Fast Binary Compressive Sensing via \ell_0 Gradient Descent
نویسندگان
چکیده
We present a fast Compressive Sensing algorithm for the reconstruction of binary signals {0, 1}-valued binary signals from its linear measurements. The proposed algorithm minimizes a non-convex penalty function that is given by a weighted sum of smoothed l0 norms, under the [0, 1] box-constraint. It is experimentally shown that the proposed algorithm is not only significantly faster than linear-programming-based convex optimization algorithms, but also shows a better recovery performance under several different metrics.
منابع مشابه
Fast Reconstruction Algorithm for Perturbed Compressive Sensing Based on Total Least-Squares and Proximal Splitting
We consider the problem of finding a sparse solution for an underdetermined linear system of equations when the known parameters on both sides of the system are subject to perturbation. This problem is particularly relevant to reconstruction in fully-perturbed compressive-sensing setups where both the projected measurements of an unknown sparse vector and the knowledge of the associated project...
متن کاملA New Analysis of Compressive Sensing by Stochastic Proximal Gradient Descent
In this manuscript, we analyze the sparse signal recovery (compressive sensing) problem from the perspective of convex optimization by stochastic proximal gradient descent. This view allows us to significantly simplify the recovery analysis of compressive sensing. More importantly, it leads to an efficient optimization algorithm for solving the regularized optimization problem related to the sp...
متن کاملEfficient two-dimensional compressive sensing in MIMO radar
Compressive sensing (CS) has been a way to lower sampling rate leading to data reduction for processing in multiple-input multiple-output (MIMO) radar systems. In this paper, we further reduce the computational complexity of a pulse-Doppler collocated MIMO radar by introducing a two-dimensional (2D) compressive sensing. To do so, we first introduce a new 2D formulation for the compressed receiv...
متن کاملCSWA: Aggregation-Free Spatial-Temporal Community Sensing
In this paper, we present a novel community sensing paradigm CSWA –Community Sensing Without Sensor/Location Data Aggregation. CSWA is designed to obtain the environment information (e.g., air pollution or temperature) in each subarea of the target area, without aggregating sensor and location data collected by community members. CSWA operates on top of a secured peer-to-peer network over the c...
متن کاملReducing ADC Sampling Rate with Compressive Sensing
Many communication systems involve high bandwidth, while sparse, radio frequency (RF) signals. Working with high frequency signals requires appropriate system-level components such as high-speed analog-to-digital converters (ADC). In particular, an analog signal should be sampled at rates that meet the Nyquist requirements to avoid aliasing. However, implementing high-speed ADC devices can be a...
متن کامل