Updating a linear least squares solution can be critical for near real-time signalprocessing applications. The Greville algorithm proposes simple formula updating the pseudoinverse of matrix A $\in$ R nxm with rank r. In this paper, we explicitly derive similar by maintaining general factorization, which call rank-Greville. Based on formula, implemented recursive exploiting rank-deficiency A, a...

The hyperspectral image compression scheme is a trade-off between the limited hardware resources of on-board platform and ever-growing resolution optical instruments. Predictive coding attracts researchers due to its low computational complexity moderate memory requirements. We propose near-lossless prediction-based that removes spatial spectral redundant information, thereby significantly redu...

For least mean-square (LMS) algorithm applications, it is important to improve the speed of convergence vs the residual error trade-off imposed by the selection of a certain value for the step size. In this paper, we propose to use a mixture approach, adaptively combining two independent LMS filters with large and small step sizes to obtain fast convergence with low misadjustment during station...

Traditional recursive least squares (RLS) adaptive filtering is widely used to estimate the impulse responses (IR) of an unknown system. Nevertheless, RLS estimator shows poor performance when tracking rapidly time-varying systems. In this paper, we propose a multi-layered (m-RLS) address concern. The m-RLS composed multiple estimators, each which employed and eliminate misadjustment previous l...