This paper presents some new results on recursive l1- minimization by Kalman filtering. We consider the l1-norm as an explicit constraint, formulated as a nonlinear l1- observation of the state to be estimated. Interpreting a sparse vector to be estimated as a state which is observed from ...
et al.(2023). Concrete and steel bridge structural health monitoring—insight into choices for mach...
Fukumori (2002) advanced a new approach to approximate Kalman filtering (and optimal smoothing, which is discussed later) that is suitable for oceanic and atmospheric data assimilation. The method solves the larger estimation problem by partitioning it into a series of smaller calculations. Errors wit...
The motivation for this book came out of my desire for a gentle introduction to Kalman filtering. I'm a software engineer that spent almost two decades in the avionics field, and so I have always been 'bumping elbows' with the Kalman filter, but never implemented one myself. As I moved...
Experimentally measured SOC from the WLTP experiment on Cell A, compared to the predicted SOC from ECM and ECMh-opt, through Coulomb counting, constant and adaptive Kalman filtering. Full size image Figure 8 The mean SOC prediction error for all models of each cell tested, evaluated against the...
The algorithm combines a traditional feature detector based on Kalman filtering and motion detection, and a lightweight shallow convolutional neural network. This technique allows the automatic selection of specific regions of interest within the image by the generation of bounding boxes for gray colored...
This repository contains our C++ implementation of (intra-camera) online multiple object tracking based on Kalman filtering. By tuning some hyperparameters, it is capable of reducing false nagatives and false positives. This algorithm is useful for efficient tracklet generation in data association. Int...
We have produced an eddy-resolving local ensemble transform Kalman filter (LETKF)-based ocean research analysis (LORA) for the western North Pacific (WNP)
First, the basics of ensemble Kalman filtering are introduced, followed by describing how the EnKF becomes the ERFF. Second, the three reference synthetic transient groundwater flow problems and the scenarios that will be analyzed are described. Third, the results for the different scenarios are ...
"filtering". The pair of prediction and filtering can be iteratively applied to update the time. (B) Illustration of the method with a 9-pixel toy image. In the Kalman filter approach for HS-AFM data, each measurement corresponds to obtain the molecular height at one pixel. For example, ...