In this paper, we would suggest a sparse adaptive filtering technique which is robust against Gaussian and non-Gaussian noises. To this goal, a linear combination of the least mean square and the least mean fourth loss functions has been considered as the fidelity term. Moreover, in order to promote the sparsity property of the underlying vector, we have added different sparsity-inducing penalty terms. To optimize the resultant cost function, the quasi-Newton scheme has been adopted which accelerates the convergence of the algorithm. The convergence of the proposed method has been proved analytically. Furthermore, the efficiency of the suggested scheme has been evaluated through extensive simulation scenarios which confirm the superiority of the proposed algorithm over the other state-of-the-art schemes.

中文翻译：

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稀疏混合范数自适应滤波技术

在本文中，我们将提出一种对高斯和非高斯噪声具有鲁棒性的稀疏自适应滤波技术。为了这个目标，最小均方和最小均值第四损失函数的线性组合被认为是保真度项。此外，为了提升底层向量的稀疏性，我们添加了不同的稀疏诱导惩罚项。为了优化合成的成本函数，采用了拟牛顿方案，加速了算法的收敛。所提出的方法的收敛性已被分析证明。此外，建议方案的效率已经通过广泛的模拟场景进行了评估，这证实了所提出的算法优于其他最先进的方案。