Recursive updating the eigenvalue decomposition of a covariance matrix
Stack Exchange network consists of 175 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. Visit Stack Exchange A naive approach is to use the eigenvalue solution of your matrix $A(t)$ as the initial guess of an iterative eigensolver for matrix $A(t \delta t)$. New iterative methods for solutions of the eigenproblem.
Adaptive receiving arrays for radar maximize the ratio of antenna gain in a specified scan direction to the total noise in the output signal. This leads to large apertures to achieve angular resolution. The present invention relates to radar systems and methods generally, and more specifically to methods of detecting a jammer. The frequency dependence of the RCS encourages use of lower microwave frequency bands for detection. Adaptive spectral estimation by the conjugate gradient method. Here's a couple of relevant references: Adaptive Eigendecomposition of Data Covariance Matrices Based on First-Order Perturbations (Champagne, IEEE TSP 42(10) 1994) Recursive updating the eigenvalue decomposition of a covariance matrix (Yu, IEEE TSP, 39(5) 1991) Online Principal Component Analysis in High Dimension: Which Algorithm to Choose?
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