Reduced-complexity scheme using alpha–beta filtering for location tracking

Abstract

This study presents an efficient location tracking algorithm to reduce the computational complexity of the conventional Kalman filtering (KF) algorithm. In the proposed training and tracking scheme, the authors replace the decision mode of the KF algorithm with an alpha–beta (α–β) algorithm to avoid repeatedly calculating the Kalman gain. After the mode with α–β – tracking, the exact information of the state and measurement noise parameters used in the KF algorithm is not required. Using the inherent fixed-coefficient feature of α–β filtering, the location information between the prediction phase and correction phase is efficiently cycled, thus simplifying implementation of the KF approach. Under a stationary environment, numerical simulations show that the proposed training and tracking approach not only can achieve the location accuracy close to the KF scheme but has much lower computational complexity.