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Power-Efficient Indoor Localization Using Adaptive Channel-aware Ultra-wideband DL-TDOA

Sagnik Bhattacharya, Junyoung Choi, Joohyun Lee

TL;DR

This work presents a power-efficient, channel-aware DL-TDOA localization framework for indoor UWB deployments. It combines a CNN-based NLOS probability predictor, a dynamic ranging frequency controller, an IMU-based ranging filter, and an LSTM-based localization predictor to maintain accuracy while reducing energy consumption. Key contributions include the eCIR updater, a CNN-driven NLOS classifier, a bounded dynamic frequency scheme, an LOS/NLOS-aware message selector, and an RNN-based predictor that provides augmented TDOA values during signal outages. Experimental validation on smartphone hardware demonstrates a $50.4\%$ improvement in NLOS accuracy and a $46.3\%$ reduction in power consumption under LOS conditions, underscoring practical impact for scalable, industrial-scale indoor localization.

Abstract

Among the various Ultra-wideband (UWB) ranging methods, the absence of uplink communication or centralized computation makes downlink time-difference-of-arrival (DL-TDOA) localization the most suitable for large-scale industrial deployments. However, temporary or permanent obstacles in the deployment region often lead to non-line-of-sight (NLOS) channel path and signal outage effects, which result in localization errors. Prior research has addressed this problem by increasing the ranging frequency, which leads to a heavy increase in the user device power consumption. It also does not contribute to any increase in localization accuracy under line-of-sight (LOS) conditions. In this paper, we propose and implement a novel low-power channel-aware dynamic frequency DL-TDOA ranging algorithm. It comprises NLOS probability predictor based on a convolutional neural network (CNN), a dynamic ranging frequency control module, and an IMU sensor-based ranging filter. Based on the conducted experiments, we show that the proposed algorithm achieves 50% higher accuracy in NLOS conditions while having 46% lower power consumption in LOS conditions compared to baseline methods from prior research.

Power-Efficient Indoor Localization Using Adaptive Channel-aware Ultra-wideband DL-TDOA

TL;DR

This work presents a power-efficient, channel-aware DL-TDOA localization framework for indoor UWB deployments. It combines a CNN-based NLOS probability predictor, a dynamic ranging frequency controller, an IMU-based ranging filter, and an LSTM-based localization predictor to maintain accuracy while reducing energy consumption. Key contributions include the eCIR updater, a CNN-driven NLOS classifier, a bounded dynamic frequency scheme, an LOS/NLOS-aware message selector, and an RNN-based predictor that provides augmented TDOA values during signal outages. Experimental validation on smartphone hardware demonstrates a improvement in NLOS accuracy and a reduction in power consumption under LOS conditions, underscoring practical impact for scalable, industrial-scale indoor localization.

Abstract

Among the various Ultra-wideband (UWB) ranging methods, the absence of uplink communication or centralized computation makes downlink time-difference-of-arrival (DL-TDOA) localization the most suitable for large-scale industrial deployments. However, temporary or permanent obstacles in the deployment region often lead to non-line-of-sight (NLOS) channel path and signal outage effects, which result in localization errors. Prior research has addressed this problem by increasing the ranging frequency, which leads to a heavy increase in the user device power consumption. It also does not contribute to any increase in localization accuracy under line-of-sight (LOS) conditions. In this paper, we propose and implement a novel low-power channel-aware dynamic frequency DL-TDOA ranging algorithm. It comprises NLOS probability predictor based on a convolutional neural network (CNN), a dynamic ranging frequency control module, and an IMU sensor-based ranging filter. Based on the conducted experiments, we show that the proposed algorithm achieves 50% higher accuracy in NLOS conditions while having 46% lower power consumption in LOS conditions compared to baseline methods from prior research.
Paper Structure (16 sections, 2 equations, 7 figures, 3 tables)

This paper contains 16 sections, 2 equations, 7 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Preliminaries of DL-TDOA localization
  4. Proposed Algorithm
  5. Effective CIR (eCIR) Updater
  6. NLOS Probability Predictor
  7. Dynamic Ranging Frequency Module
  8. Healthy Message Selector
  9. RNN-based Localization Predictor
  10. Baseline Methods
  11. Experimental Results
  12. Simulation Setup
  13. Model Implementation
  14. Model Training
  15. Performance Evaluation
  16. ...and 1 more sections

Figures (7)

  • Figure 1: A DL-TDOA cluster comprising 4 anchors and a user device
  • Figure 2: The algorithmic flow for adaptive DL-TDOA localization.
  • Figure 3: The example of effective CIR and difference between LOS/NLOS.
  • Figure 4: CNN based model for LOS/NLOS classification using CIR input.
  • Figure 5: The RNN model based LSTM for estimating the location.
  • ...and 2 more figures