DOA Direction Estimation Based on Compressed Sensing

Compressed sensing has broad applications in information technology and represents a cutting-edge discipline. Applying compressed sensing to DOA (Direction of Arrival) estimation introduces an innovative methodology, particularly effective for far-field narrowband signal estimation using microphone arrays. The implementation typically involves sparse signal reconstruction algorithms and optimization techniques to achieve accurate angular resolution with reduced sensor data requirements.

Research on Image Fusion Technology Based on Compressed Sensing

With the advancement of compressed sensing technology, research on compressed sensing-based image fusion has gained increasing attention. Leveraging the characteristics of image Fourier transform coefficients, this study proposes a compressed sensing domain image fusion algorithm based on high-frequency and low-frequency importance metrics under a dual-star sampling mode. The algorithm begins by acquiring measurements through dual-star sampling, then calculates importance metrics for high- and low-frequency regions as fusion operators, performs weighted fusion of the measurements, and finally reconstructs the fused image by solving a minimum total variation optimization problem. Subjective and objective experimental results demonstrate that this algorithm outperforms other Fourier-based approaches, with implementations involving sparse sampling and convex optimization techniques.