This paper studies the statistical behavior of the affine projection (AP) algorithm for /spl mu/=1 for Gaussian autoregressive inputs. This work extends the theoretical results of Rupp (1998) to the numerical evaluation of the MSE learning curves for the adaptive AP weights. The MSE learning behavior of the AP(P+1) algorithm with an AR(Q) input (Q/spl les/P) is shown to be the same as the NLMS algorithm (/spl mu/=1) with a white input with M-P unity eigenvalues and P zero eigenvalues and increased observation noise. Monte Carlo simulations are presented which support the theoretical results.
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机译：本文研究了高斯自回归输入的/ spl mu / = 1的仿射投影（AP）算法的统计行为。这项工作将Rupp（1998）的理论结果扩展到了针对自适应AP权重的MSE学习曲线的数值评估。显示具有AR（Q）输入（Q / spl les / P）的AP（P + 1）算法的MSE学习行为与具有白色输入的NLMS算法（/ spl mu / = 1）相同具有MP单位特征值和P个零特征值，并增加了观察噪声。提出了支持理论结果的蒙特卡洛模拟。
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