论文标题
非线性，非高斯州估计的Stein粒子过滤器
Stein Particle Filter for Nonlinear, Non-Gaussian State Estimation
论文作者
论文摘要
估计动态系统的潜在状态受传感器噪声和模型不准确性的影响仍然是机器人技术中的一个关键但困难的问题。尽管Kalman过滤器在最小平方的意义上为线性和高斯噪声问题提供了最佳的解决方案，但一般的非线性和非高斯噪声案例明显复杂得多，通常依赖于限于低维状态空间的采样策略。在本文中，我们设计了一种通用推理程序，用于过滤非线性，非高斯动力学系统，该系统利用了更新和预测模型的可不同性，以扩展到更高维空间。我们的方法是Stein粒子滤波器，可以看作是粒子的确定性流，嵌入了从初始状态到理想的后部的再现核Hilbert空间中。颗粒可以共同演变为符合后近似，同时通过排斥力相互交互。我们在将其与顺序的蒙特卡洛溶液进行比较时，评估了模拟和复杂定位任务的方法。
Estimation of a dynamical system's latent state subject to sensor noise and model inaccuracies remains a critical yet difficult problem in robotics. While Kalman filters provide the optimal solution in the least squared sense for linear and Gaussian noise problems, the general nonlinear and non-Gaussian noise case is significantly more complicated, typically relying on sampling strategies that are limited to low-dimensional state spaces. In this paper we devise a general inference procedure for filtering of nonlinear, non-Gaussian dynamical systems that exploits the differentiability of both the update and prediction models to scale to higher dimensional spaces. Our method, Stein particle filter, can be seen as a deterministic flow of particles, embedded in a reproducing kernel Hilbert space, from an initial state to the desirable posterior. The particles evolve jointly to conform to a posterior approximation while interacting with each other through a repulsive force. We evaluate the method in simulation and in complex localization tasks while comparing it to sequential Monte Carlo solutions.
