Search Results for author:
Found 15 papers, 3 papers with code
On the Impact of Sampling on Deep Sequential State Estimation
In this paper, importance sampling is applied to the DKF framework for learning deep Markov models, resulting in the IW-DKF, which shows an improvement in terms of log-likelihood estimates and KL divergence between the variational distribution and the transition model.
Robust Interference Mitigation techniques for Direct Position Estimation
Global Navigation Satellite System (GNSS) is pervasive in navigation and positioning applications, where precise position and time referencing estimations are required.
Bayesian and Neural Inference on LSTM-based Object Recognition from Tactile and Kinesthetic Information
Recent advances in the field of intelligent robotic manipulation pursue providing robotic hands with touch sensitivity.
Jammer classification with Federated Learning
Jamming signals can jeopardize the operation of GNSS receivers until denying its operation.
Dynamical Hyperspectral Unmixing with Variational Recurrent Neural Networks
First, a stochastic model is proposed to represent both the dynamical evolution of the endmembers and their abundances, as well as the mixing process.
Online Fusion of Multi-resolution Multispectral Images with Weakly Supervised Temporal Dynamics
To evaluate the proposed methodology we consider a water mapping task where real data acquired by the Landsat and MODIS instruments are fused generating high spatial-temporal resolution image estimates.
Recursive classification of satellite imaging time-series: An application to water mapping, land cover classification and deforestation detection
Our method also improves the performance of deep learning-based classifiers without the need for additional training data.
Jamming Source Localization Using Augmented Physics-Based Model
Monitoring interferences to satellite-based navigation systems is of paramount importance in order to reliably operate critical infrastructures, navigation systems, and a variety of applications relying on satellite-based positioning.
Bayesian data fusion with shared priors
When data is only available in a distributed fashion or when different sensors are used to infer a quantity of interest, data fusion becomes essential.
Neural Network-based OFDM Receiver for Resource Constrained IoT Devices
Here, ML blocks replace the individual processing blocks of an OFDM receiver, and we specifically describe this swapping for the legacy channel estimation, symbol demapping, and decoding blocks with Neural Networks (NNs).
Online multi-resolution fusion of space-borne multispectral images
Satellite imaging has a central role in monitoring, detecting and estimating the intensity of key natural phenomena.
Hybrid Neural Network Augmented Physics-based Models for Nonlinear Filtering
In this paper we present a hybrid neural network augmented physics-based modeling (APBM) framework for Bayesian nonlinear latent space estimation.
Personalized Federated Learning over non-IID Data for Indoor Localization
Localization and tracking of objects using data-driven methods is a popular topic due to the complexity in characterizing the physics of wireless channel propagation models.
Model-Based Deep Autoencoder Networks for Nonlinear Hyperspectral Unmixing
Autoencoder (AEC) networks have recently emerged as a promising approach to perform unsupervised hyperspectral unmixing (HU) by associating the latent representations with the abundances, the decoder with the mixing model and the encoder with its inverse.
Kalman Filtering and Expectation Maximization for Multitemporal Spectral Unmixing
The recent evolution of hyperspectral imaging technology and the proliferation of new emerging applications presses for the processing of multiple temporal hyperspectral images.
