Mixed Integer-Real Valued Adjustment (IRA) Problems: GPS Initial Cycle Ambiguity Resolution by Means of the LLL Algorithm

In order to achieve to GPS solutions of first-order accuracy and integrity, carrier phase observations as well as pseudorange observations have to be adjusted with respect to a linear/linearized model. Here the problem of mixed integer-real valued parameter adjustment (IRA) is met. Indeed, integer c...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:GPS solutions 2000-10, Vol.4 (2), p.31-44
1. Verfasser: Grafarend, Erik W.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 44
container_issue 2
container_start_page 31
container_title GPS solutions
container_volume 4
creator Grafarend, Erik W.
description In order to achieve to GPS solutions of first-order accuracy and integrity, carrier phase observations as well as pseudorange observations have to be adjusted with respect to a linear/linearized model. Here the problem of mixed integer-real valued parameter adjustment (IRA) is met. Indeed, integer cycle ambiguity unknowns have to be estimated and tested. At first we review the three concepts to deal with IRA: (i) DDD or triple difference observations are produced by a properly chosen difference operator and choice of basis, namely being free of integer-valued unknowns (ii) The real-valued unknown parameters are eliminated by a Gauss elimination step while the remaining integer-valued unknown parameters (initial cycle ambiguities) are determined by Quadratic Programming and (iii) a RA substitute model is firstly implemented (real-valued estimates of initial cycle ambiguities) and secondly a minimum distance map is designed which operates on the real-valued approximation of integers with respect to the integer data in a lattice. This is the place where the integer Gram-Schmidt orthogonalization by means of the LLL algorithm (modified LLL algorithm) is applied being illustrated by four examples. In particular, we prove that in general it is impossible to transform an oblique base of a lattice to an orthogonal base by Gram-Schmidt orthogonalization where its matrix enties are integer. The volume preserving Gram-Schmidt orthogonalization operator constraint to integer entries produces “almost orthogonal” bases which, in turn, can be used to produce the integer-valued unknown parameters (initial cycle ambiguities) from the LLL algorithm (modified LLL algorithm). Systematic errors generated by “almost orthogonal” lattice bases are quantified by A. K. Lenstra et al. (1982) as well as M. Pohst (1987). The solution point z^ of Integer Least Squares generated by the LLL algorithm is z^=(L′)−1[L′x^] ∈ ℤm where L is the lower triangular Gram-Schmidt matrix rounded to nearest integers, [L], and z^=[L′x^] are the nearest integers of L'◯, ◯ being the real valued approximation of z ∈ ℤm, the m-dimensional lattice space Λ. Indeed due to “almost orthogonality” of the integer Gram-Schmidt procedure, the solution point z^ is only suboptimal, only close to “least squares.” © 2000 John Wiley & Sons, Inc.
doi_str_mv 10.1007/PL00012840
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2348856226</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2348856226</sourcerecordid><originalsourceid>FETCH-LOGICAL-c172t-61d975ec5d94417cdcdf6e7a67fd36d92ea407aac235d961d4780bcd80083d3b3</originalsourceid><addsrcrecordid>eNpF0E1LxDAQBuAiCurqxV8Q8KJCdZJ-JPVWFl0XurisH9eSJululrbRJAX7740oeJpheGYG3ii6wHCLAejdugIATFgKB9EJzgiOMWP5YeiBQZwlFI6jU-f2AASKIj2JppX-UhItB6-2ysYbxTv0zrsxzEq5H53v1eDR1XJTXqO1NU2nenePFuuXsKK9Dno-iU6hsm_0dtR-QhvlTDd6bQbUTGil-OCQaZHfKVRVFSq7rbHa7_qz6KjlnVPnf3UWvT0-vM6f4up5sZyXVSwwJT7OsSxopkQmizTFVEgh21xRntNWJrksiOIpUM4FSQIJOqUMGiEZAEtk0iSz6PL37oc1n6Nyvt6b0Q7hZU2SlLEsJyQP6uZXCWucs6qtP6zuuZ1qDPVPtPV_tMk3UCJqVg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2348856226</pqid></control><display><type>article</type><title>Mixed Integer-Real Valued Adjustment (IRA) Problems: GPS Initial Cycle Ambiguity Resolution by Means of the LLL Algorithm</title><source>Springer Nature</source><creator>Grafarend, Erik W.</creator><creatorcontrib>Grafarend, Erik W.