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Experiments in Adaptive Estimation of Unknown Binary Waveforms

机译：未知二进制波形的自适应估计实验

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摘要

A special-purpose adaptive machine is described which carries out estimation in real time of an unknown binary waveform which is perturbed with additive Gaussian noise. Unknown waveforms of over 103 samples in duration can be recovered. The unknown waveforms are of unknown epoch and can reappear at either random or periodic time intervals. The observed signal is received at moderate or low signal-to-noise ratios so that a single observation of the received data (even if one knew the precise signal arrival time) is not sufficient to provide a good estimate of the signal waveshape. Experimental results are described which show transient behavior waveform estimate. The transient behavior is expressed as the number of errors in the current estimate of the signal plotted vs. time. In a noisy environment, each ``learning'' transient is a random time function. These learning transients are shown for several different signal-to-noise ratios and indicate the threshold noise levels for various types of initial states of the machine memory.
机译：描述了一种专用自适应机器，该机器实时执行未知二进制波形的估计，该未知二进制波形会受到加性高斯噪声的干扰。可以恢复持续时间超过103个样本的未知波形。未知波形具有未知时期，并且可以随机或周期性时间间隔重新出现。所观察到的信号是以中等或低信噪比接收的，因此对所接收数据的单次观察（即使有人知道精确的信号到达时间）不足以提供对信号波形的良好估计。描述了显示瞬态行为波形估计的实验结果。瞬态行为表示为绘制的信号当前估计中的误差数量与时间的关系。在嘈杂的环境中，每个``学习''瞬变都是随机时间函数。这些学习瞬变针对几种不同的信噪比显示，并指​​示机器存储器各种初始状态的阈值噪声水平。

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