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035 a| (Sirsi) o1296624539;

035 a| (OCoLC)1296624539;

049 a| EREE;

090 a| G109.5;

100 1 a| Shah, Nihar,e| author.?| UNAUTHORIZED;

245 10 a| Using random sampling consensus (RANSAC) to detect errors in global navigation satellite systems (GNSS) signals and data /c| by Nihar Shah.;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2021.;

300 a| 1 online resource (74 pages) :b| illustrations (some color);

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text file;

347 b| PDF;

347 c| 4.076 MB;

538 a| System requirements: Adobe Reader.;

538 a| Mode of access: World Wide Web.;

502 b| M.S.c| East Carolina Universityd| 2021;

500 a| Presented to the Faculty of the Department of Engineering;

500 a| Advisor: Zhen Zhu;

500 a| Title from PDF t.p. (viewed February 14, 2023).;

520 3 a| A positioning, navigation, and timing (PNT) signal can be used to estimate a user's position at an identified time. A global navigation satellite system (GNSS) uses the PNT signal to provide satellite-based navigation. Advanced receivers can track multiple GNSS constellations simultaneously. In order to have a robust and accurate solution, a user needs to detect any faulty measurements and data, and identify which satellite provided them so that faulty satellite can be excluded from a GNSS solution. Differencing techniques, such as time-differenced carrier phase (TDCP), provide for error reduction. The random sample consensus (RANSAC) method allows for the smoothing of data, even when there are a lot of gross errors present in the data set. The residuals from RANSAC and TDCP were studied to determine if they can be used to detect and identify error sources. A downsampling and thresholding method was able to identify first-order biases with slopes on the order of 10̄ ⁶ within minutes, while biases with slopes on the order of 10̄ ⁷ were identified on the order of one hour. The residuals from RANSAC and TDCP were ultimately able to detect and identify error sources.;

504 a| Includes bibliographical references.;

650 0 a| Global Positioning Systemx| Data processing.=| ^A312695;

650 0 a| Problem solving.=| ^A2850;

650 0 a| Artificial satellites in navigation.=| ^A368361;

653 a| Time-differenced carrier phase;

653 a| Random sample consensus;

653 a| global navigation satellite system;

653 a| positioning, navigation, and timing;

655 7 a| Academic theses.2| fast0| (OCoLC)fst01726453;

655 7 a| Academic theses.2| lcgft;

655 7 a| Thèses et écrits académiques.2| rvmgf0| (CaQQLa)RVMGF-000001173;

655 2 a| Academic Dissertation.0| (DNLM)D019478?| UNAUTHORIZED;

700 1 a| Zhu, Zhene| degree supervisor.=| ^A1347982;

710 2 a| East Carolina University.b| Department of Engineering.=| ^A1231592;

856 40 u| http://hdl.handle.net/10342/9716z| Access via ScholarShip;

949 o| wjh;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 5792267;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5792267-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 2/14/2022x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5792267-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 2/14/2022x| ETDz| HERESOURCE;

Using random sampling consensus (RANSAC) to detect errors in global navigation satellite systems (GNSS) signals and data / by Nihar Shah.

Author/creator	Shah, Nihar author.
Other author	Zhu, Zhen degree supervisor.
Other author	East Carolina University. Department of Engineering.
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University], 2021.
Description	1 online resource (74 pages) : illustrations (some color)
Supplemental Content	Access via ScholarShip
Subjects	Global Positioning System--Data processing. Problem solving. Artificial satellites in navigation.

Summary	A positioning, navigation, and timing (PNT) signal can be used to estimate a user's position at an identified time. A global navigation satellite system (GNSS) uses the PNT signal to provide satellite-based navigation. Advanced receivers can track multiple GNSS constellations simultaneously. In order to have a robust and accurate solution, a user needs to detect any faulty measurements and data, and identify which satellite provided them so that faulty satellite can be excluded from a GNSS solution. Differencing techniques, such as time-differenced carrier phase (TDCP), provide for error reduction. The random sample consensus (RANSAC) method allows for the smoothing of data, even when there are a lot of gross errors present in the data set. The residuals from RANSAC and TDCP were studied to determine if they can be used to detect and identify error sources. A downsampling and thresholding method was able to identify first-order biases with slopes on the order of 10̄ ⁶ within minutes, while biases with slopes on the order of 10̄ ⁷ were identified on the order of one hour. The residuals from RANSAC and TDCP were ultimately able to detect and identify error sources.
General note	Presented to the Faculty of the Department of Engineering
General note	Advisor: Zhen Zhu
General note	Title from PDF t.p. (viewed February 14, 2023).
Dissertation note	M.S. East Carolina University 2021
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.
Genre/form	Academic theses.
Genre/form	Academic theses.
Genre/form	Thèses et écrits académiques.

Using random sampling consensus (RANSAC) to detect errors in global navigation satellite systems (GNSS) signals and data / by Nihar Shah.

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