</creatorcontrib><description>In order to achieve to GPS solutions of first-order accuracy and integrity, carrier phase observations as well as pseudorange observations have to be adjusted with respect to a linear/linearized model. Here the problem of mixed integer-real valued parameter adjustment (IRA) is met. Indeed, integer cycle ambiguity unknowns have to be estimated and tested. At first we review the three concepts to deal with IRA: (i) DDD or triple difference observations are produced by a properly chosen difference operator and choice of basis, namely being free of integer-valued unknowns (ii) The real-valued unknown parameters are eliminated by a Gauss elimination step while the remaining integer-valued unknown parameters (initial cycle ambiguities) are determined by Quadratic Programming and (iii) a RA substitute model is firstly implemented (real-valued estimates of initial cycle ambiguities) and secondly a minimum distance map is designed which operates on the real-valued approximation of integers with respect to the integer data in a lattice. This is the place where the integer Gram-Schmidt orthogonalization by means of the LLL algorithm (modified LLL algorithm) is applied being illustrated by four examples. In particular, we prove that in general it is impossible to transform an oblique base of a lattice to an orthogonal base by Gram-Schmidt orthogonalization where its matrix enties are integer. The volume preserving Gram-Schmidt orthogonalization operator constraint to integer entries produces “almost orthogonal” bases which, in turn, can be used to produce the integer-valued unknown parameters (initial cycle ambiguities) from the LLL algorithm (modified LLL algorithm). Systematic errors generated by “almost orthogonal” lattice bases are quantified by A. K. Lenstra et al. (1982) as well as M. Pohst (1987). The solution point z^ of Integer Least Squares generated by the LLL algorithm is z^=(L′)−1[L′x^] ∈ ℤm where L is the lower triangular Gram-Schmidt matrix rounded to nearest integers, [L], and z^=[L′x^] are the nearest integers of L'◯, ◯ being the real valued approximation of z ∈ ℤm, the m-dimensional lattice space Λ. Indeed due to “almost orthogonality” of the integer Gram-Schmidt procedure, the solution point z^ is only suboptimal, only close to “least squares.” © 2000 John Wiley &amp; Sons, Inc.</description><identifier>ISSN: 1080-5370</identifier><identifier>EISSN: 1521-1886</identifier><identifier>DOI: 10.1007/PL00012840</identifier><language>eng</language><publisher>New York: Springer Nature B.V</publisher><subject>Algorithms ; Ambiguity ; Ambiguity resolution (mathematics) ; Approximation ; Finite differences ; Gaussian elimination ; Least squares ; Mathematical models ; Mixed integer ; Numbers ; Operators (mathematics) ; Orthogonality ; Parameter modification ; Quadratic programming ; Systematic errors</subject><ispartof>GPS solutions, 2000-10, Vol.4 (2), p.31-44</ispartof><rights>GPS Solutions is a copyright of Springer, (2000). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c172t-61d975ec5d94417cdcdf6e7a67fd36d92ea407aac235d961d4780bcd80083d3b3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Grafarend, Erik W.</creatorcontrib><title>Mixed Integer-Real Valued Adjustment (IRA) Problems: GPS Initial Cycle Ambiguity Resolution by Means of the LLL Algorithm</title><title>GPS solutions</title><description>In order to achieve to GPS solutions of first-order accuracy and integrity, carrier phase observations as well as pseudorange observations have to be adjusted with respect to a linear/linearized model. Here the problem of mixed integer-real valued parameter adjustment (IRA) is met. Indeed, integer cycle ambiguity unknowns have to be estimated and tested. At first we review the three concepts to deal with IRA: (i) DDD or triple difference observations are produced by a properly chosen difference operator and choice of basis, namely being free of integer-valued unknowns (ii) The real-valued unknown parameters are eliminated by a Gauss elimination step while the remaining integer-valued unknown parameters (initial cycle ambiguities) are determined by Quadratic Programming and (iii) a RA substitute