Support Vector Machine Assisted GPS Navigation in Limited Satellite Visibility

Jwo, Dah‐Jing; Wu, Jia-Chyi; Ho, Kun Chan

doi:10.32604/cmc.2021.018320

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…The proposed system enables the detection of intrusion behavior in the network. It uses a mechanical method to create fuzzy rules, which are obtained from specific rules using repeating elements [34]. Through the results of the experiments, the authors concluded that the system based on fuzzy logic achieves a higher accuracy to determine whether the records are normal or offensive.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Anomaly Detection Using K-Mean Array and Sequential Minimal Optimization

et al. 2022

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Recently, artificial intelligence (AI) techniques have been used to describe the characteristics of information, as they help in the process of data mining (DM) to analyze data and reveal rules and patterns. In DM, anomaly detection is an important area that helps discover hidden behavior within the data that is most vulnerable to attack. It also helps detect network intrusion. Algorithms such as hybrid K-mean array and sequential minimal optimization (SMO) rating can be used to improve the accuracy of the anomaly detection rate. This paper presents an anomaly detection model based on the machine learning (ML) technique. ML improves the detection rate, reduces the false-positive alarm rate, and is capable of enhancing the accuracy of intrusion classification. This study used a dataset known as network security-knowledge and data discovery (NSL-KDD) lab to evaluate a proposed hybrid ML technology. K-mean cluster and SMO were used for classification. In the study, the performance of the proposed anomaly detection was tested, and results showed that the use of K-mean and SMO enhances the rate of positive detection besides reducing the rate of false alarms and achieving a high accuracy at the same time. Moreover, the proposed algorithm outperformed recent and close work related to using similar variables and the environment by 14.48% and decreased false alarm probability (FAP) by (12%) in addition to giving a higher accuracy by 97.4%. These outcomes are attributed to the common algorithm providing an appropriate number of detectors to be generated with an acceptable accurate detection and a trivial false alarm probability (FAP). The proposed hybrid algorithm could be considered for anomaly detection in future data mining systems, where processing in real-time is highly likely to be reduced dramatically. The justification is that the hybrid algorithm can provide appropriate detectors numbers that can be generated with an acceptable detection accuracy and trivial FAP. Given to the low FAP, it is highly expected to reduce the time of the preprocessing and processing compared with the other algorithms.

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Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Anomaly Detection Using K-Mean Array and Sequential Minimal Optimization

et al. 2022

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“…Specifically, the position error is eliminated according to the position relationship between the fixed GPS unit and the mobile GPS unit. Finally, under this scheme, the positioning accuracy is 1-2 m. In [7], the authors put forward the solution of auxiliary GPS (AGPs), which mainly improves the speed of information acquisition and enhances the signal strength, but its positioning accuracy is slightly lower than DGPS. In [8], the authors put forward a lowcost RTK-GPS positioning solution, which can still have good robustness and positioning accuracy under high-speed road conditions.…”

Section: Related Workmentioning

confidence: 99%

LiDAR-IMU-UWB-Based Collaborative Localization

Zhang

Qin

2022

WEVJ

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This article introduced a positioning system composed of different sensors, such as LiDAR, IMU, and ultra-wideband (UWB), for the positioning method in autonomous driving technology under closed coal mine tunnels. First, we processed the LiDAR data, extracted its feature points and merged the extracted feature point clouds to generate a skewed combined feature point cloud. Then, we used the skew combined feature point clouds for feature matching, performed pre-integration processing on the IMU sensor data, and completed the LiDAR-IMU odometer with the LiDAR. Finally, we added UWB data to IMU pose node as a one-dimensional over-edge constraint. By updating the sliding window, the positioning accuracy was further improved. Moreover, we have conducted experiments to verify the proposed positioning system in a simulated roadway. The experimental results showed that the method proposed in this paper is superior to the single LiDAR method and the single UWB method in terms of positioning accuracy.

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“…GPS-based localization is ideal for outdoor environments and not suitable for indoor environments because of the lack of satellite signals. However, integration of GPS/INS systems could be used to account for satellite outage intervals [14,[16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Kinematic Precise Point Positioning Performance-Based Cost-Effective Robot Localization System

Farah,

Tlija

2023

Applied Sciences

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The use of high-precision positioning systems in modern navigation applications is crucial since location data is one of the most important pieces of information in Industry 4.0, especially for robots operating outdoors. In the modernization process of global navigation satellite system (GNSS) positioning, precise point positioning (PPP) has demonstrated its effectiveness in comparison to traditional differential positioning methods over the past thirty years. However, various challenges hinder the integration of PPP techniques into Internet of Things (IoT) systems for robot localization, with accuracy being a primary concern. This accuracy is impacted by factors such as satellite availability and signal disruptions in outdoor environments, resulting in less precise determination of satellite observations. Effectively addressing various GNSS errors is crucial when collecting PPP observations. The paper investigates the trade-off between kinematic PPP accuracy and cost effectiveness, through the examination of various influencing factors, including the choice of GNSS system (single or mixed), observation type (single or dual frequency), and satellite geometry. This research investigates kinematic PPP accuracy variation on a 10.4 km observed track based on different factors, using the GNSS system (single or mixed), and observation type (single or dual frequency). It can be concluded that mixed (GPS/GLONASS) dual frequency offers a 3D position accuracy of 9 cm, while mixed single frequency offers a 3D position accuracy of 13 cm. In industry, the results enable manufacturers to select suitable robot localization solutions according to the outdoor working environment (number of available satellites), economical constraint (single or dual frequency), and 3D position accuracy.

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Support Vector Machine Assisted GPS Navigation in Limited Satellite Visibility

Cited by 4 publications

References 11 publications

Machine Learning-Based Anomaly Detection Using K-Mean Array and Sequential Minimal Optimization

Machine Learning-Based Anomaly Detection Using K-Mean Array and Sequential Minimal Optimization

LiDAR-IMU-UWB-Based Collaborative Localization

Kinematic Precise Point Positioning Performance-Based Cost-Effective Robot Localization System

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