Nonstationary signal extraction based on BatOMP sparse decomposition technique

Sparse decomposition technique is a new method for nonstationary signal extraction in a noise background. To solve the problem of accuracy and efficiency exclusive in sparse decomposition, the bat algorithm combined with Orthogonal Matching Pursuits (BatOMP) was proposed to improve sparse decomposition, which can realize adaptive recognition and extraction of nonstationary signal containing random noise. Two general atoms were designed for typical signals, and dictionary training method based on correlation detection and Hilbert transform was developed. The sparse decomposition was turned into an optimizing problem by introducing bat algorithm with optimized fitness function. By contrast with several relevant methods, it was indicated that BatOMP can improve convergence speed and extraction accuracy efficiently as well as decrease the hardware requirement, which is cost effective and helps broadening the applications.

10 years of matching pursuits for inverse problems: what’s been done and what’s to come

<p><span>In the light of significant challenges like the climate change, the visualization of the gravitational potential remains a priority in geodesy. A decade ago, the Geomathematics Group Siegen proposed alternative representations </span><span>for</span><span> such problems. </span></p><p><span>The </span><span>respective</span><span> methods are based on matching pursuits: </span><span>hence,</span><span> they build a representation in a so-called best basis. However, they include additional aspects which occur, for instance, when the downward continuation of the gravitational potential is approximated.</span></p><p><span>In this talk, we summarize </span><span>the different developmental stages from 2011 up to now </span><span>which started with a basic implementation and then included aspects of orthogonality, weakness and dictionary learning, respectively. Further, we </span><span>give an outlook on ou</span><span>r</span><span> next steps </span><span>with these methods</span><span>. For the current status-quo, we show numerical results </span><span>with respect to</span><span> the downward continuation of the gravitational potential.</span></p>

The Study of Harmonic Energy Measurement Based on Atomic Decomposition Algorithm

This paper proposes a new method of harmonic energy measurement based on atomic decomposition algorithm. Firstly, the matching pursuits (MP) algorithm is introduced and harmonic atom dictionary is designed according to the characteristics of signals to be analyzed to improve the effect of signal decomposition. Then, the MP algorithm is optimized by particle swarm optimization (PSO) to reduce the calculation amount. The simulation results of active harmonic energy in steady and unsteady state demonstrate feasibility and accuracy of the presented method.