  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.
