A geolocation method using ToA and FoA measurements

Engel, Ulrich

doi:10.1109/wpnc.2009.4907807

Cited by 4 publications

(1 citation statement)

References 2 publications

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“…In a two-dimensional (2D) scenario, to achieve better precision for TDOA-based geolocation, at least three sensors are needed for receiving similar signals that broadcast from the emitter [11]. The FOA model depends on the carrier frequency, radio propagation velocity, and Doppler shift between emitter and sensor when both or one of them is moving [12]. The FDOA is a difference in pairwise FOA measurement caused by the Doppler shift of the received signal due to differences between sensors and emitter velocities [13].…”

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%