Comparative analysis of robust extended Kalman filter and incremental smoothing for UWB/PDR fusion positioning in NLOS environments

Abstract

Ultra wide band (UWB) sensors are widely used for indoor positioning; however, in many practical scenarios UWB signals are obscured by people, goods or other obstacles. This results in signal intensity attenuation, multipath effect and even signal loss, which causes a sharp decline in positioning accuracy. Fusion of pedestrian dead-reckoning (PDR) and UWB is an effective method to achieve high-accuracy positioning under non-line of sight conditions. While traditionally Bayesian filters, such as extended Kalman filter (EKF) and particle filter have been used for UWB/PDR fusion, recently Incremental Smoothing has been shown to achieve high accuracy in other application domains. In this paper, incremental Smoothing based on Tukey kernel function is proposed to fuse UWB and PDR data. We compare the performance of Incremental Smoothing with state of the art fusion algorithms based on EKF, and show that the incremental smoothing algorithm can achieve real-time positioning while exhibiting stronger robustness against intermittent noise, continuous noise and continuous interruption abnormality of UWB data.