本文選題：自適應(yīng)濾波器 + 非高斯噪聲��； 參考：《計(jì)算機(jī)應(yīng)用》2017年01期

【摘要】：針對(duì)現(xiàn)有基于最小均方誤差(MSE)的塊稀疏系統(tǒng)辨識(shí)算法抗沖激性能不佳的問(wèn)題,提出了一種利用反雙曲正弦函數(shù)替代最小均方誤差的改進(jìn)型塊稀疏歸一化最小均方(IBS-NLMS)算法。該算法首先構(gòu)造新的代價(jià)函數(shù),利用負(fù)梯度最陡下降法求出增量,進(jìn)而導(dǎo)出了新的濾波器權(quán)系數(shù)更新公式,在公式迭代過(guò)程中出現(xiàn)的沖激噪聲會(huì)導(dǎo)致權(quán)系數(shù)的更新量趨于零向量,從而消除了由于非高斯沖激干擾而導(dǎo)致的算法發(fā)散問(wèn)題。同時(shí),理論分析并推導(dǎo)出了該算法的均值收斂過(guò)程。塊稀疏系統(tǒng)辨識(shí)的仿真結(jié)果表明,在非高斯沖激噪聲干擾和截?cái)嘧兓闆r下,改進(jìn)型算法與塊稀疏歸一化最小均方(BS-NLMS)算法相比有更快的收斂速度和更小的穩(wěn)態(tài)誤差。
[Abstract]:An improved block sparse normalized minimum mean square (IBS-NLMSS) algorithm using inverse hyperbolic sinusoidal function instead of minimum mean square error is proposed to solve the problem of poor impulse performance of existing block sparse system identification algorithms based on minimum mean square error (MSE).In this algorithm, a new cost function is constructed, and the increment is obtained by using the steepest descent method of negative gradient, and a new updating formula of filter weight coefficient is derived.The impulse noise in the iterative process of the formula will lead to the updating of the weight coefficient to zero vector, thus eliminating the problem of algorithm divergence caused by non- impulse interference.At the same time, the mean convergence process of the algorithm is analyzed and deduced theoretically.The simulation results of block sparse system identification show that the improved algorithm has faster convergence speed and smaller steady-state error than the block sparse normalized minimum mean square BS-NLMS algorithm in the case of non- impulse noise interference and truncation variation.
【作者單位】： 信號(hào)與信息處理重慶市重點(diǎn)實(shí)驗(yàn)室(重慶郵電大學(xué));
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(61501072) 重慶市科委自然科學(xué)基金資助項(xiàng)目(cstc2015jcyjA40027) 重慶郵電大學(xué)自然科學(xué)基金資助項(xiàng)目(A2015-60)~~
【分類號(hào)】：TN713

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