Variational Compensation Based Nonlinear Filter for Continuous-Discrete Stochastic Systems

机译：连续离散随机系统的基于变分补偿的非线性滤波器

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In this paper, a novel variational compensation based nonlinear filter (VCNF) is proposed to cope with the nonlinear filtering problem in continuous-discrete systems. The core of VCNF is to construct a variational state compensation model with variational compensation parameters for accurately describing uncertain continuous state. The role of variational compensation parameters is to adaptively compensate the unpredictable approximation and discretization errors of system states. In the variational Bayesian framework, through iteratively and alternatively achieving the fitting of the state priori model and the compensation of approximation and discretization errors, estimation accuracy and adaptiveness can be enhanced gradually. The superior performance of VCNF is demonstrated in the simulation of target tracking.

机译：为了解决连续离散系统中的非线性滤波问题，提出了一种基于变分补偿的非线性滤波器（VCNF）。 VCNF的核心是构建具有变化补偿参数的变化状态补偿模型，以准确描述不确定的连续状态。变分补偿参数的作用是自适应补偿系统状态的不可预测的近似和离散误差。在变分贝叶斯框架中，通过迭代地或交替地实现状态先验模型的拟合以及近似和离散化误差的补偿，估计精度和自适应性可以逐渐提高。在目标跟踪的仿真中证明了VCNF的优越性能。

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《International Conference on Information Fusion》|2020年|1-7|共7页
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Tingjun Wang; Haoran Cui; Xiaoxu Wang;
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Adaptation models; Estimation; Time measurement; Differential equations; Probability; Stochastic systems; Mathematical model;

机译：适应模型;估计;时间测量;微分方程;概率;随机系统;数学模型;

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机译：随机混合和非线性系统的非线性滤波及其在目标跟踪中的应用。
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机译：非线性随机系统的实用控制算法及非线性滤波器的研究

Variational Compensation Based Nonlinear Filter for Continuous-Discrete Stochastic Systems

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