Sparse Factorization-based Detection of Off-the-Grid Moving targets using FMCW radars

Abstract: In this paper, we investigate the application of continuous sparse signal reconstruction algorithms for the estimation of the ranges and speeds of multiple moving targets using an FMCW radar. Conventionally, to be reconstructed, continuous sparse signals are approximated by a discrete representation.

Sparsity-Driven Moving Target Detection in Distributed Multistatic FMCW Radars

Abstract: We investigate the problem of sparse target detection from widely distributed multistatic textitFrequency Modulated Continuous Wave (FMCW) radar systems (using chirp modulation). Unlike previous strategies e.g., developed for FMCW or distributed multistatic radars), we propose a generic framework that scales well in terms of computational complexity for high-resolution space-velocity grid.

An extreme bit-rate reduction scheme for 2D radar localization

Abstract: In this paper, we further expand on the work in [1] that focused on the localization of targets in a 2D space using 1-bit dithered measurements coming from a 2 receiving antennae radar.

A sparse smoothing approach for Gaussian mixture model based acoustic-to-articulatory inversion

Abstract: It is well-known that the performance of the Gaussian mixture model (GMM) based acoustic-to-articulatory inversion (AAI) improves by either incorporating smoothness constraint directly in the inversion criterion or smoothing (low-pass filtering) estimated articulator tra- jectories in a post-processing step, where smoothing is performed independently of the inversion.

A panorama on multiscale geometric representations, intertwining spatial, directional and frequency selectivity

More information: This paper is part of the special issue “Advances in Multirate Filter Bank Structures and Multiscale Representations.” Here is a webpage related to this work (on Laurent Duval’s website).

Dequantizing Compressed Sensing: When Oversampling and Non-Gaussian Constraints Combine

Abstract: In this paper, we study the problem of recovering sparse or compressible signals from uniformly quantized measurements. We present a new class of convex optimization programs, or decoders, coined Basis Pursuit DeQuantizer of moment p (BPDQp), that model the quantization distortion more faithfully than the commonly used Basis Pursuit DeNoise (BPDN) program.