3,037 research outputs found
Recurrent Neural Networks For Accurate RSSI Indoor Localization
This paper proposes recurrent neuron networks (RNNs) for a fingerprinting
indoor localization using WiFi. Instead of locating user's position one at a
time as in the cases of conventional algorithms, our RNN solution aims at
trajectory positioning and takes into account the relation among the received
signal strength indicator (RSSI) measurements in a trajectory. Furthermore, a
weighted average filter is proposed for both input RSSI data and sequential
output locations to enhance the accuracy among the temporal fluctuations of
RSSI. The results using different types of RNN including vanilla RNN, long
short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM
(BiLSTM) are presented. On-site experiments demonstrate that the proposed
structure achieves an average localization error of m with of the
errors under m, which outperforms the conventional KNN algorithms and
probabilistic algorithms by approximately under the same test
environment.Comment: Received signal strength indicator (RSSI), WiFi indoor localization,
recurrent neuron network (RNN), long shortterm memory (LSTM),
fingerprint-based localizatio
Non-global parameter estimation using local ensemble Kalman filtering
We study parameter estimation for non-global parameters in a low-dimensional
chaotic model using the local ensemble transform Kalman filter (LETKF). By
modifying existing techniques for using observational data to estimate global
parameters, we present a methodology whereby spatially-varying parameters can
be estimated using observations only within a localized region of space. Taking
a low-dimensional nonlinear chaotic conceptual model for atmospheric dynamics
as our numerical testbed, we show that this parameter estimation methodology
accurately estimates parameters which vary in both space and time, as well as
parameters representing physics absent from the model
Non-Sequential Ensemble Kalman Filtering using Distributed Arrays
This work introduces a new, distributed implementation of the Ensemble Kalman
Filter (EnKF) that allows for non-sequential assimilation of large datasets in
high-dimensional problems. The traditional EnKF algorithm is computationally
intensive and exhibits difficulties in applications requiring interaction with
the background covariance matrix, prompting the use of methods like sequential
assimilation which can introduce unwanted consequences, such as dependency on
observation ordering. Our implementation leverages recent advancements in
distributed computing to enable the construction and use of the full model
error covariance matrix in distributed memory, allowing for single-batch
assimilation of all observations and eliminating order dependencies.
Comparative performance assessments, involving both synthetic and real-world
paleoclimatic reconstruction applications, indicate that the new,
non-sequential implementation outperforms the traditional, sequential one
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