Multi Detector Fusion of Dynamic TOA Estimation using Kalman Filter

TL;DR

This paper introduces a fusion method using Kalman filters for dynamic TOA estimation from multiple sub-Nyquist energy detectors, achieving matched filter performance in noisy environments.

Contribution

It presents a novel fusion approach for non-coherent detectors that matches digital matched filter performance using fewer resources.

Findings

Number of detectors needed decreases with higher SNR

Two detectors suffice to match matched filter performance in simulations

Analytical expression for detector count required for performance achievement

Abstract

In this paper, we propose fusion of dynamic TOA (time of arrival) from multiple non-coherent detectors like energy detectors operating at sub-Nyquist rate through Kalman filtering. We also show that by using multiple of these energy detectors, we can achieve the performance of a digital matched filter implementation in the AWGN (additive white Gaussian noise) setting. We derive analytical expression for number of energy detectors needed to achieve the matched filter performance. We demonstrate in simulation the validity of our analytical approach. Results indicate that number of energy detectors needed will be high at low SNRs and converge to a constant number as the SNR increases. We also study the performance of the strategy proposed using IEEE 802.15.4a CM1 channel model and show in simulation that two sub-Nyquist detectors are sufficient to match the performance of digital matched filter.