An improved nonlinear adaptive state estimator called Adaptive Smooth Variable Structure Filter ASVSF has been proposed and its algorithm described. ASVSF extends the functionality and performance of a previously reported robust smooth variable structure filter (SVSF) with optimal boundary layer (SVSF-OBL). Improvement in performance includes the provision for accommodating unknown and time varying process noise covariance, which generally characterizes modelling uncertainty. The novelty of this proposed ASVSF estimator, which inherits the features of the SVSF, is that it adaptively provides an estimate of the unknown time varying process noise covariance (and hence called adaptive SVSF or ASVSF) which is required for determining the optimal boundary layer width of SVSF-OBL thus obviating the need of the prior knowledge of the process noise covariance. This makes the proposed estimator performance to be insensitive to (and therefore robust with respect to) unknown time varying process noise covariance while retaining the optimality of SVSF-OBL. The performance of the proposed ASVSF estimator is evaluated using Monte Carlo simulation and is compared with previously reported state estimators using a case of maneuvering civilian aircraft where a simplified and grossly approximate process model is used in the estimator/filter for tracking and thereby generating a time varying and unknown process noise covariance situation. Three different measures of Root Mean Square (RMS) error over the trajectory have been used for comparison which demonstrates the strengths of the proposed ASVSF estimator.

提出了一种改进的非线性自适应状态估计器，称为自适应平滑可变结构滤波器 ASVSF，并描述了它的算法。ASVSF 扩展了先前报道的具有最佳边界层 (SVSF-OBL) 的鲁棒平滑可变结构滤波器 (SVSF) 的功能和性能。性能的改进包括提供适应未知和随时间变化的过程噪声协方差，这通常表征建模不确定性。这个提议的 ASVSF 估计器的新颖性，它继承了 SVSF 的特征，是它自适应地提供了对未知时变过程噪声协方差的估计（因此称为自适应 SVSF 或 ASVSF），这是确定 SVSF-OBL 的最佳边界层宽度所需的，从而消除了对过程噪声先验知识的需要协方差。这使得所提出的估计器性能对未知的时变过程噪声协方差不敏感（因此相对于）不敏感，同时保持 SVSF-OBL 的最优性。所提出的 ASVSF 估计器的性能使用蒙特卡罗模拟进行评估，并与先前报告的使用机动民用飞机的状态估计器进行比较，其中在估计器/滤波器中使用简化且大致近似的过程模型进行跟踪，从而生成时间变化和未知的过程噪声协方差情况。轨迹上的三种不同的均方根 (RMS) 误差度量已用于比较，这证明了所提出的 ASVSF 估计器的优势。