无需动力学模型，仅用观测数据就能高精度估计隐藏状态序列。
DNS: Data-driven Nonlinear Smoother for Complex Model-free Process
- 基于循环结构设计，从噪声测量中直接学习隐藏状态后验分布。
- 在洛伦兹系统等随机过程上，优于深度卡尔曼平滑器和iDANSE方法。
- 适用于无动力学先验的复杂系统状态估计，如物理仿真与传感器融合。
我们提出数据驱动非线性平滑器（DNS），用于从噪声性、线性观测序列中估计复杂动态过程的隐藏状态序列。该动态过程为无模型（model-free），即未知其非线性动力学特性，且缺乏状态转移模型（STM）。DNS采用循环架构，可对给定观测序列的隐藏状态序列提供闭式后验分布。该方法以无监督方式训练，训练数据仅包含观测值，不包含真实状态。我们在多个随机动态过程的模拟中验证了DNS，包括基准洛伦兹系统。实验结果表明，DNS显著优于深度卡尔曼平滑器（DKS）和迭代数据驱动非线性状态估计（iDANSE）平滑器。
原文摘要 · Abstract (English)
We propose data-driven nonlinear smoother (DNS) to estimate a hidden state sequence of a complex dynamical process from a noisy, linear measurement sequence. The dynamical process is model-free, that is, we do not have any knowledge of the nonlinear dynamics of the complex process. There is no state-transition model (STM) of the process available. The proposed DNS uses a recurrent architecture that helps to provide a closed-form posterior of the hidden state sequence given the measurement sequence. DNS learns in an unsupervised manner, meaning the training dataset consists of only measurement data and no state data. We demonstrate DNS using simulations for smoothing of several stochastic dynamical processes, including a benchmark Lorenz system. Experimental results show that the DNS is significantly better than a deep Kalman smoother (DKS) and an iterative data-driven nonlinear state estimation (iDANSE) smoother.
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