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Nonlinear GPS Code Tracking Using a ...

Fay, Gary Lindsay, II.

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Nonlinear GPS Code Tracking Using a Particle Filter Vector Delay Lock Loop for Interference Mitigation.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Nonlinear GPS Code Tracking Using a Particle Filter Vector Delay Lock Loop for Interference Mitigation./
Author:	Fay, Gary Lindsay, II.
Description:	107 p.
Notes:	Source: Dissertation Abstracts International, Volume: 72-05, Section: B, page: .
Contained By:	Dissertation Abstracts International72-05B.
Subject:	Engineering, Computer. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3446858
ISBN:	9781124504384

Nonlinear GPS Code Tracking Using a Particle Filter Vector Delay Lock Loop for Interference Mitigation.
Fay, Gary Lindsay, II.

Nonlinear GPS Code Tracking Using a Particle Filter Vector Delay Lock Loop for Interference Mitigation. - 107 p.

Source: Dissertation Abstracts International, Volume: 72-05, Section: B, page: .

Thesis (Ph.D.)--University of California, Los Angeles, 2010.

This dissertation describes the application of nonlinear estimation algorithms to the Global Positioning System (GPS) code tracking problem in noisy environments when signal-to-noise ratio (SNR) is low. This problem is currently of great interest due to the development of real-time software defined receivers (SDRs). In order to improve GPS signal tracking performance and position accuracy, maximum power must he recovered from the signal correlators. This is accomplished by optimal alignment of the receiver generated code replicas with the true incoming signal. The optimal recursive Bayesian phase estimator is formulated to solve this problem for a single line-of-sight tracking loop. In low SNR environments, the nonlinear code phase error probability densities become non-Gaussian, asymmetric, and often multi-modal. A closed-form solution to the integrals in the Bayesian estimator is intractable and numerical approximations to the Bayesian estimator are required. In particular, a grid-based Bayesian estimator and a Sampling Importance Resampling (SIR) particle filter are applied to the line-of-sight tracking problem. Finally, a Particle Filter Vector Delay Lock Loop (PF-VDLL) architecture is described that optimally combines measurements from all satellites simultaneously to compute the three-dimensional navigation solution. The results of the PF-VDLL are shown to improve perfromance over an Extended Kalman Filter (EKF) implementation. A novel approach for integrating measurements from an Inertial Measurement Unit (IMU) with the PF-VDLL to improve tracking performance is shown.

ISBN: 9781124504384Subjects--Topical Terms:

1669061
Engineering, Computer.

Nonlinear GPS Code Tracking Using a Particle Filter Vector Delay Lock Loop for Interference Mitigation.
LDR:02543nam 2200289 4500 001 1403332
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020 $a 9781124504384
035 $a (UMI)AAI3446858
035 $a AAI3446858
040 $a UMI $c UMI
100 1 $a Fay, Gary Lindsay, II. $3 1682591
245 1 0 $a Nonlinear GPS Code Tracking Using a Particle Filter Vector Delay Lock Loop for Interference Mitigation.
300 $a 107 p.
500 $a Source: Dissertation Abstracts International, Volume: 72-05, Section: B, page: .
500 $a Adviser: Jason Speyer.
502 $a Thesis (Ph.D.)--University of California, Los Angeles, 2010.
520 $a This dissertation describes the application of nonlinear estimation algorithms to the Global Positioning System (GPS) code tracking problem in noisy environments when signal-to-noise ratio (SNR) is low. This problem is currently of great interest due to the development of real-time software defined receivers (SDRs). In order to improve GPS signal tracking performance and position accuracy, maximum power must he recovered from the signal correlators. This is accomplished by optimal alignment of the receiver generated code replicas with the true incoming signal. The optimal recursive Bayesian phase estimator is formulated to solve this problem for a single line-of-sight tracking loop. In low SNR environments, the nonlinear code phase error probability densities become non-Gaussian, asymmetric, and often multi-modal. A closed-form solution to the integrals in the Bayesian estimator is intractable and numerical approximations to the Bayesian estimator are required. In particular, a grid-based Bayesian estimator and a Sampling Importance Resampling (SIR) particle filter are applied to the line-of-sight tracking problem. Finally, a Particle Filter Vector Delay Lock Loop (PF-VDLL) architecture is described that optimally combines measurements from all satellites simultaneously to compute the three-dimensional navigation solution. The results of the PF-VDLL are shown to improve perfromance over an Extended Kalman Filter (EKF) implementation. A novel approach for integrating measurements from an Inertial Measurement Unit (IMU) with the PF-VDLL to improve tracking performance is shown.
590 $a School code: 0031.
650 4 $a Engineering, Computer. $3 1669061
650 4 $a Engineering, Aerospace. $3 1018395
650 4 $a Engineering, Electronics and Electrical. $3 626636
690 $a 0464
690 $a 0538
690 $a 0544
710 2 $a University of California, Los Angeles. $3 626622
773 0 $t Dissertation Abstracts International $g 72-05B.
790 1 0 $a Speyer, Jason, $e advisor
790 $a 0031
791 $a Ph.D.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3446858