Synchronizing a digital GPS receiver

Goiser, A.M.J.; Berger, G.

doi:10.1109/melcon.1996.551387

Cited by 4 publications

(4 citation statements)

References 3 publications

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“…Then, the time scale is estimated from the carrier frequency, and the time shift from the base band signal, e.g., using correlation analysis (where the time scale can be neglected). This technique is exemplified in [44].…”

Section: Detected Known Signalsmentioning

confidence: 99%

On Pose Estimation in Room-Scaled Environments

Nyqvist¹

2016

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Systra mi, du är bäst! Ikväll ska vi tokdansa med Ronny och Ragge på bordet tills byxorna spricker så att mamma får dåndimpen och pappa ramlar ur soffan. AbstractPose (position and orientation) tracking in room-scaled environments is an enabling technique for many applications. Today, virtual reality (vr) and augmented reality (ar) are two examples of such applications, receiving high interest both from the public and the research community. Accurate pose tracking of the vr or ar equipment, often a camera or a headset, or of different body parts is crucial to trick the human brain and make the virtual experience realistic. Pose tracking in room-scaled environments is also needed for reference tracking and metrology. This thesis focuses on an application to metrology. In this application, photometric models of a photo studio are needed to perform realistic scene reconstruction and image synthesis. Pose tracking of a dedicated sensor enables creation of these photometric models. The demands on the tracking system used in this application is high. It must be able to provide sub-centimeter and sub-degree accuracy and at same time be easy to move and install in new photo studios.The focus of this thesis is to investigate and develop methods for a pose tracking system that satisfies the requirements of the intended metrology application. The Bayesian filtering framework is suggested because of its firm theoretical foundation in informatics and because it enables straightforward fusion of measurements from several sensors. Sensor fusion is in this thesis seen as a way to exploit complementary characteristics of different sensors to increase tracking accuracy and robustness. Four different types of measurements are considered; inertialmeasurements, images from a camera, range (time-of-flight) measurements from ultra wide band (uwb) radio signals, and range and velocity measurements from echoes of transmitted acoustic signals.A simulation study and a study of the Cramér-Rao lower filtering bound (crlb) show that an inertial-camera system has the potential to reach the required tracking accuracy. It is however assumed that known fiducial markers, that can be detected and recognized in images, are deployed in the environment. The study shows that many markers are required. This makes the solution more of a stationary solution and the mobility requirement is not fulfilled. A simultaneous localization and mapping (slam) solution, where naturally occurring features are used instead of known markers, are suggested solve this problem. Evaluation using real data shows that the provided inertial-camera slam filter suffers from drift but that support from uwb range measurements eliminates this drift. The slam solution is then only dependent on knowing the position of very few stationary uwb transmitters compared to a large number of known fiducial markers. As a last step, to increase the accuracy of the slam filter, it is investigated if and how range measurements can be complemented with velocity measurement obtained as a resu...

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Section: Detected Known Signalsmentioning

confidence: 99%

On Pose Estimation in Room-Scaled Environments

Nyqvist¹

2016

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“…In the pull-in state, a GPS receiver tries to obtain a fine Doppler frequency, carrier phase, and C/N 0 estimates of an incoming GPS signal, which can take about 1.5s (Kelley et al 2002). To improve the pull-in search performance, conventional techniques, such as Tsui (2005) and Goiser & Berger (1996), perform a fast Fourier transform (FFT) to find the maximum frequency component from code wiped-off sample data and then utilize the amplitude of the component to obtain a fine Doppler frequency estimate. Consecutive fine Doppler frequency estimates can be averaged to increase accuracy in the presence of noise, but, in general, the conventional techniques work well for high C/N 0 .…”

Section: Seung-hyun Kong Kyungwoo Yoomentioning

confidence: 99%

GPS Pull-In Search Using Reverse Directional Finite Rate of Innovation (FRI)

Kong¹,

Yoo²

2014

Journal of Positioning, Navigation, and Timing

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When an incoming Global Positioning System (GPS) signal is acquired, pull-in search performs a finer search of the Doppler frequency of the incoming signal so that phase lock loop can be quickly stabilized and the receiver can produce an accurate pseudo-range measurement. However, increasing the accuracy of the Doppler frequency estimation often involves a higher computational cost for weaker GPS signals, which delays the position fix. In this paper, we show that the Doppler frequency detectable by a long coherent auto-correlation can be accurately estimated using a complex-weighted sum of consecutive short coherent auto-correlation outputs with a different Doppler frequency hypothesis, and by exploiting this we propose a noise resistant, low-cost and highly accurate Doppler frequency and phase estimation technique based on a reverse directional application of the finite rate of innovation (FRI) technique. We provide a performance and computational complexity analysis to show the feasibility of the proposed technique and compare the performance to conventional techniques using numerous Monte Carlo simulations.

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“…Several methods are available in the literature, as described in Lindsey andSimon (1973), andSpilker (1977). For our receiver, the DSP performs a fast parallel search in the frequency domain according to Goiser and Berger (1996), and Bao and Tsui (2000). It is important to highlight that, from the analysis of GPS SIS and Galileo SIS (via software simulations, not reported in this note), it is clear that the DSP stage requires a higher computational load in the Galileo case, due to all the possible demodulation schemes.…”

Section: Dsp Sectionmentioning

confidence: 99%