A simple approach of range-based positioning with low computational complexity
For range based positioning the least square (LS) criterion and its produced solution exhibit superb estimation performance, but generally at a very high computational complexity. In this letter we consider the issue how to approach such LS solution in estimation performance at low computational com...
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| Veröffentlicht in: | IEEE transactions on wireless communications 2009-12, Vol.8 (12), p.5832-5836 |
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| container_title | IEEE transactions on wireless communications |
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| creator | Zhu, Shouhong Ding, Zhiguo |
| description | For range based positioning the least square (LS) criterion and its produced solution exhibit superb estimation performance, but generally at a very high computational complexity. In this letter we consider the issue how to approach such LS solution in estimation performance at low computational complexity. We propose a novel algorithm that is based on the equations linearized from range measurement equations and implements a weighted least square criterion in a computationally efficient way. The proposed algorithm involves a quadratic equation linking the linearization-caused extra variable and the position to be estimated, thus results in a closed form solution.We analyze and simulate its estimation performance, and evidently show that the proposed algorithm can very closely approach the LS solution in estimation performance at a significantly low computational complexity. |
| doi_str_mv | 10.1109/TWC.2009.12.090905 |
| format | Article |
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In this letter we consider the issue how to approach such LS solution in estimation performance at low computational complexity. We propose a novel algorithm that is based on the equations linearized from range measurement equations and implements a weighted least square criterion in a computationally efficient way. The proposed algorithm involves a quadratic equation linking the linearization-caused extra variable and the position to be estimated, thus results in a closed form solution.We analyze and simulate its estimation performance, and evidently show that the proposed algorithm can very closely approach the LS solution in estimation performance at a significantly low computational complexity.</description><identifier>ISSN: 1536-1276</identifier><identifier>EISSN: 1558-2248</identifier><identifier>DOI: 10.1109/TWC.2009.12.090905</identifier><identifier>CODEN: ITWCAX</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Algorithm design and analysis ; Algorithms ; Applied sciences ; Closed-form solution ; Complexity ; Computation ; Computational complexity ; Computational efficiency ; Criteria ; Detection, estimation, filtering, equalization, prediction ; Equations ; Exact sciences and technology ; Information, signal and communications theory ; Joining processes ; Least squares approximation ; Least squares method ; Least squares methods ; Mathematical analysis ; Mathematical models ; Performance analysis ; Position measurement ; sensor localization ; Services and terminals of telecommunications ; Signal and communications theory ; Signal, noise ; Systems, networks and services of telecommunications ; Telecommunications ; Telecommunications and information theory ; Telemetry. 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In this letter we consider the issue how to approach such LS solution in estimation performance at low computational complexity. We propose a novel algorithm that is based on the equations linearized from range measurement equations and implements a weighted least square criterion in a computationally efficient way. The proposed algorithm involves a quadratic equation linking the linearization-caused extra variable and the position to be estimated, thus results in a closed form solution.We analyze and simulate its estimation performance, and evidently show that the proposed algorithm can very closely approach the LS solution in estimation performance at a significantly low computational complexity.</description><subject>Algorithm design and analysis</subject><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Closed-form solution</subject><subject>Complexity</subject><subject>Computation</subject><subject>Computational complexity</subject><subject>Computational efficiency</subject><subject>Criteria</subject><subject>Detection, estimation, filtering, equalization, prediction</subject><subject>Equations</subject><subject>Exact sciences and technology</subject><subject>Information, signal and communications theory</subject><subject>Joining processes</subject><subject>Least squares approximation</subject><subject>Least squares method</subject><subject>Least squares methods</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Performance analysis</subject><subject>Position measurement</subject><subject>sensor localization</subject><subject>Services and terminals of telecommunications</subject><subject>Signal and communications theory</subject><subject>Signal, noise</subject><subject>Systems, networks and services of telecommunications</subject><subject>Telecommunications</subject><subject>Telecommunications and information theory</subject><subject>Telemetry. 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Remote supervision. Telewarning. Remote control</topic><topic>Wireless ad hoc sensor network</topic><topic>Wireless sensor networks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhu, Shouhong</creatorcontrib><creatorcontrib>Ding, Zhiguo</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on wireless communications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zhu, Shouhong</au><au>Ding, Zhiguo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A simple approach of range-based positioning with low computational complexity</atitle><jtitle>IEEE transactions on wireless communications</jtitle><stitle>TWC</stitle><date>2009-12-01</date><risdate>2009</risdate><volume>8</volume><issue>12</issue><spage>5832</spage><epage>5836</epage><pages>5832-5836</pages><issn>1536-1276</issn><eissn>1558-2248</eissn><coden>ITWCAX</coden><abstract>For range based positioning the least square (LS) criterion and its produced solution exhibit superb estimation performance, but generally at a very high computational complexity. In this letter we consider the issue how to approach such LS solution in estimation performance at low computational complexity. We propose a novel algorithm that is based on the equations linearized from range measurement equations and implements a weighted least square criterion in a computationally efficient way. The proposed algorithm involves a quadratic equation linking the linearization-caused extra variable and the position to be estimated, thus results in a closed form solution.We analyze and simulate its estimation performance, and evidently show that the proposed algorithm can very closely approach the LS solution in estimation performance at a significantly low computational complexity.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TWC.2009.12.090905</doi><tpages>5</tpages></addata></record> |
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| subjects | Algorithm design and analysis Algorithms Applied sciences Closed-form solution Complexity Computation Computational complexity Computational efficiency Criteria Detection, estimation, filtering, equalization, prediction Equations Exact sciences and technology Information, signal and communications theory Joining processes Least squares approximation Least squares method Least squares methods Mathematical analysis Mathematical models Performance analysis Position measurement sensor localization Services and terminals of telecommunications Signal and communications theory Signal, noise Systems, networks and services of telecommunications Telecommunications Telecommunications and information theory Telemetry. Remote supervision. Telewarning. Remote control Wireless ad hoc sensor network Wireless sensor networks |
| title | A simple approach of range-based positioning with low computational complexity |
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