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Special Issue "Artificial Neural Networks for Navigation Sensor Integration Applications"

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (15 December 2018) | Viewed by 23326

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Special Issue Editors

Prof. Dr. Naser El-Sheimy

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Guest Editor

Department of Geomatics Engineering, The University of Calgary, Calgary, AB T2N 1N4, Canada
Interests: intelligent and autonomous systems; navigation & positioning technologies; satellite technologies; multi-sensor systems; wireless positioning; vehicles & transportation systems; driverless cars; technology development; applications
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Kai-Wei Chiang

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Guest Editor

High Definition Maps Research Center, Department of Geomatics, National Cheng Kung University, No.1, Ta-Hsueh Road, Tainan 701, Taiwan
Interests: inertial navigation system; optimal multi-sensor fusion; seamless mapping and navigation applications; artificial intelligence and collaborative mobile mapping technology
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Prof. Dr. Cheng Wang

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Guest Editor

Department of Computer Science, School of Informatics, Xiamen University, 422 Siming Road South, Xiamen 361005, Fujian, China
Interests: 3D vision; LiDAR; mobile mapping; geospatial big data analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is open for submissions of both review and original research articles related to artificial neural networks for navigation sensor integration applications. In particular, papers are welcome concerning utilizing artificial neural networks for developing algorithms for applications ranging from indoor/outdoor navigation, navigation sensor calibration, multi-sensor fusion schemes, pedestrian navigation, vehicular navigation, airborne navigation, space borne navigation, unmanned systems, location-based service, navigation quality control, as well as innovative approaches for developing intelligent navigators for autonomous vehicles. Original contributions that look at integrated sensor base sensor fusion technologies using artificial intelligence with remote sensing platforms for mobile mapping and unknown territory exploration applications (ground vehicle, drone, airborne, and space borne) are also encouraged.

Prof. Dr. Naser El-Sheimy
Prof. Dr. Kai-Wei Chiang
Prof. Dr. Cheng Wang
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

Artificial neural networks
Artificial intelligence
Sensor fusion
Multi-sensor systems
Sensor calibration
Unmanned systems
Pedestrian navigation
Vehicular navigation
Airborne navigation
Indoor/outdoor navigation

Published Papers (4 papers)

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Research

Open AccessArticle

A Human Activity Recognition Algorithm Based on Stacking Denoising Autoencoder and LightGBM

and

Sensors 2019, 19(4), 947; https://doi.org/10.3390/s19040947 - 23 Feb 2019

Cited by 48 | Viewed by 4779

Abstract

Recently, the demand for human activity recognition has become more and more urgent. It is widely used in indoor positioning, medical monitoring, safe driving, etc. Existing activity recognition approaches require either the location information of the sensors or the specific domain knowledge, which are expensive, intrusive, and inconvenient for pervasive implementation. In this paper, a human activity recognition algorithm based on SDAE (Stacking Denoising Autoencoder) and LightGBM (LGB) is proposed. The SDAE is adopted to sanitize the noise in raw sensor data and extract the most effective characteristic expression with unsupervised learning. The LGB reveals the inherent feature dependencies among categories for accurate human activity recognition. Extensive experiments are conducted on four datasets of distinct sensor combinations collected by different devices in three typical application scenarios, which are human moving modes, current static, and dynamic behaviors of users. The experimental results demonstrate that our proposed algorithm achieves an average accuracy of 95.99%, outperforming other comparative algorithms using XGBoost, CNN (Convolutional Neural Network), CNN + Statistical features, or single SDAE. Full article

(This article belongs to the Special Issue Artificial Neural Networks for Navigation Sensor Integration Applications)

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Open AccessArticle

Pedestrian Stride-Length Estimation Based on LSTM and Denoising Autoencoders

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Sensors 2019, 19(4), 840; https://doi.org/10.3390/s19040840 - 18 Feb 2019

