Simultaneous position and channel parameter estimation applying adaptive Kalman Filters

摘要

Recently, location-based services have become very popular. Ubiquitous positioning is elementary for the the Internet of Things. Hence, obtaining precise location information is a core feature of recent wireless sensor networks (WSNs). Besides of location-awareness, energy-efficiency is another essential property of a modern sensor network. RSSI-based direction finding is a prospective approach for WSNs providing low-power positioning. However, radio-based localization techniques, including RSSI-based direction finding, are prone to fading effects of the wireless propagation channel. Therefore, a-priori knowledge of channel parameters is inevitable for precise positioning. Fading parameters rapidly change when traversing different environments. Thus, a-priori channel knowledge can not be expected. In this paper, we apply adaptive Kalman Filters to the problem of simultaneous estimation of position and channel parameters. Applicability is proven by simulations and a field trial tracking bats in a forest.