dithering | Laurent Jacques

A Novel Multiplicative Phase Dithering Scheme for 1-bit Compressive Radar

Abstract: In this paper, we tackle the issue of implementing a dithering procedure for the 1-bit quantization of radar signals that is able to generate high-quality estimates while remaining a low-complexity and cost-efficient solution.

Thomas Feuillen, Luc Vandendorpe, Laurent Jacques

2025 IEEE Journal of Selected Topics in Signal Processing

1-bit Compressive Radar with Phase Dithering

Abstract: Dithering techniques, through the addition of a (random) signal before the Analog to Digital Converter (ADC), are widely used in acquisition chain designs for their ability to shape the quantization noise, e.

Thomas Feuillen, Laurent Jacques, Luc Vandendorpe

2024 32nd European Signal Processing Conference (EUSIPCO)

Quantized Compressive Sensing with RIP Matrices: The Benefit of Dithering

Abstract: Quantized compressive sensing (QCS) deals with the problem of coding compressive measurements of low-complexity signals with quantized, finite precision representations, i.e., a mandatory process involved in any practical sensing model.

Chunlei Xu, Laurent Jacques

2019 Information and Inference

Dithered quantized compressive sensing with arbitrary RIP matrices

Abstract: Quantized compressive sensing (QCS) deals with the problem of coding compressive measurements of low-complexity signals (e.g., sparse vectors in a given basis, low-rank matrices) with quantized, finite precision representations, i.

Time for dithering! Quantized random embeddings with RIP random matrices

(invited by H. Tyagi and M. Cucuringu) Abstract: Quantized compressive sensing (QCS) deals with the problem of coding compressive measurements of low-complexity signals (e.g., sparse vectors in a given basis, low-rank matrices) with quantized, finite precision representations, i.