Cited by 57 | Viewed by 8068

Abstract

Accurate stride-length estimation is a fundamental component in numerous applications, such as pedestrian dead reckoning, gait analysis, and human activity recognition. The existing stride-length estimation algorithms work relatively well in cases of walking a straight line at normal speed, but their error overgrows in complex scenes. Inaccurate walking-distance estimation leads to huge accumulative positioning errors of pedestrian dead reckoning. This paper proposes TapeLine, an adaptive stride-length estimation algorithm that automatically estimates a pedestrian’s stride-length and walking-distance using the low-cost inertial-sensor embedded in a smartphone. TapeLine consists of a Long Short-Term Memory module and Denoising Autoencoders that aim to sanitize the noise in raw inertial-sensor data. In addition to accelerometer and gyroscope readings during stride interval, extracted higher-level features based on excellent early studies were also fed to proposed network model for stride-length estimation. To train the model and evaluate its performance, we designed a platform to collect inertial-sensor measurements from a smartphone as training data, pedestrian step events, actual stride-length, and cumulative walking-distance from a foot-mounted inertial navigation system module as training labels at the same time. We conducted elaborate experiments to verify the performance of the proposed algorithm and compared it with the state-of-the-art SLE algorithms. The experimental results demonstrated that the proposed algorithm outperformed the existing methods and achieves good estimation accuracy, with a stride-length error rate of 4.63% and a walking-distance error rate of 1.43% using inertial-sensor embedded in smartphone without depending on any additional infrastructure or pre-collected database when a pedestrian is walking in both indoor and outdoor complex environments (stairs, spiral stairs, escalators and elevators) with natural motion patterns (fast walking, normal walking, slow walking, running, jumping). Full article

(This article belongs to the Special Issue Artificial Neural Networks for Navigation Sensor Integration Applications)

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Open AccessArticle

Wireless Fingerprinting Uncertainty Prediction Based on Machine Learning

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Sensors 2019, 19(2), 324; https://doi.org/10.3390/s19020324 - 15 Jan 2019

Cited by 34 | Viewed by 4152

Abstract

Although wireless fingerprinting has been well researched and widely used for indoor localization, its performance is difficult to quantify. Therefore, when wireless fingerprinting solutions are used as location updates in multi-sensor integration, it is challenging to set their weight accurately. To alleviate this issue, this paper focuses on predicting wireless fingerprinting location uncertainty by given received signal strength (RSS) measurements through the use of machine learning (ML). Two ML methods are used, including an artificial neural network (ANN)-based approach and a Gaussian distribution (GD)-based method. The predicted location uncertainty is evaluated and further used to set the measurement noises in the dead-reckoning/wireless fingerprinting integrated localization extended Kalman filter (EKF). Indoor walking test results indicated the possibility of predicting the wireless fingerprinting uncertainty through ANN the effectiveness of setting measurement noises adaptively in the integrated localization EKF. Full article

(This article belongs to the Special Issue Artificial Neural Networks for Navigation Sensor Integration Applications)

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Open AccessFeature PaperArticle

Radar and Visual Odometry Integrated System Aided Navigation for UAVS in GNSS Denied Environment

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Sensors 2018, 18(9), 2776; https://doi.org/10.3390/s18092776 - 23 Aug 2018

Cited by 46 | Viewed by 5620

Abstract

Drones are becoming increasingly significant for vast applications, such as firefighting, and rescue. While flying in challenging environments, reliable Global Navigation Satellite System (GNSS) measurements cannot be guaranteed all the time, and the Inertial Navigation System (INS) navigation solution will deteriorate dramatically. Although different aiding sensors, such as cameras, are proposed to reduce the effect of these drift errors, the positioning accuracy by using these techniques is still affected by some challenges, such as the lack of the observed features, inconsistent matches, illumination, and environmental conditions. This paper presents an integrated navigation system for Unmanned Aerial Vehicles (UAVs) in GNSS denied environments based on a Radar Odometry (RO) and an enhanced Visual Odometry (VO) to handle such challenges since the radar is immune against these issues. The estimated forward velocities of a vehicle from both the RO and the enhanced VO are fused with the Inertial Measurement Unit (IMU), barometer, and magnetometer measurements via an Extended Kalman Filter (EKF) to enhance the navigation accuracy during GNSS signal outages. The RO and VO are integrated into one integrated system to help overcome their limitations, since the RO measurements are affected while flying over non-flat terrain. Therefore, the integration of the VO is important in such scenarios. The experimental results demonstrate the proposed system’s ability to significantly enhance the 3D positioning accuracy during the GNSS signal outage. Full article

(This article belongs to the Special Issue Artificial Neural Networks for Navigation Sensor Integration Applications)

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