卡尔曼滤波算法存在线性计算与数值计算两类参数递推模式，分别用于线性系统与非线性系统的滤波参数计算。对于同时存在线性变换与非线性变换的混合非线性系统，基于单一参数递推模式的传统滤波器在滤波处理时存在滤波精度与计算复杂度无法同时兼顾、高维度系统实时解算难以工程实现等问题。针对上述问题，提出了一种双模并行滤波器，该滤波器能够同时采用线性计算与数值计算两种参数递推模式，分别对系统中的线性与非线性状态变量进行并行滤波处理，并通过解算线性与非线性状态的互协方差矩阵实现数据融合，能够在不损失任何非线性精度的前提下节省线性状态的计算成本。进一步，针对数值计算模式数值稳定性差的问题，基于QR分解、SVD分解方法设计了多种适用于双模并行滤波器的数值稳定性增强方法，并给出了各类增强方法在不同形式的混合非线性系统中的使用原则。仿真结果表明，该算法在保证滤波精度的前提下，计算效率得到了显著提升。

Kalman filtering algorithms employ two distinct parameter update modes: analytical propagation for linear sys-tems and numerical approximation for nonlinear systems. When applied to mixed nonlinear systems with both linear and nonlinear transformations, single-mode filtering architectures face limitations in simultaneously maintaining estimation accuracy and computational efficiency, and the real-time solution of high-dimensional systems is difficult to realize in engineering. To address these limitations, a dual-mode parallel filter is proposed. This filter concurrently executes analytical and numerical propagation for linear and nonlinear state variables, respectively. Through cross-covariance matrix resolution, the filter achieves data fusion, which can save the computation cost of linear states without losing any nonlinear accuracy. Furthermore, aiming at the problem of poor numerical stability when using Cholesky decomposition to obtain the square root matrix in the numerical calculation mode, a variety of numerical stability enhancement methods suitable for dual-mode parallel filters are designed based on QR decomposition and SVD decomposition methods. Besides, the application principles of stability enhancement methods in different forms of mixed nonlinear systems are given. The simulation re-sults show that the filtering accuracy of the proposed algorithm is consistent with that of the traditional single-mode global nonlinear filter, and the computational efficiency is significantly improved.