An Information-Intelligent Recursive Estimation for AdaptiveSignal Processing Networks

其目的是研究一种新的递归估计算法，它的实现涉及一种有洞察力的更新策略，而不是大多数现有算法的持续更新。模型参数估计被确定为参数空间中最优有界椭球的中心。对模型预测误差的简单检验就可以确定OBE是否需要更新。在大多数情况下，情况并非如此。通过将来自多个独立传感器的输入缓冲到一个OBE自适应信号处理器中，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.