Data-aided ML timing acquisition in ultra-wideband radios

Realizing the great potential of ultra-wideband radios depends critically on the success of timing acquisition. To this end, optimum data-aided timing offset estimators are derived in this paper based on the maximum likelihood (ML) criterion. Specifically, generalized likelihood ratio tests are employed to detect an ultra-wideband waveform propagating through dense multipath, as well as to estimate the associated timing and channel parameters in closed form. The acquisition ambiguity induced by multipath spreading and time hopping is resolved via a robust ML formulation. The proposed algorithms only employ digital samples collected at a low symbol or frame rate, thus reducing considerably the implementation complexity and acquisition time.