用神经网络联合学习动态与观测模型，提升状态估计精度。
Coordinate ascent neural Kalman-MLE for state estimation
- 采用坐标上升法优化神经网络参数和噪声协方差矩阵
- 在监督学习下同时建模动态与测量过程，支持非线性系统
- 适合需要高精度状态估计的机器人、自动驾驶等场景
本文提出一种坐标上升算法，通过最大似然估计在监督学习框架下联合学习动态模型与观测模型。假设动态与观测模型均为高斯分布，算法同时学习用于建模动态与观测函数的神经网络参数，以及噪声协方差矩阵。训练完成后，利用所学模型与非线性卡尔曼滤波器进行测试阶段的状态估计。
原文摘要 · Abstract (English)
This paper presents a coordinate ascent algorithm to learn dynamic and measurement models in dynamic state estimation using maximum likelihood estimation in a supervised manner. In particular, the dynamic and measurement models are assumed to be Gaussian and the algorithm learns the neural network parameters that model the dynamic and measurement functions, and also the noise covariance matrices. The trained dynamic and measurement models are then used with a non-linear Kalman filter algorithm to estimate the state during the testing phase.
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