Extended Kalman Filtering and Pathloss modeling for Shadow Power Parameter Estimation in Mobile Wireless Communications

Pappas, George P.; Zohdy, Mohamed

doi:10.21307/ijssis-2017-687

Cited by 7 publications

(6 citation statements)

References 25 publications

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“…The extended Kalman filter is based on a recursive approach, and it is used in this paper to estimate the jammer position. It is suitable for nonlinear state estimation, and it consists of two main processes: the prediction and update states [11]. The state transition model can be expressed as:…”

Section: Methodsmentioning

confidence: 99%

Tracking a Jammer in Wireless Sensor Networks and Selecting Boundary Nodes by Estimating Signal-to-Noise Ratios and Using an Extended Kalman Filter

Aldosari

Zohdy

2018

JSAN

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This work investigates boundary node selection when tracking a jammer. A technique to choose nodes to track jammers by estimating signal-to-noise Ratio (SNR), jammer-to-noise ratio (JNR), and jammer received signal strength (JRSS) are introduced in this paper. We proposed a boundary node selection threshold (BNST) algorithm. Every node can become a boundary node by comparing the SNR threshold, the average SNR estimated at the boundary node, and the received BNST value. The maximum sensing range, transmission range, and JRSS are the main parts of this algorithm. The algorithm is divided into three steps. In the first step, the maximum distance between two jammed nodes is found. Next, the maximum distance between the jammed node and its unjammed neighbors is computed. Finally, maximum BNST value is estimated. The extended Kalman filter (EKF) is utilized in this work to track the jammer and estimate its position in a different time step using selected boundary nodes. The experiment validates the benefits of selecting a boundary when tracking a jammer.

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Section: Methodsmentioning

confidence: 99%

Tracking a Jammer in Wireless Sensor Networks and Selecting Boundary Nodes by Estimating Signal-to-Noise Ratios and Using an Extended Kalman Filter

Aldosari

Zohdy

2018

JSAN

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“…The Extended Kalman Filtering is an extension of Linear Kalman Filter (LKF), which is capable to take into account the nonlinearity of the system model. More specifically, the EKF employs the first-order linearization of the nonlinear system in a recursive fashion to find the estimates current mean and the covariance of the state vector [ 30 ].…”

Section: Proposed Distributed Extended Kalman Filter Based 3d Jammmentioning

confidence: 99%

Distributed Extended Kalman Filtering Based Techniques for 3-D UAV Jamming Localization

Aldosari

Moinuddin

Aljohani

et al. 2020

Sensors

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Wireless networks are vulnerable to jamming attacks. Jamming in wireless communication becomes a major research problem due to ease in Unmanned Aerial Vehicle (UAV) launching and blocking of communication channels. Jamming is a subset of Denial of Service Attack (DoS) and an intentional interference where the malicious node disrupts the wireless communication by increasing the noise at the receiver node through transmission interference signal towards the target channel. In this work, the considered jammer is a UAV hovering around the target area to block the communication channel between two transceivers. We proposed a three-dimensional (3-D) UAV jamming localization scheme to track and detect the jammer position at each time step by employing a single boundary node observer. For this purpose, we developed two distributed Extended Kalman Filter (EKF) based schemes: (1) the Distributed EKF (DEKF) scheme using the information of the received power from the jammer at a single nearby boundary node only and (2) Distance Ratio aided Distributed EKF (DEKF-DR) based scheme utilizing an edge node in addition to a single boundary node. Extensive simulations are conducted in order to evaluate the performance of the proposed distributed algorithms for a 3-D trajectory and compared with that of the conventional Centralized EKF (EKF-Centr) based method (which is also modified for the 3-D scenario). The results show the clear supremacy of the proposed distributed algorithms with much lesser complexity in contrast to the conventional EKF-Centr technique.

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“…The extended Kalman filter adapts the standard model to nonlinear systems via online Taylor expansion. According to [146], this improves shadow/fading estimation.…”

Section: A Time Series Predictive Modelingmentioning

confidence: 99%

A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

Bui

Cesana

Hosseini³

et al. 2017

IEEE Commun. Surv. Tutorials

220

137

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A growing trend for information technology is to not just react to changes, but anticipate them as much as possible. This paradigm made modern solutions, such as recommendation systems, a ubiquitous presence in today's digital transactions. Anticipatory networking extends the idea to communication technologies by studying patterns and periodicity in human behavior and network dynamics to optimize network performance. This survey collects and analyzes recent papers leveraging context information to forecast the evolution of network conditions and, in turn, to improve network performance. In particular, we identify the main prediction and optimization tools adopted in this body of work and link them with objectives and constraints of the typical applications and scenarios. Finally, we consider open challenges and research directions to make anticipatory networking part of next generation networks.

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Extended Kalman Filtering and Pathloss modeling for Shadow Power Parameter Estimation in Mobile Wireless Communications

Cited by 7 publications

References 25 publications

Tracking a Jammer in Wireless Sensor Networks and Selecting Boundary Nodes by Estimating Signal-to-Noise Ratios and Using an Extended Kalman Filter

Tracking a Jammer in Wireless Sensor Networks and Selecting Boundary Nodes by Estimating Signal-to-Noise Ratios and Using an Extended Kalman Filter

Distributed Extended Kalman Filtering Based Techniques for 3-D UAV Jamming Localization

A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

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