Localisation in Wireless Networks using Deep Bidirectional Recurrent Neural Networks

The accurate localisation and tracking of objects is crucial in many domains. In this paper, we focus on location tracking in wireless networks. Reliable localisation will be essential for self-driving, future factories, and beamforming in 5G deployments. Time-of-arrival (TOA) based localisation systems use synchronised nodes to receive radio signals sent by transmitters at the object to be located. The time it takes for the signal to propagate in a straight line to the receiver is used to trilaterate the position of the transmitter. However, the correct TOA can be difficult to identify due to multiple reflected copies of the same signal arriving at different times. This paper presents a bidirectional recurrent neural network (BiRNN) for estimating the TOA from the channel impulse response (CIR) of the signal, and a multilayer perceptron (MLP) to trilaterate the position of the transmitter from the TOA. The BiRNN and MLP are trained using measured CIR data, and outperform the conventional approaches for TOA estimation and trilateration.