# Multiresolution time-of-arrival estimation from multiband radio channel measurements

## TL;DR

This paper introduces a multiband sampling method combined with a multiresolution ESPRIT-based algorithm for high-resolution time-of-arrival estimation in multipath radio channels, reducing spectral and sampling costs.

## Contribution

It proposes a practical multibranch receiver and a multiresolution TOA estimation algorithm leveraging multiple shift invariance structures, advancing high-resolution multipath parameter estimation.

## Key findings

- Achieves high-resolution TOA estimates with reduced spectral occupancy.
- Performance approaches the Cramér-Rao Lower Bound in simulations.
- Outperforms traditional UWB methods in resolution and efficiency.

## Abstract

Achieving high resolution time-of-arrival (TOA) estimation in multipath propagation scenarios from bandlimited observations of communication signals is challenging because the multipath channel impulse response (CIR) is not bandlimited. Modeling the CIR as a sparse sequence of Diracs, TOA estimation becomes a problem of parametric spectral inference from observed bandlimited signals. To increase resolution without arriving at unrealistic sampling rates, we consider multiband sampling approach, and propose a practical multibranch receiver for the acquisition. The resulting data model exhibits multiple shift invariance structures, and we propose a corresponding multiresolution TOA estimation algorithm based on the ESPRIT algorithm. The performance of the algorithm is compared against the derived Cram\'er Rao Lower Bound, using simulations with standardized ultra-wideband (UWB) channel models. We show that the proposed approach provides high-resolution estimates while reducing spectral occupancy and sampling costs compared to traditional UWB approaches.