Multipath Delay Estimation in Complex Environments using Transformer

Modern radio frequency based positioning systems exploit multipath propagation to achieve accurate and robust positioning at a minimum effort in infrastructure. A key concept is exploitation of multipath component (MPC) delays from channel measurements, which have a direct relation to the geometry of the environment. This is a challenging task given complex multipath-rich environments and limited bandwidths. However, downstream tasks suffer from false or missed detections, which is why reliable MPC detection and delay estimation is crucial. We propose an MPC delay estimation pipeline based on a Transformer (TF) neural network, which implicitly estimates the number and delays of the MPCs. We achieve subsample accuracy without using computational expensive super-resolution techniques. Our approach outperforms state-of-the art on detection and delay estimation of MPCs on different bandwidths. We also show that our approach can easily be fine-tuned on real world data with very few labeled data samples, making it a well-suited candidate for real world deployments.