LocHunt: Angle of Arrival Based Location Estimation in Harsh Multipath Environments

摘要

The next generation of cellular networks promises to be the platform for ubiquitous, precise, and accurate location-awareness. Although positioning using radio-frequency waves has many advantages in terms of cost and performance, propagation phenomena such as multipath propagation and shadowing can immensely deteriorate this performance leading to incorrect estimation of a user's location. This paper introduces LocHunt, an algorithm that addresses such a problem. Concretely, at each time-instance, angle-of-arrivals (AoAs) of multipath waves from several anchors are estimated and all the candidate locations of the user are derived. Repeating this operation for a span of time, the algorithm exploits the heuristics that those candidate locations obtained from non-line-of-sight (NLoS) paths exhibit large spatial variations, whereas the true location of the user is manifested as a denser cluster. Subsequently, the peak of the underlying empirical probability density function corresponds to an estimate of the user's location, which can be found by the mean-shift algorithm followed by the connected-components algorithm. The proposed data-driven approach is compared to the state-of-the-art, showing improved localization accuracy with up to one order of magnitude reduction in computational complexity. Channel state information (CSI), similar to that exposed by 802.11n MIMO-OFDM systems, is used for testing the algorithms and is generated by a modified Winner 2 (WIM2) channel simulator.