Signals of opportunity geolocation methods for urban and indoor environments

Abstract

Motivated by the geolocation requirements of future mobile network applications such as portable internet of things (IoT) devices and automated airborne drone systems, this paper aims to provide techniques for improving device geolocation estimates in urban and indoor locations. In these applications low size, weight and power are vital design constraints. This paper proposes methods for improving the geolocation estimate available to a system in indoor and urban environments without the need for addition sensing or transmitting hardware. This paper proposes novel system application techniques that enable the integration of signals of opportunity, providing a robust geolocation estimate without any additional hardware. The proposed method utilises a sinusoidal Kalman filter architecture to analyse raw radio frequency (RF) signals that surround a system in urban and indoor environments. The introduced techniques efficiently analyse the raw RF data from any signal of opportunity and combine it with higher level geolocation sensors to provide an improved geolocation estimate. The improvements achieved by the system in a range of environments have been simulated, analysed and compared to the results obtained using the prior art. These improvements have been further validated and benchmarked by hardware test. The results obtained provide evidence that the efficient use of signals of opportunity coupled with common navigation sensors can provide a robust and reliable geolocation system in indoor and urban environments.