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Mesh-free adjoint methods for nonlinear filters

机译:非线性滤波器的无网格伴随方法

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We apply a new industrial strength numerical approximation, called the "mesh-free adjoint method," to solve the nonlinear filtering problem. This algorithm exploits the smoothness of the problem, unlike particle filters, and hence we expect that mesh-free adjoints are superior to particle filters for many practical applications. The nonlinear filter problem is equivalent to solving the Fokker-Planck equation in real time. The key idea is to use a good adaptive non-uniform quantization of state space to approximate the solution of the Fokker-Planck equation. In particular, the adjoint method computes the location of the nodes in state space to minimize errors in the final answer. This use of an adjoint is analogous to optimal control algorithms, but it is more interesting. The adjoint method is also analogous to importance sampling in particle filters, but it is better for four reasons: (1) it exploits the smoothness of the problem; (2) it explicitly minimizes the errors in the relevant functional; (3) it explicitly models the dynamics in state space; and (4) it can be used to compute a corrected value for the desired functional using the residuals. We will attempt to make this paper accessible to normal engineers who do not have PDEs for breakfast.
机译:我们应用了一种新的工业强度数值近似方法,称为“无网格伴随方法”,以解决非线性滤波问题。与粒子过滤器不同,该算法利用了问题的平滑性,因此在许多实际应用中,我们期望无网格的连接优于粒子过滤器。非线性滤波器问题等效于实时求解Fokker-Planck方程。关键思想是使用状态空间的良好自适应非均匀量化来近似Fokker-Planck方程的解。特别是,伴随方法计算状态空间中节点的位置,以最大程度地减少最终答案中的错误。伴随的这种使用类似于最佳控制算法,但是更有趣。伴随方法也类似于粒子过滤器中的重要性采样,但是由于以下四个原因而更好:(1)利用问题的平滑性; (2)明确最小化相关功能中的错误; (3)显式地模拟状态空间中的动力学; (4)它可以用于使用残差为所需功能计算校正值。我们将尝试使没有PDE用作早餐的普通工程师可以访问本文。

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