Sparse Recovery from Inaccurate Saturated Measurements

This article studies a variation of the standard compressive sensing problem, in which sparse vectors \(\mathbf{x}\in\mathbb{R}^{N}\) are acquired through inaccurate saturated measurements \(\mathbf{y}= \mathcal{S}(\mathbf {A}\mathbf{x}+ \mathbf{e}) \in\mathbb{R}^{m}\), \(m \ll N\). The saturation function \(\mathcal{S}\) acts componentwise by sending entries that are large in absolute value to plus-or-minus a threshold while keeping the other entries unchanged. The present study focuses on the effect of the presaturation error \(\mathbf{e}\in\mathbb{R}^{m}\). The existing theory for accurate saturated measurements, i.e., the case \(\mathbf{e}= \mathbf{0}\), which exhibits two regimes depending on the magnitude of \(\mathbf{x}\in\mathbb {R}^{N}\), is extended here. A recovery procedure based on convex optimization is proposed and shown to be robust to presaturation error in both regimes. Another procedure ignoring the presaturation error is also analyzed and shown to be robust in the small magnitude regime.