single observer passive localization is introduce. As the observability and model development are the fundamental problem, they are discussed in the Chapter 2. Followly, some considerable algorithms of localization and tracking are presented in Chaper 3 and 4. In the last years, the extended Kalman filter(EKF) has become the usual algorithm to deal with the nonlinear dynamic systems. Unfortunately, because of the linearization, the EKF produces large errors and easily lead to divergence. To overcome this problem, several algorithms of filtering algorithm, such as UKF, SR-UKF are introduced in the dissertation. These algorithms estimate the posteriors with some typical sigma point. These methods amend flaws of the EKF, and have better properties such as robustness and accuracy. However, UKF still assumes a Gaussian parametric form of the posterior. To overcome this problem, the particle filter is described in the chapter 4. The particle filter assumes no functional form, but instead, use a set of random samples (also called particles) to estimate the posteriors, and have a good property in the passive localization and tracking problems.It is difficult for traditional measurements to determine the position of emitter quickly and accurately. How to make use of the available information fromelectromagnetic radiation is key to improve the performance of passive localization and tracking. In Chapter 5, the rate of bearing changing rate is introduced to realize the passive localization and tracking and has a better performance.At last, the conclusion and forecast are drawn in the dissertation.