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Noncoherent MIMO Radar for Location and Velocity Estimation: More Antennas Means Better Performance
This paper presents an analysis of the joint estimation of target location and velocity using a multiple-input multiple-output (MIMO) radar employing noncoherent processing for a complex Gaussian extended target. A MIMO radar with M transmit and N receive antennas is considered. To provide insight,...
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Published in: | IEEE transactions on signal processing 2010-07, Vol.58 (7), p.3661-3680 |
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description | This paper presents an analysis of the joint estimation of target location and velocity using a multiple-input multiple-output (MIMO) radar employing noncoherent processing for a complex Gaussian extended target. A MIMO radar with M transmit and N receive antennas is considered. To provide insight, we focus on a simplified case first, assuming orthogonal waveforms, temporally and spatially white noise-plus-clutter, and independent reflection coefficients. Under these simplifying assumptions, the maximum-likelihood (ML) estimate is analyzed, and a theorem demonstrating the asymptotic consistency, large MN , of the ML estimate is provided. Numerical investigations, given later, indicate similar behavior for some reasonable cases violating the simplifying assumptions. In these initial investigations, we study unconstrained systems, in terms of complexity and energy, where each added transmit antenna employs a fixed energy so that the total transmitted energy is allowed to increase as we increase the number of transmit antennas. Following this, we also look at constrained systems, where the total system energy and complexity are fixed. To approximate systems of fixed complexity in an abstract way, we restrict the total number of antennas employed to be fixed. Here, we show numerical examples which indicate a preference for receive antennas, similar to MIMO communications, but where systems with multiple transmit antennas yield the smallest possible mean-square error (MSE). The joint Cramér-Rao bound (CRB) is calculated and the MSE of the ML estimate is analyzed. It is shown for some specific numerical examples that the signal-to-clutter-plus-noise ratio (SCNR) threshold, indicating the SCNRs above which the MSE of the ML estimate is reasonably close to the CRB, can be lowered by increasing MN . The noncoherent MIMO radar ambiguity function (AF) is developed in two different ways and illustrated by examples. It is shown for some specific examples that the size of the product MN controls the levels of the sidelobes of the AF. |
doi_str_mv | 10.1109/TSP.2010.2044613 |
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A MIMO radar with M transmit and N receive antennas is considered. To provide insight, we focus on a simplified case first, assuming orthogonal waveforms, temporally and spatially white noise-plus-clutter, and independent reflection coefficients. Under these simplifying assumptions, the maximum-likelihood (ML) estimate is analyzed, and a theorem demonstrating the asymptotic consistency, large MN , of the ML estimate is provided. Numerical investigations, given later, indicate similar behavior for some reasonable cases violating the simplifying assumptions. In these initial investigations, we study unconstrained systems, in terms of complexity and energy, where each added transmit antenna employs a fixed energy so that the total transmitted energy is allowed to increase as we increase the number of transmit antennas. Following this, we also look at constrained systems, where the total system energy and complexity are fixed. To approximate systems of fixed complexity in an abstract way, we restrict the total number of antennas employed to be fixed. Here, we show numerical examples which indicate a preference for receive antennas, similar to MIMO communications, but where systems with multiple transmit antennas yield the smallest possible mean-square error (MSE). The joint Cramér-Rao bound (CRB) is calculated and the MSE of the ML estimate is analyzed. It is shown for some specific numerical examples that the signal-to-clutter-plus-noise ratio (SCNR) threshold, indicating the SCNRs above which the MSE of the ML estimate is reasonably close to the CRB, can be lowered by increasing MN . The noncoherent MIMO radar ambiguity function (AF) is developed in two different ways and illustrated by examples. It is shown for some specific examples that the size of the product MN controls the levels of the sidelobes of the AF.</description><identifier>ISSN: 1053-587X</identifier><identifier>EISSN: 1941-0476</identifier><identifier>DOI: 10.1109/TSP.2010.2044613</identifier><identifier>CODEN: ITPRED</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Acoustic reflection ; Ambiguity function (AF) ; Antennas ; Asymptotic properties ; Complexity ; Cramér-Rao bound (CRB) ; Energy transmission ; Estimates ; joint estimation ; Mathematical analysis ; Maximum likelihood estimation ; mean-square error (MSE) ; MIMO ; noncoherent MIMO radar ; Performance analysis ; Position (location) ; Radar ; Radar antennas ; Receiving antennas ; Size control ; Transmitting antennas ; White noise</subject><ispartof>IEEE transactions on signal processing, 2010-07, Vol.58 (7), p.3661-3680</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Jul 2010</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c370t-29713b1150da7f16e77f66f1aa37a066d03ce77e4f24164329b0b1934be6f0e33</citedby><cites>FETCH-LOGICAL-c370t-29713b1150da7f16e77f66f1aa37a066d03ce77e4f24164329b0b1934be6f0e33</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5422709$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,54796</link.rule.ids></links><search><creatorcontrib>Qian