Majorization–Minimization Based Direct Localization Using One-Bit Channel Measurements

Abstract

Direct localization or direct position determination (DPD) can outperform the more traditional angle and delay estimation based approaches, yet being less used in practice due to the requirement of aggregating raw data or measurements to a single processing point. To reduce the network burden, this paper considers one-bit quantized channel response data, and proposes a majorization–minimization (MM) based one-bit DPD (MO-DPD) algorithm to localize an orthogonal frequency division multiplexing (OFDM) signal source. First, the one-bit DPD is formulated as a maximum likelihood (ML) estimation problem, which is then iteratively solved using the MM approach. The proposed MO-DPD avoids iteratively estimating any nuisance parameters, leading to high computational efficiency. The numerical results show that the MO-DPD outperforms the baseline one-bit ML solver in terms of computational load, while efficiently converging to one-bit Cramér-Rao lower bound (CRLB) over wide range of signal-to-noise ratios (SNRs). Furthermore, we show that no more than three iterations are required to achieve high accuracy.