Incremental Least Squares Methods and the Extended Kalman Filter
本文提出并分析了一种逐块处理数据的增量非线性最小二乘法，重点研究了扩展卡尔曼滤波作为高斯-牛顿法增量版本的收敛性质，并讨论了加速收敛的变体。
In this paper we propose and analyze nonlinear least squares methods which process the data incrementally, one data block at a time. Such methods are well suited for large data sets and real time operation and have received much attention in the context of neural network training problems. We focus on the extended Kalman filter, which may be viewed as an incremental version of the Gauss–Newton method. We provide a nonstochastic analysis of its convergence properties, and we discuss variants aimed at accelerating its convergence.