On the Foundation of Sparse Sensing (Part I): Necessary and Sufficient Sampling Theory and Robust Remaindering Problem

by   Hanshen Xiao, et al.

In the first part of the series papers, we set out to answer the following question: given specific restrictions on a set of samplers, what kind of signal can be uniquely represented by the corresponding samples attained, as the foundation of sparse sensing. It is different from compressed sensing, which exploits the sparse representation of a signal to reduce sample complexity (compressed sampling or acquisition). We use sparse sensing to denote a board concept of methods whose main focus is to improve the efficiency and cost of sampling implementation itself. The "sparse" here is referred to sampling at a low temporal or spatial rate (sparsity constrained sampling or acquisition), which in practice models cheaper hardware such as lower power, less memory and throughput. We take frequency and direction of arrival (DoA) estimation as concrete examples and give the necessary and sufficient requirements of the sampling strategy. Interestingly, we prove that these problems can be reduced to some (multiple) remainder model. As a straightforward corollary, we supplement and complete the theory of co-prime sampling, which receives considerable attention over last decade. On the other hand, we advance the understanding of the robust multiple remainder problem, which models the case when sampling with noise. A sharpened tradeoff between the parameter dynamic range and the error bound is derived. We prove that, for N-frequency estimation in either complex or real waveforms, once the least common multiple (lcm) of the sampling rates selected is sufficiently large, one may approach an error tolerance bound independent of N.


On the Foundation of Sparse Sensing (Part II): Diophantine Sampling and Array Configuration

In the second part of the series papers, we set out to study the algorit...

Sparse Multiband Signal Acquisition Receiver with Co-prime Sampling

Cognitive radio (CR) requires spectrum sensing over a broad frequency ba...

From Co-prime to the Diophantine Equation Based Sparse Sensing in Complex Waveforms

For frequency estimation, the co-prime sampling tells that in time domai...

Coherence and sufficient sampling densities for reconstruction in compressed sensing

We give a new, very general, formulation of the compressed sensing probl...

Solve-Select-Scale: A Three Step Process For Sparse Signal Estimation

In the theory of compressed sensing (CS), the sparsity x_0 of the unknow...

The constrained Dantzig selector with enhanced consistency

The Dantzig selector has received popularity for many applications such ...

Please sign up or login with your details

Forgot password? Click here to reset