Reducing Emitter Localization Error in Urban Environments with Geometry Adaptive UAS Arrays

A geometrically-adaptable cooperative unmanned aerial system (UAS) array provides enhanced localization accuracy for radio frequency (RF) emitter localization over that of a stationary array. We investigate such an array in simulated urban settings, where atmospheric scattering and building multipath impacts localization accuracy. The research leverages the time difference of arrival (TDOA)-based Location on a Conic Axis (LOCA) algorithm to estimate emitter locations, demonstrating improved accuracy through iterative updates to the geometry of the UAS sensor array. The research analyzes dynamic repositioning approaches for the UAS array, showing how an array that adapts to changing environmental conditions with new sensor geometries will reduce localization error in successive searches. Further, we show that using environmental feedback and exploiting the LOCA geometry to adapt the UAS geometry at a large distance facilitates remote localization as an improvement over traditional received signal strength (RSS) based methods that require physical convergence of the UAS to the emitter. Our results demonstrate that optimal UAS placement and dynamic repositioning are viable opportunities to enhance localization performance in dense urban settings.