Hidden Markov Estimation of Bistatic Range From Cluttered Ultra-wideband Impulse Responses

TL;DR

This paper introduces a hidden Markov model approach for estimating bistatic range in UWB radar, effectively handling dense multipath signals and measurement noise, leading to more accurate target localization.

Contribution

It proposes a novel HMM-based method for bistatic delay estimation in cluttered UWB CIRs, outperforming traditional thresholding techniques.

Findings

HMM approach achieves RMSE of 2.76-2.85 ns, better than thresholding methods.

The method is robust to initial parameter settings using Baum-Welch algorithm.

Improved localization accuracy with HMM-based bistatic delay estimates.

Abstract

Ultra-wideband (UWB) multistatic radar can be used for target detection and tracking in buildings and rooms. Target detection and tracking relies on accurate knowledge of the bistatic delay. Noise, measurement error, and the problem of dense, overlapping multipath signals in the measured UWB channel impulse response (CIR) all contribute to make bistatic delay estimation challenging. It is often assumed that a calibration CIR, that is, a measurement from when no person is present, is easily subtracted from a newly captured CIR. We show this is often not the case. We propose modeling the difference between a current set of CIRs and a set of calibration CIRs as a hidden Markov model (HMM). Multiple experimental deployments are performed to collect CIR data and test the performance of this model and compare its performance to existing methods. Our experimental results show an RMSE of 2.85 ns and 2.76 ns for our HMM-based approach, compared to a thresholding method which, if the ideal threshold is known a priori, achieves 3.28 ns and 4.58 ns. By using the Baum-Welch algorithm, the HMM-based estimator is shown to be very robust to initial parameter settings. Localization performance is also improved using the HMM-based bistatic delay estimates.