Passive track fusion via hybrid coordinate filtering with recursive least squares estimator

Abstract

An estimation fusion algorithm based on a group of hybrid coordinate (HC) filters is presented to the position and velocity estimation using angle-only measurements extracted from multiple maneuverable aircrafts with onboard passive sensor. The algorithm is a hierarchical architecture which consists of several local processors and a global processor. In each local processor, an extended Kalman (EK) filter utilizes the reference Cartesian coordinate (RCC) system for state and state covariance extrapolation and utilizes the modified spherical coordinate (MSC) system for state and state covariance updating. In the global processor, a recursive least squares (RLS) estimator sequentially computes a global estimate in the local inertial Cartesian coordinate (LICC) system. The estimator is developed by utilizing RCC updated state covariance to compute each filter weight for combining the outputs of local processors. The typical case of target motion analysis is investigated through computer simulation. In simulation study the proposed algorithm is compared to each HC EK filter. The EK filter encounters slow convergence problem under both circular arc and waving flight scenarios. By using a RLS estimator, the convergence is greatly accelerated. The algorithm markedly improves the tracking accuracy as well.