model is firstly implemented (real-valued estimates of initial cycle ambiguities) and secondly a minimum distance map is designed which operates on the real-valued approximation of integers with respect to the integer data in a lattice. This is the place where the integer Gram-Schmidt orthogonalization by means of the LLL algorithm (modified LLL algorithm) is applied being illustrated by four examples. In particular, we prove that in general it is impossible to transform an oblique base of a lattice to an orthogonal base by Gram-Schmidt orthogonalization where its matrix enties are integer. The volume preserving Gram-Schmidt orthogonalization operator constraint to integer entries produces “almost orthogonal” bases which, in turn, can be used to produce the integer-valued unknown parameters (initial cycle ambiguities) from the LLL algorithm (modified LLL algorithm). Systematic errors generated by “almost orthogonal” lattice bases are quantified by A. K. Lenstra et al. (1982) as well as M. Pohst (1987). The solution point z^ of Integer Least Squares generated by the LLL algorithm is z^=(L′)−1[L′x^] ∈ ℤm where L is the lower triangular Gram-Schmidt matrix rounded to nearest integers, [L], and z^=[L′x^] are the nearest integers of L'◯, ◯ being the real valued approximation of z ∈ ℤm, the m-dimensional lattice space Λ. Indeed due to “almost orthogonality” of the integer Gram-Schmidt procedure, the solution point z^ is only suboptimal, only close to “least squares.” © 2000 John Wiley &amp; Sons, Inc.</description><subject>Algorithms</subject><subject>Ambiguity</subject><subject>Ambiguity resolution (mathematics)</subject><subject>Approximation</subject><subject>Finite differences</subject><subject>Gaussian elimination</subject><subject>Least squares</subject><subject>Mathematical models</subject><subject>Mixed integer</subject><subject>Numbers</subject><subject>Operators (mathematics)</subject><subject>Orthogonality</subject><subject>Parameter modification</subject><subject>Quadratic programming</subject><subject>Systematic errors</subject><issn>1080-5370</issn><issn>1521-1886</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2000</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpF0E1LxDAQBuAiCurqxV8Q8KJCdZJ-JPVWFl0XurisH9eSJululrbRJAX7740oeJpheGYG3ii6wHCLAejdugIATFgKB9EJzgiOMWP5YeiBQZwlFI6jU-f2AASKIj2JppX-UhItB6-2ysYbxTv0zrsxzEq5H53v1eDR1XJTXqO1NU2nenePFuuXsKK9Dno-iU6hsm_0dtR-QhvlTDd6bQbUTGil-OCQaZHfKVRVFSq7rbHa7_qz6KjlnVPnf3UWvT0-vM6f4up5sZyXVSwwJT7OsSxopkQmizTFVEgh21xRntNWJrksiOIpUM4FSQIJOqUMGiEZAEtk0iSz6PL37oc1n6Nyvt6b0Q7hZU2SlLEsJyQP6uZXCWucs6qtP6zuuZ1qDPVPtPV_tMk3UCJqVg</recordid><startdate>200010</startdate><enddate>200010</enddate><creator>Grafarend, Erik W.</creator><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>H8D</scope><scope>HCIFZ</scope><scope>L7M</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope></search><sort><creationdate>200010</creationdate><title>Mixed Integer-Real Valued Adjustment (IRA) Problems: GPS Initial Cycle Ambiguity Resolution by Means of the LLL Algorithm</title><author>Grafarend, Erik W.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c172t-61d975ec5d94417cdcdf6e7a67fd36d92ea407aac235d961d4780bcd80083d3b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2000</creationdate><topic>Algorithms</topic><topic>Ambiguity</topic><topic>Ambiguity resolution (mathematics)</topic><topic>Approximation</topic><topic>Finite differences</topic><topic>Gaussian elimination</topic><topic>Least squares</topic><topic>Mathematical models</topic><topic>Mixed integer</topic><topic>Numbers</topic><topic>Operators (mathematics)</topic><topic>Orthogonality</topic><topic>Parameter modification</topic><topic>Quadratic programming</topic><topic>Systematic errors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Grafarend, Erik W.</creatorcontrib><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Earth, Atmospheric &amp; Aquatic Science</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Aerospace Database</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>ProQuest Earth, Atmospheric &amp; Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>GPS solutions</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Grafarend, Erik W.