Range-Angle Likelihood Maps for Indoor Positioning Using Deep Neural Networks

TL;DR

This paper introduces a deep neural network approach using range-angle likelihood maps and residual neural networks to achieve centimeter-level accuracy in indoor aircraft cabin positioning.

Contribution

It proposes a novel method combining likelihood maps and ResNet for precise indoor positioning in aircraft cabins, with optimized hyperparameters for high accuracy.

Findings

Achieves centimeter-level localization accuracy.

Uses simulated aircraft cabin data for training.

Employs hyperparameter optimization for best performance.

Abstract

Accurate and high precision of the indoor positioning is as important as ensuring reliable navigation in outdoor environments. Using the state-of-the-art deep learning models provides better reliability and accuracy to navigate and monitor the accurate positions in the aircraft cabin environment. We utilize the simulated aircraft cabin environment measurements and propose a residual neural network (ResNet) model to predict the accurate positions inside the cabin. The measurements include the ranges and angles between a tag and the anchors points which are then mapped onto a grid as range and angle residuals. These residual maps are then transformed into the likelihood grid maps where each cell of the grid shows the likelihood of being a true location. These grid maps along with the true positions are then passed as inputs to train the ResNet model. Since any deep learning model involve numerous parameter settings, hyperparameter optimization is performed to get the optimal parameters for training the model effectively with the highest accuracy. Once we get the best hyperparameters settings of the model, it is then trained to predict the positions which provides a centimeter-level accuracy of the localization.