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Subband IPNLMS for blind adaptive MIMO filtering with sparse impulse response systems

机译:用于稀疏脉冲响应系统的盲自适应MIMO滤波的子带IPNLMS

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In acoustic signal processing, the improved proportionate normalized least mean square (IPNLMS) is known as an effective adaptive algorithm for sparse impulse response systems. And it is known that the subband adaptive filter provides fast convergence rate, because of its data prewhitening characteristic. In this paper, we propose a subband blind adaptive algorithm for multi-inputs multi-outputs (MIMO) systems. The proposed algorithm (subband IPNLMS) combines subband filtering technique with IPNLMS algorithm. In this approach, subband adaptive filtering is employed to overcome the problems in long adaptive filters such as computational complexity and slow convergence rate. Simulation results show that the subband IPNLMS performs better than the subband NLMS, when the blind channel impulse response is sparse.
机译:在声信号处理中,改进的比例归一化最小均方(IPNLMS)被称为稀疏脉冲响应系统的有效自适应算法。并且众所周知,子带自适应滤波器由于其数据预白化特性而提供了快速的收敛速度。在本文中,我们提出了一种用于多输入多输出(MIMO)系统的子带盲自适应算法。所提出的算法(子带IPNLMS)将子带滤波技术与IPNLMS算法相结合。在这种方法中,采用子带自适应滤波来克服长自适应滤波器中的问题,例如计算复杂度和收敛速度慢。仿真结果表明,在盲信道冲激响应较稀疏的情况下,子带IPNLMS的性能优于子带NLMS。

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