DOA Direction Estimation Based on Compressed Sensing

Compressed sensing technology is an emerging discipline with extensive applications in information technology. Applying compressed sensing to Direction of Arrival (DOA) estimation represents a novel approach. This method utilizes microphone arrays for far-field narrowband signal estimation, offering significant practical value. Implementation typically involves constructing a sparse representation of the signal space and solving underdetermined systems using optimization algorithms like L1-norm minimization, which enables accurate DOA estimation with fewer measurements than traditional methods.

Furthermore, compressed sensing technology finds applications in image processing, video compression, and speech signal processing for efficient signal acquisition, processing, and transmission. In image-related domains, compressed sensing can be implemented through sparse transforms (like wavelet or DCT) and reconstruction algorithms for image compression, reconstruction, and enhancement. For video applications, it facilitates efficient video encoding and compression through frame-by-frame sparse representation and recovery algorithms. In speech processing, compressed sensing techniques enable efficient acquisition and processing of speech signals using sparse coding and reconstruction methods.

Consequently, compressed sensing technology demonstrates vast application prospects, providing substantial support and facilitation for research and development across multiple technical domains. The core implementation often involves mathematical optimization, sparse recovery algorithms, and efficient sensing matrix design to achieve superior performance with reduced computational resources.