Zobrazeno 1 - 10
of 219
pro vyhledávání: '"Ling, Keck Voon"'
We present a neural network for mitigating biased errors in pseudoranges to improve localization performance with data collected from mobile phones. A satellite-wise Multilayer Perceptron (MLP) is designed to regress the pseudorange bias correction f
Externí odkaz:
http://arxiv.org/abs/2309.12204
Autor:
Weng, Xu, Ling, Keck Voon
Android raw Global Navigation Satellite System (GNSS) measurements are expected to bring smartphones power to take on demanding localization tasks that are traditionally performed by specialized GNSS receivers. The hardware constraints, however, make
Externí odkaz:
http://arxiv.org/abs/2309.08936
In this work, we aim to address the challenging task of open set recognition (OSR). Many recent OSR methods rely on auto-encoders to extract class-specific features by a reconstruction strategy, requiring the network to restore the input image on pix
Externí odkaz:
http://arxiv.org/abs/2108.02373
Publikováno v:
In Advances in Space Research 1 May 2024 73(9):4571-4583
Deep neural networks have made breakthroughs in a wide range of visual understanding tasks. A typical challenge that hinders their real-world applications is that unknown samples may be fed into the system during the testing phase, but traditional de
Externí odkaz:
http://arxiv.org/abs/2008.05129
Deep neural networks have achieved state-of-the-art performance in a wide range of recognition/classification tasks. However, when applying deep learning to real-world applications, there are still multiple challenges. A typical challenge is that unk
Externí odkaz:
http://arxiv.org/abs/2003.08823
Publikováno v:
In Measurement 30 June 2023 215
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
In Advances in Space Research 15 May 2022 69(10):3655-3667
In this paper, instead of the usual Gaussian noise assumption, $t$-distribution noise is assumed. A Maximum Likelihood Estimator using the most recent N measurements is proposed for the Auto-Regressive-Moving-Average with eXogenous input (ARMAX) proc
Externí odkaz:
http://arxiv.org/abs/1706.06509