Target Localization and Sensor Movement Trajectory Planning with Bearing-Only Measurements in Three Dimensional Space

Abstract

In order to improve the accuracy of bearing-only localization in three dimensional (3D) space, this paper proposes a novel bias compensation method and a new single-sensor maneuvering trajectory algorithm, respectively. Compared with traditional methods, the bias compensation method estimates the unknown variance of bearing noise consistently, which is utilized in pseudo-linear target localization to achieve higher precision. The sensor maneuvering algorithm designs the next moment sensor location in consideration of all the past sensor locations, unlike other approaches that only consider finite past locations. Research shows that the trajectories generated by our algorithm have greater Fisher information matrix (FIM) determinants and better localization accuracy.

Non-Gaussian Pseudolinear Kalman Filtering-Based Target Motion Analysis with State Constraints

“…This method effectively solves the problem that Kalman filtering methods are sensitive to initial value selection and reduces the estimation error. The third category is to linearize the nonlinear angle measurement equation by using the pseudolinear estimator (PLE) method [18]. The pseudolinear Kalman filter (PLKF) [19,20] is produced by combining the Kalman filter with the pseudolinear estimator method.…”