On fast estimation of direction of arrival for underwater acoustic target based on sparse Bayesian learning

The Direction of Arrival （DOA） estimation methods for underwater acoustic target using Temporally Multiple Sparse Bayesian Learning （TMSBL） as the reconstructing algorithm have the disadvantage of slow computing speed. To solve this problem, a fast underwater acoustic target direction of arrival estimation was proposed. Analyzing the model characteristics of block-sparse Bayesian learning framework for DOA estimation, an algorithm was proposed to obtain the value of core hyper-parameter through MacKay＇s fixed-point method to estimate the DOA. By this process, it will spend less time for computation and provide more superior recovery performance than TMSBL algorithm. Simulation results verified the feasibility and effectiveness of the proposed algorithm.

Study on the key technology of spectral reflectivity reconstruction based on sparse prior by a single-pixel detector

By studying the traditional spectral reflectance reconstruction method, spectral reflectance and the relative spectral power distribution of a lighting source are sparsely decomposed, and the orthogonal property of the principal component orthogonal basis is used to eliminate basis; then spectral reflectance data are obtained by solving a sparse coefficient. After theoretical analysis, the spectral reflectance reconstruction based on sparse prior knowledge of the principal component orthogonal basis by a single-pixel detector is carried out by software simulation and experiment. It can reduce the complexity and cost of the system, and has certain significance for the improvement of multispectral image acquisition technology.

Compressed sensing reconstruction of sparse spectrum based on digital micro-mirror device platform

A new method which employs compressive sensing(CS) to reconstruct the sparse spectrum is designed and experimentally demonstrated. On the basis of CS theory, the simulation results indicate that the probability of reconstruction is high when the step of the sparsity adaptive matching pursuit algorithm is confirmed as 1. Contrastive analysis for four kinds of commonly used measurement matrices: part Hadamard, Bernoulli, Toeplitz and Circular matrix, has been conducted. The results illustrate that the part Hadamard matrix has better performance of reconstruction than the other matrices. The experimental system of the spectral compression reconstruction is mainly based on the digital micro-mirror device(DMD). The experimental results prove that CS can reconstruct sparse spectrum well under the condition of 50% sampling rate. The system error 0.0781 is obtained, which is defined by the average value of the 2-norm. Furthermore, the proposed method shows a dominant ability to discard redundancy.