Evaluation of Localization by Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter-Based Techniques

Ullah, Inam; Su, Xin; Zhu, Jinxiu; Zhang, Xuewu; Choi, Dongmin; Hou, Zhenguo

doi:10.1155/2020/8898672

Cited by 14 publications

(8 citation statements)

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“…e SDN blockchain integration is shown, which addresses all of the critical security apprehensions of IoT from an ultramodern standpoint. e primary ability of that amalgamation is the protection from DoS attacks, impersonating attacks, and routing attacks [29][30][31][32].…”

Section: Background and Existing Literaturementioning

confidence: 99%

An AI-Driven Hybrid Framework for Intrusion Detection in IoT-Enabled E-Health

Wahab

Zhao

Javeed

et al. 2022

Computational Intelligence and Neuroscience

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E-health has grown into a billion-dollar industry in the last decade. Its device’s high throughput makes it an obvious target for cyberattacks, and these environments desperately need protection. In this scientific study, we presented an artificial intelligence (AI)-driven software-defined networking (SDN)-enabled intrusion detection system (IDS) to address increasing cyber threats in the E-health and internet of medical things (IoMT) environments. AI’s success in various fields, including big data and intrusion detection systems, has prompted us to develop a flexible and cost-effective approach to protect such critical environments from cyberattacks. We present a hybrid model consisting of long short-term memory (LSTM) and gated recurrent unit (GRU). The proposed model was thoroughly evaluated using the publicly available CICDDoS2019 dataset and conventional evaluation measures. Furthermore, for proper validation, the proposed framework is compared with relevant classifiers, such as cu-GRU+ DNN and cu-BLSTM. We have further compared the proposed model with existing literature to prove its efficacy. Lastly, 10-fold cross-validation is also used to verify that our results are unbiased. The proposed approach has bypassed the current literature with extraordinary performance ramifications such as 99.01% accuracy, 99.04% precision, 98.80 percent recall, and 99.12% F1-score.

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Section: Background and Existing Literaturementioning

confidence: 99%

An AI-Driven Hybrid Framework for Intrusion Detection in IoT-Enabled E-Health

Wahab

Zhao

Javeed

et al. 2022

Computational Intelligence and Neuroscience

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“…In the visualization of objects, feature extraction needs to extract various visual features to provide a reliable representation [18,33,34,36,37]. In fact, these features represent similarities with the human brain and complex cells [12].…”

Section: Introductionmentioning

confidence: 99%

Efficient Algorithms for E-Healthcare to Solve Multiobject Fuse Detection Problem

Ahmad¹,

Ullah

Khan

et al. 2021

Journal of Healthcare Engineering

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Object detection plays a vital role in the fields of computer vision, machine learning, and artificial intelligence applications (such as FUSE-AI (E-healthcare MRI scan), face detection, people counting, and vehicle detection) to identify good and defective food products. In the field of artificial intelligence, target detection has been at its peak, but when it comes to detecting multiple targets in a single image or video file, there are indeed challenges. This article focuses on the improved K-nearest neighbor (MK-NN) algorithm for electronic medical care to realize intelligent medical services and applications. We introduced modifications to improve the efficiency of MK-NN, and a comparative analysis was performed to determine the best fuse target detection algorithm based on robustness, accuracy, and computational time. The comparative analysis is performed using four algorithms, namely, MK-NN, traditional K-NN, convolutional neural network, and backpropagation. Experimental results show that the improved K-NN algorithm is the best model in terms of robustness, accuracy, and computational time.

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“…Although the proposed approach is efficient for tracking radio-tagged flying insects, it should not be overlooked that most systems are nonlinear dynamic systems with complex disturbances in the natural environment. Therefore, if a particle filter or an unscented Kalman filter that further covers nonlinearity is applied in the developed system for more advanced tracking, the dynamic target can be estimated and tracked more efficiently [41]. Alternatively, an event-driven heterogeneous robot system (e.g., a radiotracking UAV equipped with a thermal infrared camera [42]) is scalable and can be more useful than conventional systems for tracking radio-tagged flying targets [43].…”

Section: E Results Of Field Testsmentioning

confidence: 99%

Investigation of an Autonomous Tracking System for Localization of Radio-Tagged Flying Insects

Son

2022

IEEE Access

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The tracking of flying insects is one of the main challenges in the field of ecosystem and biodiversity protection. In the case of invasive insects such as Asian hornets, extensive considerations must be made in estimating the habitat because of the limitations of available sensors and environmental uncertainty. In this study, we propose an approach for localizing and autonomously tracking radio-tagged flying insects and developing an unmanned aerial vehicle (UAV)-based robotic system. The extended Kalman filter is applied to the received signal strength emitted from a radio telemetry transmitter to estimate the insect's position while reducing the measurement error and noise. The proposed autonomous tracking strategy involves a method in which the UAV rotates around one point to measure the signal strength and control its position in relation to the direction of the strongest signal. We also designed a system architecture that includes a tracking sensor system and a UAV system for radio-tagged flying insects. The effectiveness of the proposed system for estimating and autonomously tracking the target position was evaluated via numerical dynamics simulation. Furthermore, feasibility tests with unmanned ground vehicle replacements for insects and UAVs were performed to validate the proposed approach. Finally, to verify the UAV system, field tests were conducted to track a fixed radio-tagged flying target on a vast flatland. This study is unique because we propose and validate a novel mobile robot-based tracking system to localize radio tags of flying insects. INDEX TERMSRadio-tagged flying insects, unmanned aerial vehicles, radio telemetry localization, autonomous tracking, sensor systems I. INTRODUCTION

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Evaluation of Localization by Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter-Based Techniques

Cited by 14 publications

References 50 publications

An AI-Driven Hybrid Framework for Intrusion Detection in IoT-Enabled E-Health

An AI-Driven Hybrid Framework for Intrusion Detection in IoT-Enabled E-Health

Efficient Algorithms for E-Healthcare to Solve Multiobject Fuse Detection Problem

Investigation of an Autonomous Tracking System for Localization of Radio-Tagged Flying Insects

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