This paper introduces the factored conditional filter, a new filtering
algorithm for simultaneously tracking states and estimating parameters in
high-dimensional state spaces. The conditional nature of the algorithm is used
to estimate parameters and the factored nature is used to decompose the state
space into low-dimensional subspaces in such a way that filtering on these
subspaces gives distributions whose product is a good approximation to the
distribution on the entire state space. The conditions for successful
application of the algorithm are that observations be available at the subspace
level and that the transition model can be factored into local transition
models that are approximately confined to the subspaces; these conditions are
widely satisfied in computer science, engineering, and geophysical filtering
applications. We give experimental results on tracking epidemics and estimating
parameters in large contact networks that show the effectiveness of our
approach.

本文介绍了分解条件过滤器的新型滤波算法，用于同时跟踪状态和估计高维状态空间中的参数，是一种用于计算机科学、工程和地球物理过滤应用的有效工具。

高维空间中状态跟踪和参数估计的因子条件滤波

Factored Conditional Filtering: Tracking States and Estimating Parameters in High-Dimensional Spaces

The Kalman filter is a fundamental filtering algorithm that fuses noisy
sensory data, a previous state estimate, and a dynamics model to produce a
principled estimate of the current state. It assumes, and is optimal for,
linear models and white Gaussian noise. Due to its relative simplicity and
general effectiveness, the Kalman filter is widely used in engineering
applications. Since many sensory problems the brain faces are, at their core,
filtering problems, it is possible that the brain possesses neural circuitry
that implements equivalent computations to the Kalman filter. The standard
approach to Kalman filtering requires complex matrix computations that are
unlikely to be directly implementable in neural circuits. In this paper, we
show that a gradient-descent approximation to the Kalman filter requires only
local computations with variance weighted prediction errors. Moreover, we show
that it is possible under the same scheme to adaptively learn the dynamics
model with a learning rule that corresponds directly to Hebbian plasticity. We
demonstrate the performance of our method on a simple Kalman filtering task,
and propose a neural implementation of the required equations.

本文介绍了一种基于梯度下降逼近的 Kalman 滤波方法，其仅需要进行加权预测误差的局部计算，同时还提出了一种适应性学习规则。作者在一个简单的 Kalman 滤波任务中展示了该方法的性能，并提出了一种神经实现方法。

神经卡尔曼滤波

Neural Kalman Filtering

Autonomous driving systems are broadly used equipment in the industries and
in our daily lives, they assist in production, but are majorly used for
exploration in dangerous or unfamiliar locations. Thus, for a successful
exploration, navigation plays a significant role. Road detection is an
essential factor that assists autonomous robots achieved perfect navigation.
Various techniques using camera sensors have been proposed by numerous scholars
with inspiring results, but their techniques are still vulnerable to these
environmental noises: rain, snow, light intensity and shadow. In addressing
these problems, this paper proposed to enhance the road detection system with
filtering algorithm to overcome these limitations. Normalized Differences Index
(NDI) and morphological operation are the filtering algorithms used to address
the effect of shadow and guidance and re-guidance image filtering algorithms
are used to address the effect of rain and/or snow, while dark channel image
and specular-to-diffuse are the filters used to address light intensity
effects. The experimental performance of the road detection system with
filtering algorithms was tested qualitatively and quantitatively using the
following evaluation schemes: False Negative Rate (FNR) and False Positive Rate
(FPR). Comparison results of the road detection system with and without
filtering algorithm shows the filtering algorithm's capability to suppress the
effect of environmental noises because better road/non-road classification is
achieved by the road detection system. with filtering algorithm. This
achievement has further improved path planning/region classification for
autonomous driving system

本文提出使用滤波算法增强道路检测系统以解决环境噪声对自主驾驶系统路径规划 / 区域分类的影响，使用 False Negative Rate (FNR) 和 False Positive Rate (FPR) 进行检测并与未使用滤波算法进行比较。