Non-Line-of-Sight Detection for Radio Localization using Deep State Space Models

Localization based on channel impulse responses (CIRs) of radio frequency (RF) signals yields centimeter-accurate positions under optimal line-of-sight (LOS) propagation conditions. However, in real indoor environments, e.g., in car manufacturing, non-line-of-sight (NLOS) situations dominate. Here, multipath propagation affects the time-of-arrival (ToA) estimation and downstream multilateration and localization accuracy. The detection and subsequent mitigation of NLOS per transceiver line compensates for these effects. To detect NLOS, the state-of-the-art employs either supervised or unsupervised learning methods that require the acquisition of expensive reference data or do not generalize to changes or unknown environments. This is due to, among other things, the fact that they cannot exploit spatial and temporal information from CIR signal streams.Thus, we propose a generative deep state space model (SSM) for NLOS detection on CIRs that exploits time and space. Our ultra-wideband (UWB) experiments show that our dynamical variational autoencoder (DVAE) detects NLOS signals from sequences of CIRs more accurately than the state-of-the-art and is robust to unknown environments.