DOA Estimation for Coherent Signals Using Spatial Smoothing Algorithm

In this field, spatial smoothing algorithms are widely employed for Direction of Arrival (DOA) estimation of coherent signals. The core implementation typically involves dividing the sensor array into overlapping subarrays and averaging their covariance matrices to decorrelate coherent sources. Beyond the fundamental approach, numerous enhanced algorithms exist, including methods based on covariance matrix decomposition techniques (such as eigenvalue decomposition or singular value decomposition) and algorithms exploiting signal sparsity characteristics through compressive sensing frameworks. These algorithms demonstrate significant advantages in effectively handling both Gaussian and non-Gaussian noise environments, while substantially improving DOA estimation accuracy through optimized matrix operations and statistical processing. Key MATLAB functions often involve svd() for matrix decomposition, cov() for covariance computation, and optimization tools for sparse recovery. Furthermore, these advanced algorithms find practical applications in medical imaging systems for source localization and sonar signal processing for underwater acoustic analysis, providing valuable technical support for research in these domains. Researchers are encouraged to experiment with these algorithms to enhance their projects through customizable parameter tuning and algorithm hybridization approaches.