Markovian Adaptive Filtering Algorithm for Block-Sparse System Identification
Zahra Habibi, Hadi Zayyani
Abstract
In this brief, a novel adaptive filtering algorithm for block-sparse system identification called, Block-Sparse Adaptive Bayesian Algorithm (BS-ABA) is proposed. We use a Gaussian Markov (GM) model to generate the unknown block-sparse system. In the proposed algorithm, a maximum a posteriori (MAP) estimation procedure is used to estimate the adaptive filter coefficients which characterized by a Gaussian Mixture Markov (GMM) model. Moreover, the convergence in the mean of the proposed algorithm is provided in this brief. Simulation results show that the computational complexity of the proposed algorithm is less than some recent algorithms, while the performance is comparable to them.
Topics & Concepts
AlgorithmBlock (permutation group theory)Maximum a posteriori estimationComputer scienceAdaptive filterConvergence (economics)GaussianA priori and a posterioriIdentification (biology)MathematicsMaximum likelihoodStatisticsPhysicsEconomic growthBotanyEpistemologyGeometryBiologyEconomicsQuantum mechanicsPhilosophyAdvanced Adaptive Filtering TechniquesBlind Source Separation TechniquesSpeech and Audio Processing