Conformal Prediction for Indoor Positioning with Correctness Coverage Guarantees

TL;DR

This paper introduces conformal prediction methods to indoor positioning, providing statistical guarantees on accuracy and uncertainty, and demonstrating effective generalization and control over error rates in deep learning models.

Contribution

It applies conformal prediction to indoor positioning, offering a novel framework for uncertainty quantification and error control with deep learning models in complex environments.

Findings

Achieved ~100% accuracy on training data and 85% on testing data.

Effectively approximates target coverage with conformal prediction.

Different models show varied performance in uncertainty quantification.

Abstract

With the advancement of Internet of Things (IoT) technologies, high-precision indoor positioning has become essential for Location-Based Services (LBS) in complex indoor environments. Fingerprint-based localization is popular, but traditional algorithms and deep learning-based methods face challenges such as poor generalization, overfitting, and lack of interpretability. This paper applies conformal prediction (CP) to deep learning-based indoor positioning. CP transforms the uncertainty of the model into a non-conformity score, constructs prediction sets to ensure correctness coverage, and provides statistical guarantees. We also introduce conformal risk control for path navigation tasks to manage the false discovery rate (FDR) and the false negative rate (FNR).The model achieved an accuracy of approximately 100% on the training dataset and 85% on the testing dataset, effectively demonstrating its performance and generalization capability. Furthermore, we also develop a conformal p-value framework to control the proportion of position-error points. Experiments on the UJIIndoLoc dataset using lightweight models such as MobileNetV1, VGG19, MobileNetV2, ResNet50, and EfficientNet show that the conformal prediction technique can effectively approximate the target coverage, and different models have different performance in terms of prediction set size and uncertainty quantification.