Evidential reasoning for adaptive state and parameter estimation in nonlinear systems

非线性系统中自适应状态和参数估计的证据推理

• 批准号: 227726-2007
• 负责人: Aitken, Victor
• 金额: $ 1.09万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2011
• 资助国家: 加拿大
• 起止时间: 2011-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=478588
• 关键词: Evidential; reasoning; adaptive; state; parameter

State and parameter estimation is fundamental to control and modeling in many disciplines. Parameter estimation is needed to implement system models that, together with estimates of the current state, are used to formulate intelligent decisions based on sensor information, to compute feedback control signals, to adapt the model or control algorithms and/or parameters, and to build a representation of the operational environment. Classical approaches to state and parameter estimation, such as Kalman or related Bayesian methods, are largely based on linear Gaussian assumptions under which it is sufficient to estimate the mean and covariance of prior and posterior density distributions of the estimates. More recently, new methods have been developed for nonlinear systems based on sequential Monte Carlo particle filtering and its many variants. Particle filters carry more complete information using a point mass representation of probability distributions rather than just the mean and covariance. Our recent research has evaluated particle filtering methods for nonlinear applications and has contributed new techniques to improve performance and robustness of the methods and also to introduce evidential reasoning for representation and accumulation of knowledge based on the Dempter-Shafer theory of evidence. Resources requested under this proposal will fund graduate student research to develop, implement, and test new methods for evidential reasoning in particle filtering for parameter and state estimation. In our research, we will target applications in vehicular robotics, but the results are expected to be applicable to many diverse applications.

状态和参数估计是许多学科控制和建模的基础。需要参数估计来实现系统模型，该模型与当前状态的估计一起用于基于传感器信息制定智能决策，计算反馈控制信号，调整模型或控制算法和/或参数，并构建操作环境的表示。经典的状态和参数估计方法，如卡尔曼或相关的贝叶斯方法，很大程度上是基于线性高斯假设，在这种假设下，估计估计的先验和后验密度分布的均值和协方差就足够了。最近，基于顺序蒙特卡罗粒子滤波及其变体的非线性系统的新方法得到了发展。粒子滤波器使用概率分布的点质量表示来携带更完整的信息，而不仅仅是均值和协方差。我们最近的研究评估了非线性应用的粒子滤波方法，并贡献了新技术来提高方法的性能和鲁棒性，并引入了基于Dempter-Shafer证据理论的证据推理来表示和积累知识。本提案要求的资源将资助研究生研究，以开发、实施和测试用于参数和状态估计的粒子滤波证据推理的新方法。在我们的研究中，我们将针对车辆机器人的应用，但结果有望适用于许多不同的应用。