TDOA/FDOA based target tracking with imperfect position and velocity data of distributed moving sensors

Han, Seul Ki; Sang, Won; Park, Jin Bae

doi:10.1007/s12555-015-0419-y

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…To estimate the location and tracking of the RF emitter, Kalman filter (KF) and its expansion based on conventional geolocation measurements has been proposed in numerous studies [28][29][30]. In [31], the Extended Kalman filter (EKF) based on TDOA/FDOA was proposed to estimate target position and tracking.…”

Section: Nomentioning

confidence: 99%

Quasi-Real RFI Source Generation Using Orolia Skydel LEO Satellite Simulator for Accurate Geolocation and Tracking: Modeling and Experimental Analysis

et al. 2022

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Accurate geolocation and tracking of Radio-Frequency Interference (RFI) sources, which affect wireless and satellite systems such as Global Navigation Satellite Systems (GNSS) and Satellite Communication (SatCom) systems, are considered to be a significant issue. Several studies connected to civil and military operations on this issue have been investigated recently. The literature review has surveyed many algorithm simulations for optimizing geolocation and target-tracking estimation. Although most of these algorithms have their own advantages, they have weaknesses, such as accuracy, mathematical complexity, difficulties in implementation, and validation in the real environment, etc. This study has been concerned with investigating the accuracy of geolocation and tracking under high speed and powerful rotation using extracted data from the Orolia Skydel simulator, which simulates the space environment involving Low Earth Orbit (LEO) satellites as sensors and Unmanned Aerial Vehicles (UAV) as RFI emitters. Various scenarios modeled using the Orolia Simulator for quasi-real dynamic trajectories of LEO satellites have been created. The assumed approaches have been verified by Cramer–Rao Lower Bound (CRLB) and Posterior CRLB (PCRLB) to determine the increase in Root Mean Square Error (RMSE) value. The simulation scenarios have been performed using the Monte Carlo iteration. Eventually, the overall achieved results of the considered approaches using data acquired from the Orolia Simulator were presented and compared with theoretical simulation.

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Section: Nomentioning

confidence: 99%

Quasi-Real RFI Source Generation Using Orolia Skydel LEO Satellite Simulator for Accurate Geolocation and Tracking: Modeling and Experimental Analysis

et al. 2022

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“…On the other hand, [2] introduces a Markov Jump Particle filter (MJPF), which consists of continuous and discrete inference levels that are dynamically estimated by a collection of Kalman filters (KF) assembled into a Particle filter (PF) algorithm.MJPF enables the prediction of future states in continuous and discrete levels. Most of the related works [9], [13] use position-related information to make inferences.However, the information related to the control of an autonomous entity plays a significant role in the prediction of future states and actions of the entity. By taking this into account, it is imperative to consider variables related to control 978-1-5386-4980-0/19/$31.00 ©2019 IEEE arXiv:2010.14900v1 [cs.LG] 28 Oct 2020 to make the system more efficient.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Bayesian Approach for decision-making in Ego-Things

Kanapram

Campo

Baydoun

et al. 2019

2019 IEEE 5th World Forum on Internet of Things (WF-IoT)

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This paper presents a novel approach to detect abnormalities in dynamic systems based on multisensory data and feature selection. The proposed method produces multiple inference models by considering several features of the observed data. This work facilitates the obtainment of the most precise features for predicting future instances and detecting abnormalities. Growing neural gas (GNG) is employed for clustering multisensory data into a set of nodes that provide a semantic interpretation of data and define local linear models for prediction purposes. Our method uses a Markov Jump particle filter (MJPF) for state estimation and abnormality detection. The proposed method can be used for selecting the optimal set features to be shared in networking operations such that state prediction, decision-making, and abnormality detection processes are favored. This work is evaluated by using a real dataset consisting of a moving vehicle performing some tasks in a controlled environment.

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Gaussian Sum FIR Filtering for 2D Target Tracking

Pak

2019

Int. J. Control Autom. Syst.

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TDOA/FDOA based target tracking with imperfect position and velocity data of distributed moving sensors

Cited by 4 publications

References 14 publications

Quasi-Real RFI Source Generation Using Orolia Skydel LEO Satellite Simulator for Accurate Geolocation and Tracking: Modeling and Experimental Analysis

Quasi-Real RFI Source Generation Using Orolia Skydel LEO Satellite Simulator for Accurate Geolocation and Tracking: Modeling and Experimental Analysis

Dynamic Bayesian Approach for decision-making in Ego-Things

Gaussian Sum FIR Filtering for 2D Target Tracking

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