Aerial obstacle estimation using RSSI observations based on OHLOSS diffraction model

Abstract

This paper presents a new approach to estimate the location and height of the obstacle located between two unmanned aerial vehicles (UAVs) using Received Signal Strength Indication (RSSI) observations. The goal of developing this approach is to improve the localization of a radio frequency (RF) source by estimating the effect of obstacles on the signal attenuation. The effect of an obstacle on signal strength attenuation is the most important source of error in distance estimation based on general or empirical path loss model. Therefore, mapping the primary obstacle, which has the greatest effect on the signal attenuation, can improve the distance estimation. The main idea of the proposed approach is in the distinction between the effects of obstacle(s) on the signal attenuation, i.e. the diffraction loss, and the path loss. The proposed approach uses a model of diffraction loss to estimate the height and position of the primary knife-edge obstacle. The observations include the diffraction losses which are collected on the UAV's paths. Due to the Gaussian distribution of the diffraction loss observations and nonlinearity of the observation function, extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter are implemented and compared with each other. The results of the simulations show that the proposed approach is able to map an obstacle between two RF sources. Furthermore, the detected and estimated obstacle can be used for better RF source localization.