Circuitry test response signal reconstruction based on GP-KSVD algorithm

Response signals in circuitry system always have the characteristics of high periodicity and sparse distribution. In order to realize response signals reconstruction in circuitry system, an algorithm combining gradient pursuit and K singular value decomposition (GP-KSVD) was proposed. Dictionary was trained according to the features of single and mixed signal. Making use of the updated dictionary and gradient pursuit to sparse representation on noisy signal, the reconstruction achieves the aim of de-noising. The algorithm has excellent reconstruction results with low computing complexity and storage capacity. In simulation, GP-KSVD dictionary was compared with both random and discrete cosine dictionary (DCT) dictionary, and the results show that the denoising effect of sparse representation with KSVD dictionary is the best depending on the indices of signal to noise ratio (SNR) and root mean square error (RMSE). GP-KSVD sparse representation was compared with orthogonal matching pursuit(OMP)-KSVD and preconditioning conjugate gradient pursuit(PCGP) algorithms. The simulation results prove that GP-KSVD has the minimum computer running time and the highest reconstruction precision, and the measurement verification proves the universality of the algorithm. This algorithm can be applied to response signal preprocessing, which provides theoretical basis for circuitry system equipment performance evaluation analysis.