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Recursive two dimensional spectral estimation based on an AR model excited by a T-distribution process using QR decomposition approach

机译:基于QR模型分解的T分布过程激发的AR模型的二维二维光谱估计

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In this paper a robust two dimensional spectral estimation based on an AR model is proposed. The robustness of the method is obtained by assuming that the output or the residual signal has an independently and identically distribution with a t probability density function. By doing so, the effect of large amplitude residuals can be reduced. To reduce the calculation burden, the optimal solution is recursively calculated by incorporating the QR-decomposition method. The simulation results show that the obtained spectral estimate after 100 by 100 pixels iterations by using small /spl alpha/; i.e. /spl alpha/=3; is more accurate than that obtained by using large /spl alpha/; i.e. /spl alpha/=/spl infin/. The plots of the mean square error (MSE) to the iteration number show that by using small /spl alpha/ we can obtain a smaller MSE than that by using large /spl alpha/.
机译:本文提出了一种基于AR模型的鲁棒二维频谱估计方法。通过假设输出或残差信号具有t概率密度函数的独立且相同的分布来获得该方法的鲁棒性。这样,可以减小大幅度残差的影响。为了减少计算负担,通过合并QR分解方法来递归计算最优解。仿真结果表明,使用小/ spl alpha /,可以得到100 x 100像素迭代后的光谱估计值。即/ spl alpha / = 3;比使用大的/ spl alpha /更准确;即/ spl alpha / = / spl infin /。均方误差(MSE)与迭代次数的关系图显示,与使用较大的/ spl alpha /相比,使用较小的/ spl alpha /可以得到较小的MSE。

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