Zobrazeno 1 - 10
of 4 815
pro vyhledávání: '"Skog, A."'
The lack of dense random-access memory is one of the main obstacles to the development of digital superconducting computers. It has been suggested that AVRAM cells, based on the storage of a single Abrikosov vortex, the smallest quantized object in s
Externí odkaz:
http://arxiv.org/abs/2408.14912
Basis Function (BF) expansions are a cornerstone of any engineer's toolbox for computational function approximation which shares connections with both neural networks and Gaussian processes. Even though BF expansions are an intuitive and straightforw
Externí odkaz:
http://arxiv.org/abs/2408.07480
Maintaining consistent uncertainty estimates in localization systems is crucial as the perceived uncertainty commonly affects high-level system components, such as control or decision processes. A method for constructing an observability-constrained
Externí odkaz:
http://arxiv.org/abs/2406.02161
Typical iterated filters, such as the iterated extended Kalman filter (IEKF), iterated unscented Kalman filter (IUKF), and iterated posterior linearization filter (IPLF), have been developed to improve the linearization point (or density) of the like
Externí odkaz:
http://arxiv.org/abs/2404.15359
Autonomous driving technology is increasingly being used on public roads and in industrial settings such as mines. While it is essential to detect pedestrians, vehicles, or other obstacles, adverse field conditions negatively affect the performance o
Externí odkaz:
http://arxiv.org/abs/2404.05307
A Magnetic field Aided Inertial Navigation System (MAINS) for indoor navigation is proposed in this paper. MAINS leverages an array of magnetometers to measure spatial variations in the magnetic field, which are then used to estimate the displacement
Externí odkaz:
http://arxiv.org/abs/2312.02599
This paper considers the problem of detecting and tracking objects in a sequence of images. The problem is formulated in a filtering framework, using the output of object-detection algorithms as measurements. An extension to the filtering formulation
Externí odkaz:
http://arxiv.org/abs/2310.08316
This paper considers the problem of sequential fusion of predictions from neural networks (NN) and fusion of predictions from multiple NN. This fusion strategy increases the robustness, i.e., reduces the impact of one incorrect classification and det
Externí odkaz:
http://arxiv.org/abs/2310.08315
This letter investigates relationships between iterated filtering algorithms based on statistical linearization, such as the iterated unscented Kalman filter (IUKF), and filtering algorithms based on quasi-Newton (QN) methods, such as the QN iterated
Externí odkaz:
http://arxiv.org/abs/2309.07636
This letter shows that the following three classes of recursive state estimation filters: standard filters, such as the extended Kalman filter; iterated filters, such as the iterated unscented Kalman filter; and dynamically iterated filters, such as
Externí odkaz:
http://arxiv.org/abs/2309.07631