无需物理模型，用神经网络实现复杂动态系统的高效状态估计。
VSE: Variational state estimation of complex model-free process
- 基于变分推断构建循环神经网络，直接输出高斯后验分布。
- 在洛伦兹系统追踪任务中，性能媲美已知模型的粒子滤波器。
- 适合无解析模型、依赖传感器观测的实时状态估计场景。
我们提出一种变分状态估计（VSE）方法，从非线性、含噪声的观测中，以闭式高斯后验形式估计复杂动态过程的状态。该过程为模型无关（model-free），即缺乏描述状态演化规律的物理模型。通过递归神经网络（RNN）实现闭式高斯后验，推理阶段计算高效。学习阶段使用另一个RNN辅助优化，二者基于变分推断相互促进。在2维相机测量模型下对随机洛伦兹系统（经典基准过程）进行追踪验证，结果显示：尽管不依赖系统模型，其性能仍可与已知模型的粒子滤波器及近期数据驱动方法相媲美。
原文摘要 · Abstract (English)
We design a variational state estimation (VSE) method that provides a closed-form Gaussian posterior of an underlying complex dynamical process from (noisy) nonlinear measurements. The complex process is model-free. That is, we do not have a suitable physics-based model characterizing the temporal evolution of the process state. The closed-form Gaussian posterior is provided by a recurrent neural network (RNN). The use of RNN is computationally simple in the inference phase. For learning the RNN, an additional RNN is used in the learning phase. Both RNNs help each other learn better based on variational inference principles. The VSE is demonstrated for a tracking application - state estimation of a stochastic Lorenz system (a benchmark process) using a 2-D camera measurement model. The VSE is shown to be competitive against a particle filter that knows the Lorenz system model and a recently proposed data-driven state estimation method that does not know the Lorenz system model.
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