Optimal Hyperparameters Solutions for DNN to Improve Accuracy of Indoor Positioning Systems Based on Channel Impulse Response
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Abstract
This paper introduces a fault-tolerant indoor robot localization framework that leverages deep neural networks (DNNs) to process channel impulse response (CIR) data from multiple access points (APs). Unlike conventional CIR-based localization methods that assume stable infrastructure, this work explicitly addresses the challenge of AP failure—an issue largely overlooked in prior studies. Two complementary DNN architectures are investigated: a single-input model that concatenates CIRs from all APs, and a modular multi-input model in which each branch processes the CIR of an individual AP. To improve robustness, we propose a hybrid Hyperband–Simulated Annealing (SA) optimization strategy for both hyperparameters and network parameters. Experiments show that while the single-input network achieves high accuracy under ideal conditions (ADE = 0.552 m), its performance deteriorates significantly under AP outages (up to 1.019 m). In contrast, the multi-input architecture—with branch-specific hyperparameter tuning—consistently maintains strong performance across all failure scenarios, achieving ADEs of 1.15--1.19 m. These results demonstrate the novelty and effectiveness of combining architectural modularity with adaptive optimization to achieve resilient CIR-based localization in dynamic indoor environments.
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