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A sparse bias-compensated least generalized mixed norm algorithm is developed for sparse system identification (SSI) with noisy input. The lp-norm and lq-norm are mixed to define a novel cost function (called generalized mixed norm, GMN) for adaptive filtering algorithm (AFA) in system identification. Furthermore, a bias-compensated least GMN algorithm is proposed for the case that the input is corrupted...
A bias-compensated normalized least mean absolute deviation (NLMAD) algorithm is developed for system identification under impulsive output measurement noise and noisy input environment, which takes the advantage of the NLMAD to resist impulsive output noises. Considering biased estimation caused by the noisy input, we employ an unbiasedness criterion to obtain a bias-compensated vector for NLMAD...
Proportionate-type adaptive filtering (PtAF) algorithms have been successfully applied to sparse system identification. The major drawback of the traditional PtAF algorithms based on the mean square error (MSE) criterion show poor robustness in the presence of impulsive noises or abrupt changes because MSE is only valid and rational under Gaussian assumption. However, this assumption is not satisfied...
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