Localization is highly critical for wireless sensor network applications. The present paper makes the following noticeable contributions. First, an energy confirmable overlapping tracking algorithm for mobile targets is proposed in wireless sensor networks. Different from most target localization algorithms based on compressive sensing, it improves localization accuracy through overlapping area...

In the present time bandwidth efficiency is one of the most important parameter to measure different modulation scheme in digital communication. Due to the separation of waveforms of all the existing modulation schemes in time domain it becomes difficult to improve their bandwidth efficiency. Hence it was become very important for researchers to proposed such a scheme that able to improves the ...

Explicitly using the block structure of the unknown signal can achieve better recovery performance in compressive censing. An unknown signal with block structure can be accurately recovered from underdetermined linear measurements provided that it is sufficiently block sparse. However, in practice, the block sparsity level is typically unknown. In this paper, we consider a soft measure of block...

This letter proposes a novel distributed compressed estimation scheme for sparse signals and systems based on compressive sensing techniques. The proposed scheme consists of compression and decompression modules inspired by compressive sensing to perform distributed compressed estimation. A design procedure is also presented and an algorithm is developed to optimize measurement matrices, which ...

We review compressive sensing and its extension to classification and joint signal recovery. We present an overview of compressed sensing, followed by some simulation results on perfect reconstruction for sparse signals. We review previous work on compressed signal classification and discuss relations between the two earlier papers. Finally, we discuss joint signal reconstruction for compressed...

Internet of Things is the network of interconnection between people and things, and between things themselves by embedding additional gadgets, such as sensors, RFID tags. Mass data are usually collected, transmitted, processed and stored in Internet of Things. In this paper, a novel sampling method, i.e. compressed sensing, is used in processing mass data in Internet of Things. Compressed sensi...

Traditionally, compressed sensing assumes a linear, ill-posed or non-invertible forward model, which is inverted with the help of non-convex constraints. Recently these ideas have been extended to non-linear forward models. It could be shown that, under certain conditions, strong performance guarantees available for traditional compressed sensing also hold in the non-linear case. In this paper ...

In Compressed Sensing [9], we consider a signal that is compressible with respect to some dictionary of ’s, that is, its information is concentrated in coefficients . The goal is to reconstruct such signals using only a few measurements , for carefully chosen ’s which depend on . Known results [9], [3], [21] prove that there exists a single measurement matrix such that any compressible signal c...

We consider faithfully combining phase retrieval with classical compressed sensing. Inspired by the recent novel formulation for phase retrieval called PhaseMax, we present and analyze SparsePhaseMax, a linear program for phaseless compressed sensing in the natural parameter space. We establish that when provided with an initialization that correlates with an arbitrary k-sparse n-vector, Sparse...