One-Bit Direction-of-Arrival Estimation via AdaBoost

The use of one-bit analog-to-digital converters (ADCs) for the direction-of-arrival (DOA) estimation problem offers several advantages over high-precision counterparts, such as reductions in power consumption and production costs. However, it also introduces new challenges, including information loss due to discarding of amplitude information and preservation of signal sign only. As a result, customized DOA estimation methods need to be developed. In this work, two novel learning-based one-bit DOA estimation algorithms are proposed. The proposed learning-based algorithms integrate a specific approximate Gaussian discriminant analysis (GDA) as weak classifiers within an adaptive boosting (AdaBoost) framework. The first proposed method, referred to as on-grid AdaBoost (OG-AdaBoost), first discretizes the DOA angular interval and builds an over-complete dictionary for the array steering matrix and then estimates the corresponding source signal matrix. The second proposed method, named as MUSIC-AdaBoost, estimates the array steering matrix and source signal matrix in an alternating manner with a constraint on the number of iterations using MUSIC and AdaBoost. Numerical simulations demonstrate the advantages and disadvantages of each proposed method compared to state-of-the-art in various challenging scenarios. The results highlight that OG-AdaBoost performs effectively in scenarios involving widely spaced DOAs. Furthermore, the numerical results demonstrate the MUSIC-AdaBoost method's superior performance relative to state-of-the-art algorithms in the face of closely spaced and/or highly correlated sources when both the number of snapshots and the number of antennas are greater than or equal to 40.