3D pseudolinear Kalman filter with own-ship path optimization for AOA target tracking

Sheng Xu; Kutluyil Dogancay; Hatem Hmam

ICASSP 2016accepted0 citations

3D pseudolinear Kalman filter with own-ship path optimization for AOA target tracking

Abstract

This paper investigates the problem of how to optimize the path of a single moving own-ship for angle-of-arrival (AOA) target tracking in three-dimensional (3D) space. First, a novel 3D pseudolinear Kalman filter (PLKF) is proposed to reduce computational complexity and to improve stability of an extended Kalman filter solution. This filter consists of an xy-PLKF and a z-PLKF, transforming the nonlinear azimuth and elevation angle measurements into pseudolinear models. We show that when the own-ship and target are at the same height, the z-PLKF will be unbiased. Next, a gradient-descent path optimization algorithm is developed for the xy-PLKF aiming at minimizing the trace of the covariance matrix. Then, a grid search path optimization method is designed for the z-PLKF. Simulation examples verify the effectiveness of the proposed path optimization algorithm.

BibTeX

@inproceedings{icassp2016_3dpseudolinearka,
  title = {3D pseudolinear Kalman filter with own-ship path optimization for AOA target tracking},
  author = {Sheng Xu and Kutluyil Dogancay and Hatem Hmam},
  booktitle = {ICASSP 2016},
  year = {2016}
}