2020/10/28 by Amir Weiss, Weiss, Amir, Arie Yeredor +1 · 1 citation
Computer Science · Engineering · #Distributed Sensor Networks and Detection Algorithms #FOS: Electrical engineering #Signal Processing (eess.SP) #Structural Health Monitoring Techniques #Target Tracking and Data Fusion in Sensor Networks #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2010.14803
openalex publication_date 2020/10/28 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
One-bit quantization has recently become an attractive option for data\nacquisition in cutting edge applications, due to the increasing demand for low\npower and higher sampling rates. Subsequently, the rejuvenated one-bit array\nprocessing field is now receiving more attention, as "classical" array\nprocessing techniques are adapted / modified accordingly. However, array\ncalibration, often an instrumental preliminary stage in array processing, has\nso far received little attention in its one-bit form. In this paper, we present\na novel solution approach for the blind calibration problem, namely, without\nusing known calibration signals. In order to extract information within the\nsecond-order statistics of the quantized measurements, we propose to estimate\nthe unknown sensors' gains and phases offsets according to a Kullback-Leibler\nDivergence (KLD) covariance fitting criterion. We then provide a quasi-Newton\nsolution algorithm, with a consistent initial estimate, and demonstrate the\nimproved accuracy of our KLD-based estimates in simulations.\n