RANSAC Methods for Robust Positioning with 5G Networks in Industrial IoT Scenarios

Positioning measurements that are performed in cellular networks under non line-of-sight (NLOS) conditions can degrade the positioning performance heavily. In this paper we show that the problem can be mitigated by robust positioning methods based on the RANSAC algorithm. Starting with a basic implementation of RANSAC for the TDOA positioning problem, we propose several enhancements based on probabilistic analysis. The methods perform particularly well in dense network deployments with multiple transmission/reception points (TRP), even when the fraction of LOS measurements is low. In simulations on standardized 5G test-scenarios for industrial internet-of-things, the best algorithm achieves an accuracy on par with what is achieved when LOS/NLOS conditions are known by the estimator.