Enhance UWB Performance Using Dynamic AI-Enabled Approach

Developing more intelligent instrument via Artificial Intelligence (AI) is highly valuable. Ultra-Wide Band (UWB), with rich Radio Frequency (RF) and Channel Impulse Response (CIR) features, is widely favored in geospatial applications with AI-based enhancement. This paper presents an AI-enhanced UWB solution using a Dynamic Neural Network (DNN) approach, for the improvement of ranging and positioning performance. This Dynamic AI-enabled UWB (DAI UWB) solution, employs a pretrained channel estimation model as the policy network to adjust a changeable error fitting network structure for efficient ranging error compensation. Tested in various complex indoor-outdoor environments (up to about 110 meters), DAI UWB’s performance was evaluated using mixed Line-of-Sight (LOS) / Non-Line-of-Sight (NLOS) datasets. Comparative results indicate that DAI UWB outperforms the state-of-the-art baselines in ranging and positioning stability. Specifically, in ranging tasks, DAI UWB performs outstanding ranging error compensation while reducing computational waste through dynamic inference routing. Moreover, DAI UWB surpasses all baselines in providing the most stable and accurate positioning results, with the most robust statistical significance. Additionally, DAI UWB achieved a 92.8% accuracy and 93.5% accuracy of channel estimation in real-world and the public benchmark tests, playing a positive role to boost positioning system performance. Furthermore, we believe that DAI UWB’s “teaching according to students’ aptitude” design philosophy through dynamic AI methods is instructive for developing other AI-enabled advanced instruments.