  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.
