Cooperative AoA Wireless Positioning using LSTM Neural Network: Preliminary Results

Wireless positioning (WP) enables ego or hetero-localization, e.g. for asset tracking applications. It can be provided by 3GPP technologies, complementing GPS with extra accuracy where conditions are challenging (e.g., urban canyons). We address scenarios where base-stations (BS) track a mobile user using a wireless Angle-of-Arrival (AoA) technique and, by exchanging angle estimates, can estimate the location of a target node. Cooperative position determination from angle estimates has been addressed by estimation techniques (e.g., Least Squares). We investigate if machine learning techniques, notably a Long Short-Term Memory Neural Network (LSTM-NN), can offer competitive performance. The LSTM is designed to be fed sequential error-affected angle measurements and output position estimates. We assume the path taken by the target mobile node is known beforehand. At training stage, the LSTM is trained to learn a subset of scenario locations (belonging to the node’s path) with well-known positions; then, in runtime, it corrects position estimates for points in the whole path. The LSTM architecture is selected as it retains temporal relationship between input samples. Preliminary simulation results show competitive accuracy when the angle estimate error is relatively large (15°) with respect to a baseline value (5°).