An improved LS algorithm for the estimation of an impulsive noise corrupted signal by linear programming
作者： E.D. DiClaudio , G. Orlandi , F. Piazza , A. Uncini
DOI: 10.1109/ISCAS.1991.176434
关键词: Algorithm 、 Noise reduction 、 Robustness (computer science) 、 Least squares 、 Mathematics 、 Estimation methods 、 White noise 、 Linear programming 、 Signal processing 、 Dynamic range
摘要: Least squares (LS) algorithms are often used in many spectrum estimation methods. However, when the signals contaminated by a few strong noise spikes, standard LS algorithm can easily lead to biased solutions characterized strongly reduced dynamic range of estimated spectra. In order treat this problem, classical approach is weight prediction errors before applying minimization algorithm. present work procedure for assigning optimal weights equations presented. The set computed, linear programming techniques, reduce effects impulsive noise. demonstrate capability proposed approach, has been tested with corrupted stationary white noise, additive and combination both. results show high degree robustness that makes method attractive automatic analysis real-world data. >
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