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Mixed Integer-Real Valued Adjustment (IRA) Problems: GPS Initial Cycle Ambiguity Resolution by Means of the LLL Algorithm</atitle><jtitle>GPS solutions</jtitle><date>2000-10</date><risdate>2000</risdate><volume>4</volume><issue>2</issue><spage>31</spage><epage>44</epage><pages>31-44</pages><issn>1080-5370</issn><eissn>1521-1886</eissn><abstract>In order to achieve to GPS solutions of first-order accuracy and integrity, carrier phase observations as well as pseudorange observations have to be adjusted with respect to a linear/linearized model. Here the problem of mixed integer-real valued parameter adjustment (IRA) is met. Indeed, integer cycle ambiguity unknowns have to be estimated and tested. At first we review the three concepts to deal with IRA: (i) DDD or triple difference observations are produced by a properly chosen difference operator and choice of basis, namely being free of integer-valued unknowns (ii) The real-valued unknown parameters are eliminated by a Gauss elimination step while the remaining integer-valued unknown parameters (initial cycle ambiguities) are determined by Quadratic Programming and (iii) a RA substitute model is firstly implemented (real-valued estimates of initial cycle ambiguities) and secondly a minimum distance map is designed which operates on the real-valued approximation of integers with respect to the integer data in a lattice. This is the place where the integer Gram-Schmidt orthogonalization by means of the LLL algorithm (modified LLL algorithm) is applied being illustrated by four examples. In particular, we prove that in general it is impossible to transform an oblique base of a lattice to an orthogonal base by Gram-Schmidt orthogonalization where its matrix enties are integer. The volume preserving Gram-Schmidt orthogonalization operator constraint to integer entries produces “almost orthogonal” bases which, in turn, can be used to produce the integer-valued unknown parameters (initial cycle ambiguities) from the LLL algorithm (modified LLL algorithm). Systematic errors generated by “almost orthogonal” lattice bases are quantified by A. K. Lenstra et al. (1982) as well as M. Pohst (1987). The solution point z^ of Integer Least Squares generated by the LLL algorithm is z^=(L′)−1[L′x^] ∈ ℤm where L is the lower triangular Gram-Schmidt matrix rounded to nearest integers, [L], and z^=[L′x^] are the nearest integers of L'◯, ◯ being the real valued approximation of z ∈ ℤm, the m-dimensional lattice space Λ. Indeed due to “almost orthogonality” of the integer Gram-Schmidt procedure, the solution point z^ is only suboptimal, only close to “least squares.” © 2000 John Wiley &amp; Sons, Inc.</abstract><cop>New York</cop><pub>Springer Nature B.V</pub><doi>10.1007/PL00012840</doi><tpages>14</tpages></addata></record>
fulltext fulltext
identifier ISSN: 1080-5370
ispartof GPS solutions, 2000-10, Vol.4 (2), p.31-44
issn 1080-5370
1521-1886
language eng
recordid cdi_proquest_journals_2348856226
source Springer Nature
subjects Algorithms
Ambiguity
Ambiguity resolution (mathematics)
Approximation
Finite differences
Gaussian elimination
Least squares
Mathematical models
Mixed integer
Numbers
Operators (mathematics)
Orthogonality
Parameter modification
Quadratic programming
Systematic errors
title Mixed Integer-Real Valued Adjustment (IRA) Problems: GPS Initial Cycle Ambiguity Resolution by Means of the LLL Algorithm
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T15%3A33%3A32IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Mixed%20Integer-Real%20Valued%20Adjustment%20(IRA)%20Problems:%20GPS%20Initial%20Cycle%20Ambiguity%20Resolution%20by%20Means%20of%20the%20LLL%20Algorithm&rft.jtitle=GPS%20solutions&rft.au=Grafarend,%20Erik%20W.&rft.date=2000-10&rft.volume=4&rft.issue=2&rft.spage=31&rft.epage=44&rft.pages=31-44&rft.issn=1080-5370&rft.eissn=1521-1886&rft_id=info:doi/10.1007/PL00012840&rft_dat=%3Cproquest_cross%3E2348856226%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2348856226&rft_id=info:pmid/&rfr_iscdi=true