This paper presents a fast method for sparse decomposition of real second-order polynomial phase signals (PPSs). In the method, we first set three concatenate dictionaries to complete the sparse decomposition of real second-order polynomial phase signals. Three concatenate dictionaries are the frequency modulation dictionary, the frequency dictionary and the phase dictionary, respectively. Seco...

In this article, we discuss a novel greedy algorithm for the recovery of compressive sampled signals under noisy conditions. Most of the greedy recovery algorithms proposed in the literature require sparsity of the signal to be known or they estimate sparsity, for a known representation basis, from the number of measurements. These algorithms recover signals when noise level is significantly lo...

the most current pursuit algorithms for moving targets which are presented so far in the literatureare pure pursuit and pure rendezvous navigations. recently, one of the present authors has introduced ageometric model for the pure pursuit navigation algorithm. here, in this paper, we study a new algorithm forthe pursuit navigation problem which is a combination of both of the above algorithms. ...

Sign truncated matching pursuit (STrMP) algorithm is presented in this paper. STrMP is a new greedy algorithm for the recovery of sparse signals from the sign measurement, which combines the principle of consistent reconstruction with orthogonal matching pursuit (OMP). The main part of STrMP is as concise as OMP and hence STrMP is simple to implement. In contrast to previous greedy algorithms f...

The Compressed Sensing framework aims to recover a sparse signal from a small set of projections onto random vectors; the problem reduces to searching for a sparse approximation of this measurement vector. Conventional solutions involve linear programming or greedy algorithms and can be computationally expensive. These techniques are generic, however, and assume no structure in the signal aside...

Finding the best K-sparse approximation of a signal in redundant dictionary is an NP-hard problem. Suboptimal greedy matching pursuit (MP) algorithms are generally used for this task. In work, we present acceleration technique and implementation algorithm acting on multi-Gabor dictionary, i.e., concatenation several Gabor-type time-frequency dictionaries, each which consisting translations modu...

Ultrasonic guided wave is an effective tool for structural health monitoring of structures for detecting defects. In practice, guided wave signals are dispersive and contain multiple modes and noise. In the presence of overlapped wave-packets/modes and noise together with dispersion, extracting meaningful information from these signals is a challenging task. Handling such challenge requires an ...

Recent work on sparse approximation has focused on the theoretical performance of algorithms for random inputs. This average-case behavior is typically far better than the behavior of the algorithm for the worst inputs. Moreover, an average-case analysis fits naturally with the type of signals that arise in certain applications, such as wireless communications. This paper describes what is curr...

This paper proposes a video communication scheme combing the multiple description coding (MDC) using scalar quantizers with the matching pursuit (MP) to transmit the video over the Internet. The parameters obtained after MP, called atoms, are encoded into two balanced descriptions, which are transmitted over two separate channels. Simulation results show that our proposed method surpasses MP si...

Usually a block-based DCT transform is used in hybrid video coding to decompose motion compensated residuals. Another method, which has been successfully applied to the coding of video residuals, is Matching Pursuit (MP). Matching Pursuit is an overcomplete expansion technique providing some advantages over orthogonal transforms like DCT. In this paper we discuss the coding efficiency of a MP c...