MCA Learning Algorithm for Incident Signals Estimation: A Review
This work addresses signal estimation for applications like telecommunications and antenna array processing, but it appears incremental as it builds on existing MCA methods.
The paper tackles the problem of estimating incident signals by proposing a Minor Component Analysis (MCA) learning algorithm with a specific learning rate parameter to ensure fast convergence, and it presents simulation results to illustrate the theoretical findings.
Recently there has been many works on adaptive subspace filtering in the signal processing literature. Most of them are concerned with tracking the signal subspace spanned by the eigenvectors corresponding to the eigenvalues of the covariance matrix of the signal plus noise data. Minor Component Analysis (MCA) is important tool and has a wide application in telecommunications, antenna array processing, statistical parametric estimation, etc. As an important feature extraction technique, MCA is a statistical method of extracting the eigenvector associated with the smallest eigenvalue of the covariance matrix. In this paper, we will present a MCA learning algorithm to extract minor component from input signals, and the learning rate parameter is also presented, which ensures fast convergence of the algorithm, because it has direct effect on the convergence of the weight vector and the error level is affected by this value. MCA is performed to determine the estimated DOA. Simulation results will be furnished to illustrate the theoretical results achieved.