An Information-Intelligent Recursive Estimation for AdaptiveSignal Processing Networks

目的是研究一种新的递推估计 一种算法，其实现涉及识别更新 战略，而不是不断更新大多数现有的 算法 模型参数估计值确定为 参数空间中的最优边界椭球（OBE）的中心。 对模型预测误差的一个简单测试可以确定 出窍经验是否需要更新。 大多数时候它并不。 通过 将来自多个独立传感器的输入缓冲为一个 OBE自适应信号处理器，可以充分利用 这种新方法可能节省的计算量。

The purpose is to investigate a novel recursive estimation algorithm whose implementation involves a discerning update strategy, as opposed to the continual update of most existing algorithms. Model parameter estimates are determined as the center of an optimal bounding ellipsoid (OBE) in parameter space. A simple test on the model prediction error determines whether the OBE needs updating or not. Most of the time it does not. By buffering the inputs from several independent sensors into one OBE adaptive signal processor, one could take full advantage of the possible computational savings of this new approach.