ALGORITHM FOR LINEARLY CONSTRAINED ADAPTIVE ARRAY PROCESSING
ALGORITHM FOR LINEARLY CONSTRAINED ADAPTIVE ARRAY PROCESSING
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DOI：
10.1109/proc.1972.8817
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发表时间：
1972-01-01
期刊：
影响因子：
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通讯作者：
FROST, OL
中科院分区：
文献类型：
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作者：
FROST, OL
本文推导了一种约束最小均方算法，它能在真实的时间内调整传感器阵列，使其对来自某一方向的信号作出响应，同时能对来自其它方向的噪声作出区分。分析和计算机仿真证实，该算法能够迭代地适应传感器阵列的抽头上的可变权重，以最小化阵列输出中的噪声功率。对权重的一组线性等式约束保持阵列在感兴趣的方向上的选定频率特性。阵列问题将是一个经典的约束最小均方问题，除了信号和噪声的统计假设是未知的先验。几何表示表明，该算法是能够保持的约束，并防止在数字实现的量化误差的积累。
A constrained least mean-squares algorithm has been derived which is capable of adjusting an array of sensors in real time to respond to a signal coming from a desired direction while discriminating against noises coming from other directions. Analysis and computer simulations confirm that the algorithm is able to iteratively adapt variable weights on the taps of the sensor array to minimize noise power in the array output. A set of linear equality constraints on the weights maintains a chosen frequency characteristic for the array in the direction of interest. The array problem would be a classical constrained least-mean-squares problem except that the signal and noise statistics are assumed unknown a priori. A geometrical presentation shows that the algorithm is able to maintain the constraints and prevent the accumulation of quantization errors in a digital implementation.