Research On Passive Location And Tracking

The techniques of passive location and tracking are one of the important topics of the research on multi-sensor data fusion. Passive location and tracking system plays a more and more important role in modern electronic warfare, because it has the advantages of anti-interference, anti-low-altitude, antagonizing anti-radiation missile and anti-stealth. Meanwhile, passive location and tracking system also plays an important role in civilian areas such as navigation and aviation. This dissertation focuses on the problems of passive locating and tracking. First, the Least Squares adaptive Kalman filter(LS-AKF) is proposed for passive location and tracking system with unknown measurement noise; Secondly, a new method for outlier detection and elimination based on innovation likelihood is proposed. The main contributions of the dissertation are as follows:1. The fundamental theory of multi-observers passive location and tracking is studied, including the multi-observers passive location modelling, several common target motion models. Including Constant Velocity Model and Constant Acceleration Model for rectilinear motion; and Singer Model, Current Statistical Model and Turning Model for maneuvering target. And the commonly used filter algorithms are researched, such as Kalman filter for linear system, Extended Kalman filter, Unscented Kalman filter and particle filter for non-linear system.2. Aiming at solving the problem of multi-observers passive locating and tracking in the condition of unknown measurement noise, the Least Squares adaptive Kalman filter algorithm is proposed. This algorithm combine multi-observers passive location with Variational Bayesian Kalman filter. The target state and measurement noise can jointly estimated. First of all, target is located using Least Squares method, and the locating result is used as pseudo-measurement of Kalman filter. Meanwhile the Variational Bayesian Kalman filter is used to estimate the state of the target and the observation noise covariance. The simulation results demonstrate the proposed algorithm is able to effectively improve the performance in multi-observers passive locating and tracking system.3. In passive locating and tracking system, the emergence of outlier has a bad influence in both the stability and the precision of the filtering. In order to solve this problem, a new method for outlier detection and elimination based on innovation likelihood is proposed. First, the influence of outlier to the precision of the filtering is theoretically analyzed. Secondly, three commonly used methods for detecting and eliminating the outlier based on innovation is introduced. A method for the outlier detection and elimination based the conception of innovation likelihood is proposed. This algorithm is based on Kalman filter, the innovation likelihood is calculated in update step. A threshold is given to aim at the detection and elimination of the outlier. The proposed algorithm plays a good performance in passive locating and tracking system with outlier.