TDOA/FDOA Mobile Target Localization and Tracking with Adaptive Extended Kalman Filter

Chen, Yee Ming; Tsai, Chi-Li; Fang, Ren-Wei

doi:10.1109/iccairo.2017.47

Cited by 10 publications

(12 citation statements)

References 11 publications

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“…f are the variances of TDOA, and FDOA measurements noise respectively, and ⊗ represents the Kronecker product [20].…”

Section: ) Rf Emitter Tracking Estimationmentioning

confidence: 99%

“…For estimating the target location, TDOA measurements may not be an accurate one especially if the number of sensors is four or less than four [19]. To avoid this weakness and for estimating the position and the velocity of the emitter [20], we can combine two or more conventional geolocation measurements. In addition, to rectify the nonlinear problems, this filter has been developed.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, to rectify the nonlinear problems, this filter has been developed. For example, the Extended Kalman Filter (EKF) and Adaptive Extended Kalman Filter (AEKF) are used for approximation estimation under nonlinearity state [20], [21]. Even though the AEKF can reduce nonlinear problems, it has challenges in higher non-linearity.…”

Section: Introductionmentioning

confidence: 99%

“…Even though the AEKF can reduce nonlinear problems, it has challenges in higher non-linearity. Increasing the emitter rotation speed may create an increase in an error on the emitter estimation [20]. In [22], a new adaptive robust Unscented Kalman Filter (UKF) based on Singular Value Decomposition (SVD) with QR decomposition for improving the accuracy of target tracking has been proposed; the authors used a classical measurement for Target Tracking Range and Bearing; as well as they, applied a simple target Tracking state model (steady-state-transition matrix is stationary and fixed).…”

Section: Introductionmentioning

confidence: 99%

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Gauss Hermite H _∞ Filter for UAV Tracking Using LEO Satellites TDOA/FDOA Measurement—Part I

et al. 2020

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The precision geolocation and target tracking problem has been addressed in this paper using High-Degree Non-linear Filtering based on hybrid Time Difference of Arrival (TDOA), Frequency Difference of Arrival (FDOA) measurements using Low Earth Orbit (LEO) satellite with slant range. In order to update the noise covariance and estimation process at each measurement, the Gauss Hermite H ∞ Filter based on hybrid TDOA/FDOA geolocation measurements are proposed in this work. Numerous scenarios with the different rotation speed of Radio Frequency (RF) emitter has been considered. Multi LEO satellites have used to estimate and track the location of the unknown Unmanned Aerial Vehicle (UAV) under uncertainties of measurements. The uncertainties of measurements have been considered because the position and velocity of sensors are not fixed, which may affect the emitter location estimation measurements. The Cramer-Rao Lower Bound (CRLB) is used as a metric for measuring and analyzing the performance of the H ∞ /GHKF 3 rd degree and H ∞ /GHKF 5 th degree algorithm, as well as compare it with state-of-the-art algorithms. The simulation results of the proposed algorithm indicate that the significant improvement in performance for example, 10% based on TDOA, 40% for FDOA, and 50% on TDOA/FDOA have been achieved.

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“…f are the variances of TDOA, and FDOA measurements noise respectively, and ⊗ represents the Kronecker product [20].…”

Section: ) Rf Emitter Tracking Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Gauss Hermite H _∞ Filter for UAV Tracking Using LEO Satellites TDOA/FDOA Measurement—Part I

et al. 2020

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“…TDOA involves a minimum of three sensors at different places apart from each other. The position of the target can be identified by the signal intercepted at different sensors and the precision of the location estimation can be improved by increasing the number of sensors [14]. Whereas the passive localization using TDOA of emitting sources can only be performed, if the sources are emitting radio frequency signals.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Access Control Model With Dynamic COI for Secure Localization of Satellite and IoT-Based Vehicles

et al. 2020

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Secure localization of vehicles is gaining the attention of researchers from both academia and industry especially due to the emergence of internet of things (IoT). The modern vehicles are usually equipped with circuitries that gives connectivity with other vehicles and with cellular networks such as 4G/Fifth generation cellar network (5G). The challenge of secure localization and positioning is magnified further with the invention of technologies such as autonomous or driverless vehicles based on IoT, satellite, and 5G. Some satellite and IoT based localization techniques exploit machine learning, semantic segmentation, and access control mechanism. Access control provides access grant and secure information sharing mechanism to authorized users and restricts unauthorized users, which is necessary regarding security and privacy of government or military vehicles. Previously, static conflict of interest (COI) based access control was used for security proposes. However, static COI based access control creates excesses and administrative overload that creates latency in execution, which is the least tolerable factor in modern IoT or 5G control vehicles. Therefore, in this paper, a hybrid access control (HAC) model is proposed that implements the dynamic COI in the HAC model on the level of roles. The proposed model is enhanced by modifying the role-based access control (RBAC) model by inserting new attributes of the RBAC entities. The HAC model deals with COI on the level of roles in an efficient manner as compared to previously proposed models. Moreover, this model features significant improvement in terms of dynamic behavior, decreased administrative load, and security especially for vehicular localization. Furthermore, the mathematical modeling of the proposed model is implemented with an example scenario to validate the concept. INDEX TERMS Access control, hybrid access control, secure vehicle localization, machine learning, neural networks, Internet of Things.

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Uniform Acceleration Motion Target Location and Tracking Based on Time-Frequency Difference

Zhang

Song

et al. 2019

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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TDOA/FDOA Mobile Target Localization and Tracking with Adaptive Extended Kalman Filter

Cited by 10 publications

References 11 publications

Gauss Hermite H _∞ Filter for UAV Tracking Using LEO Satellites TDOA/FDOA Measurement—Part I

Gauss Hermite H _∞ Filter for UAV Tracking Using LEO Satellites TDOA/FDOA Measurement—Part I

A Hybrid Access Control Model With Dynamic COI for Secure Localization of Satellite and IoT-Based Vehicles

Uniform Acceleration Motion Target Location and Tracking Based on Time-Frequency Difference

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TDOA/FDOA Mobile Target Localization and Tracking with Adaptive Extended Kalman Filter

Cited by 10 publications

References 11 publications

Gauss Hermite H ∞ Filter for UAV Tracking Using LEO Satellites TDOA/FDOA Measurement—Part I

Gauss Hermite H ∞ Filter for UAV Tracking Using LEO Satellites TDOA/FDOA Measurement—Part I

A Hybrid Access Control Model With Dynamic COI for Secure Localization of Satellite and IoT-Based Vehicles

Uniform Acceleration Motion Target Location and Tracking Based on Time-Frequency Difference

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Product

Resources

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Gauss Hermite H _∞ Filter for UAV Tracking Using LEO Satellites TDOA/FDOA Measurement—Part I

Gauss Hermite H _∞ Filter for UAV Tracking Using LEO Satellites TDOA/FDOA Measurement—Part I