scholarly journals Variational Inference-Based Positioning with Nondeterministic Measurement Accuracies and Reference Location Errors

2017 ◽  
Vol 16 (10) ◽  
pp. 2955-2969 ◽  
Author(s):  
Bingpeng Zhou ◽  
Qingchun Chen ◽  
Henk Wymeersch ◽  
Pei Xiao ◽  
Lian Zhao
Keyword(s):  
Variational Inference ◽  
Reference Location ◽  
Location Errors

scholarly journals SAR Target Recognition via Meta-Learning and Amortized Variational Inference

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5966
Author(s):  
Ke Wang ◽  
Gong Zhang
Keyword(s):  
Target Recognition ◽  
Probability Distributions ◽  
Automatic Target Recognition ◽  
Variational Inference ◽  
Training Data ◽  
Superior Performance ◽  
Small Data ◽  
Meta Learning ◽  
Radar Automatic Target Recognition ◽  
Global Parameters

The challenge of small data has emerged in synthetic aperture radar automatic target recognition (SAR-ATR) problems. Most SAR-ATR methods are data-driven and require a lot of training data that are expensive to collect. To address this challenge, we propose a recognition model that incorporates meta-learning and amortized variational inference (AVI). Specifically, the model consists of global parameters and task-specific parameters. The global parameters, trained by meta-learning, construct a common feature extractor shared between all recognition tasks. The task-specific parameters, modeled by probability distributions, can adapt to new tasks with a small amount of training data. To reduce the computation and storage cost, the task-specific parameters are inferred by AVI implemented with set-to-set functions. Extensive experiments were conducted on a real SAR dataset to evaluate the effectiveness of the model. The results of the proposed approach compared with those of the latest SAR-ATR methods show the superior performance of our model, especially on recognition tasks with limited data.

Fast copula variational inference

2021 ◽  
pp. 1-16
Author(s):  
Jinjin Chi ◽  
Jihong Ouyang ◽  
Ang Zhang ◽  
Xinhua Wang ◽  
Ximing Li
Keyword(s):  
Variational Inference

Amortized Variational Inference for Road Friction Estimation

2020 ◽  
Author(s):  
Shuangshuang Chen ◽  
Sihao Ding ◽  
L. Srikar Muppirisetty ◽  
Yiannis Karayiannidis ◽  
Marten Bjorkman
Keyword(s):  
Variational Inference ◽  
Road Friction ◽  
Friction Estimation

scholarly journals Sparse Bayesian Nonlinear System Identification Using Variational Inference

2018 ◽  
Vol 63 (12) ◽  
pp. 4172-4187 ◽  
Author(s):  
William R. Jacobs ◽  
Tara Baldacchino ◽  
Tony Dodd ◽  
Sean R. Anderson
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Variational Inference

scholarly journals Mobility-Assisted on-Demand Routing Algorithm for MANETs in the Presence of Location Errors

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Trung Kien Vu ◽  
Sungoh Kwon
Keyword(s):  
Ad Hoc ◽  
Routing Algorithm ◽  
Mobility Prediction ◽  
Location Awareness ◽  
Mobile Nodes ◽  
Location Errors ◽  
On Demand ◽  
Demand Routing ◽  
Hoc Networks ◽  
The Impact

We propose a mobility-assisted on-demand routing algorithm for mobile ad hoc networks in the presence of location errors. Location awareness enables mobile nodes to predict their mobility and enhances routing performance by estimating link duration and selecting reliable routes. However, measured locations intrinsically include errors in measurement. Such errors degrade mobility prediction and have been ignored in previous work. To mitigate the impact of location errors on routing, we propose an on-demand routing algorithm taking into account location errors. To that end, we adopt the Kalman filter to estimate accurate locations and consider route confidence in discovering routes. Via simulations, we compare our algorithm and previous algorithms in various environments. Our proposed mobility prediction is robust to the location errors.

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