Positioning of Radio Emission Sources with Unmanned Aerial Vehicles using TDOA-AOA Measurement Processing

Actual trends in current passive geolocation system development includes cooperation of flying segment based on receiver stations aboard Unmanned Aerial Vehicles (UAVs) with terrestrial segment including stationary ground receiver stations. Existing accuracy results achieves the order of tens and hundreds of meters in optimistic Line of Sight (LOS) conditions. However, the problem of radio emission sources positioning with UAVs is especially relevant for search and rescue operations in heterogeneous terrain, when separate primary measurements obtained, for example, after reflections, could lead to a significant error. One possible way to improve the accuracy of positioning in such conditions is to use aerial passive geolocation based on UAVs with joint processing of Time Difference of Arrival (TDOA) and Angle of Arrival (AOA) primary measurements. The contribution of the current investigation is the development of mathematical model for positioning of radio emission sources with UAVs using TDOA-AOA measurement processing.This work was supported by the Ministry of Science and Education of the Russian Federation with Grant of the President of the Russian Federation for the state support of young Russian scientists № MK-3468.2018.9

Comparison of recursive algorithms for emitter localisation using TDOA measurements from a pair of UAVs

This paper presents a comparative analysis of three nonlinear filters for estimation of the location and velocity of a moving emitter, using time difference of arrival (TDOA) measurements received by two unmanned aerial vehicles (UAVs) as they traverse the surveillance region. The TDOA measurements are generated over time by comparing and subtracting leading edge time of arrivals (TOAs) of signals. The Cram*aaer Rao lower bound (CRLB) of estimation errors is derived and used as the benchmark in performance analysis. The three nonlinear filters considered in the comparison are: a Gaussian mixture measurement integrated track splitting filter (GMM-ITSF), a multiple model filter with unscented Kalman filters (UKFs) and a multiple-model filter with extended Kalman filters (EKFs)