One-Bit Radar Processing with Time-Varying Sampling Thresholds

Abstract

Target parameter estimation in active sensing, and particularly radar signal processing, is a long-standing problem that has been studied extensively. In this paper, we propose a novel approach for target parameter estimation in cases where one-bit analog-to-digital-converters (ADCs), also known as signal comparators with time-varying thresholds, are employed to sample the received radar signal instead of high-resolution ADCs. The considered problem has potential applications in the design of inexpensive radar and sensing devices in civilian applications, and can likely pave the way for future radar systems employing low-resolution ADCs for faster sampling and high-resolution target determination. We formulate the target estimation as a multivariate weighted-least-squares optimization problem that can be solved in a cyclic manner. Numerical results are provided to exhibit the effectiveness of the proposed algorithms.

Cited by in corpus (10)

• On the Performance of One-Bit DoA Estimation via Sparse Linear Arrays
• One-Bit ADCs/DACs based MIMO Radar: Performance Analysis and Joint Design
• Localization with One-Bit Passive Radars in Narrowband Internet-of-Things using Multivariate Polynomial Optimization
• LoRD-Net: Unfolded Deep Detection Network with Low-Resolution Receivers
• Blind Direction-of-Arrival Estimation in Acoustic Vector-Sensor Arrays via Tensor Decomposition and Kullback-Leibler Divergence Covariance Fitting
• BiLiMO: Bit-Limited MIMO Radar via Task-Based Quantization
• Deep-URL: A Model-Aware Approach To Blind Deconvolution Based On Deep Unfolded Richardson-Lucy Network
• Deep Radar Waveform Design for Efficient Automotive Radar Sensing
• Model-Aware Deep Architectures for One-Bit Compressive Variational Autoencoding
• Waveform Design for One-Bit Radar Systems Under Uncertain Interference Statistics