This article develops the modified extended Kalman filter based recursive estimation algorithms for Wiener nonlinear systems with process noise and measurement noise. The prior estimate of the linear block output is computed based on the auxiliary model, and the posterior estimate is updated by designing a modified extended Kalman filter. A multi‐innovation gradient algorithm and a recursive least squares algorithm are derived to estimate the parameters of the linear subsystem, respectively. The simulation examples are provided to demonstrate the effectiveness of the proposed algorithms.

中文翻译：

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具有过程噪声和测量噪声的维纳非线性系统基于改进扩展卡尔曼滤波器的递归估计

本文针对具有过程噪声和测量噪声的Wiener非线性系统，开发了基于改进的扩展卡尔曼滤波器的递归估计算法。基于辅助模型计算线性块输出的先验估计，并通过设计改进的扩展卡尔曼滤波器来更新后验估计。推导了多元创新梯度算法和递归最小二乘算法分别估计线性子系统的参数。提供了仿真示例，以证明所提出算法的有效性。