4.7 Article

Computational Array Signal Processing via Modulo Non-Linearities

期刊

IEEE TRANSACTIONS ON SIGNAL PROCESSING
卷 70, 期 -, 页码 2168-2179

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2021.3101437

关键词

Direction-of-arrival estimation; Array signal processing; Signal processing algorithms; Estimation; Parallel processing; Hardware; Signal reconstruction; Array signal processing; direction of arrival (DoA) estimation; multi-channel sampling; non-linear sensing

资金

  1. U.K. Research and Innovation Council's FutureLeaders Fellowship program Sensing Beyond Barriers (MRC) [MR/S034897/1]
  2. European Partners Fund

向作者/读者索取更多资源

This paper aims to address the problem of information loss caused by sensor saturation and clipping. By using a co-design approach with computational arrays, we can overcome the barriers between sensor array hardware and algorithms, enabling encoding and decoding of high-dynamic-range information for various signal processing tasks.
Conventionalliterature on array signal processing (ASP) is based on the capture first, process later philosophy and to this end, signal processing algorithms are typically decoupled from the hardware. This poses fundamental limitations because if the sensors result in information loss, the algorithms may no longer be able to achieve their guaranteed performance. In this paper, our goal is to overcome the barrier of information loss via sensor saturation and clipping. This is a significant problem in application areas including physiological monitoring and extra-terrestrial exploration where the amplitudes may be unknown or larger than the dynamic range of the sensor. To overcome this fundamental bottleneck, we propose computational arrays which are based on a co-design approach so that a collaboration between the sensor array hardware and algorithms can be harnessed. Our work is inspired by the recently introduced unlimited sensing framework. In this context, our computational arrays encode the high-dynamic-range information by folding the signal amplitudes, thus introducing a new form of information loss in terms of the modulo measurements. On the decoding front, we develop mathematically guaranteed recovery algorithms for spatio-temporal array signal processing tasks that include DoA estimation, beamforming and signal reconstruction. Numerical examples corroborate the applicability of our approach and pave a path for the development of novel computational arrays for ASP.

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