<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>The conventional RAIM based on the least square algorithm of the residuals of the snapshot data can detect the single outlier with high probability, but it can miss the multiple outliers frequently. So, it invites a serious problem of the missed detection. We must clarify this problem, and develop a method to prevent the missed detection of the multiple outliers. In the present paper, a method to detect multiple outliers more correctly is discussed, and the numerical results seem much improved and appropriate.</p></abstract>

The conventional RAIM based on the least square algorithm of the residuals of the snapshot data can detect the single outlier with high probability, but it can miss the multiple outliers frequently. So, it invites a serious problem of the missed detection. We must clarify this problem, and develop a method to prevent the missed detection of the multiple outliers. In the present paper, a method to detect multiple outliers more correctly is discussed, and the numerical results seem much improved and appropriate.

Detection, Identification and Mitigation of Outliers by Solving Observation Equations with Outliers as Part of Unknowns

School of Naval Architecture and Ocean Engineering, University of Ulsan, Ulsan 680-749, Republic of Korea

Artificial Satellites

The conventional RAIM based on the least square algorithm of the residuals of the snapshot data can detect the single outlier with high probability, but it can...

Detection, Identification and Mitigation of Outliers by Solving Observation Equations with Outliers as Part of Unknowns

Online veröffentlicht: 09. Okt. 2009

Seitenbereich: 1 - 19

DOI: https://doi.org/10.2478/v10018-009-0015-6

Schlüsselwörter
Multiple Outliers, Detection, Observation Equation, Residual

This content is open access.

Detection, Identification and Mitigation of Outliers by Solving Observation Equations with Outliers as Part of Unknowns

Online veröffentlicht: 09. Okt. 2009

Seitenbereich: 1 - 19

DOI: https://doi.org/10.2478/v10018-009-0015-6

SchlüsselwörterMultiple Outliers, Detection, Observation Equation, Residual

This content is open access.

Schlüsselwörter
Multiple Outliers, Detection, Observation Equation, Residual