He</creatorcontrib><creatorcontrib>Blum, Rick S</creatorcontrib><creatorcontrib>Haimovich, Alexander M</creatorcontrib><title>Noncoherent MIMO Radar for Location and Velocity Estimation: More Antennas Means Better Performance</title><title>IEEE transactions on signal processing</title><addtitle>TSP</addtitle><description>This paper presents an analysis of the joint estimation of target location and velocity using a multiple-input multiple-output (MIMO) radar employing noncoherent processing for a complex Gaussian extended target. A MIMO radar with M transmit and N receive antennas is considered. To provide insight, we focus on a simplified case first, assuming orthogonal waveforms, temporally and spatially white noise-plus-clutter, and independent reflection coefficients. Under these simplifying assumptions, the maximum-likelihood (ML) estimate is analyzed, and a theorem demonstrating the asymptotic consistency, large MN , of the ML estimate is provided. Numerical investigations, given later, indicate similar behavior for some reasonable cases violating the simplifying assumptions. In these initial investigations, we study unconstrained systems, in terms of complexity and energy, where each added transmit antenna employs a fixed energy so that the total transmitted energy is allowed to increase as we increase the number of transmit antennas. Following this, we also look at constrained systems, where the total system energy and complexity are fixed. To approximate systems of fixed complexity in an abstract way, we restrict the total number of antennas employed to be fixed. Here, we show numerical examples which indicate a preference for receive antennas, similar to MIMO communications, but where systems with multiple transmit antennas yield the smallest possible mean-square error (MSE). The joint Cramér-Rao bound (CRB) is calculated and the MSE of the ML estimate is analyzed. It is shown for some specific numerical examples that the signal-to-clutter-plus-noise ratio (SCNR) threshold, indicating the SCNRs above which the MSE of the ML estimate is reasonably close to the CRB, can be lowered by increasing MN . The noncoherent MIMO radar ambiguity function (AF) is developed in two different ways and illustrated by examples. It is shown for some specific examples that the size of the product MN controls the levels of the sidelobes of the AF.</description><subject>Acoustic reflection</subject><subject>Ambiguity function (AF)</subject><subject>Antennas</subject><subject>Asymptotic properties</subject><subject>Complexity</subject><subject>Cramér-Rao bound (CRB)</subject><subject>Energy transmission</subject><subject>Estimates</subject><subject>joint estimation</subject><subject>Mathematical analysis</subject><subject>Maximum likelihood estimation</subject><subject>mean-square error (MSE)</subject><subject>MIMO</subject><subject>noncoherent MIMO radar</subject><subject>Performance analysis</subject><subject>Position (location)</subject><subject>Radar</subject><subject>Radar antennas</subject><subject>Receiving antennas</subject><subject>Size control</subject><subject>Transmitting antennas</subject><subject>White noise</subject><issn>1053-587X</issn><issn>1941-0476</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><recordid>eNpdkM1LAzEQxYMoqNW74CXgwdPqTJJNut60-AWtil94W9J0FlfaRJP04H9vtOLB08w8fm-YeYztIRwhQnP8-HB3JKBMApTSKNfYFjYKK1BGr5cealnVQ_OyybZTegNApRq9xdxN8C68UiSf-eR6csvv7cxG3oXIx8HZ3AfPrZ_xZ5oH1-dPfp5yv_jRT_gkROKnPpP3NvEJWZ_4GeVMkd9RLDsW1jvaYRudnSfa_a0D9nRx_ji6qsa3l9ej03HlpIFcicagnCLWMLOmQ03GdFp3aK00FrSegXRFI9UJhVpJ0Uxhio1UU9IdkJQDdrja-x7Dx5JSbhd9cjSfW09hmVpTS230cIiFPPhHvoVl9OW4FkEYIXBY4AGDFeViSClS177H8nr8LFD7HXpbQm-_Q29_Qy-W_ZWlJ6I_vFZCGGjkF459fDI</recordid><startdate>201007</startdate><enddate>201007</enddate><creator>Qian He</creator><creator>Blum, Rick S</creator><creator>Haimovich, Alexander M</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>F28</scope><scope>FR3</scope></search><sort><creationdate>201007</creationdate><title>Noncoherent MIMO Radar for Location and Velocity Estimation: More Antennas Means Better Performance</title><author>Qian He ; Blum, Rick S ; Haimovich, Alexander M</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c370t-29713b1150da7f16e77f66f1aa37a066d03ce77e4f24164329b0b1934be6f0e33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Acoustic reflection</topic><topic>Ambiguity function (AF)</topic><topic>Antennas</topic><topic>Asymptotic properties</topic><topic>Complexity</topic><topic>Cramér-Rao bound (CRB)</topic><topic>Energy transmission</topic><topic>Estimates</topic><topic>joint estimation</topic><topic>Mathematical analysis</topic><topic>Maximum likelihood estimation</topic><topic>mean-square error (MSE)</topic><topic>MIMO</topic><topic>noncoherent MIMO radar</topic><topic>Performance analysis</topic><topic>Position (location)</topic><topic>Radar</topic><topic>Radar antennas</topic><topic>Receiving antennas</topic><topic>Size control</topic><topic>Transmitting antennas</topic><topic>White noise</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Qian He</creatorcontrib><creatorcontrib>Blum, Rick S</creatorcontrib><creatorcontrib>Haimovich, Alexander M</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Xplore</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 signal processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Qian He</au><au>Blum, Rick S</au><au>Haimovich, Alexander M</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Noncoherent MIMO Radar for Location and Velocity Estimation: More Antennas Means Better Performance</atitle><jtitle>IEEE transactions on signal processing</jtitle><stitle>TSP</stitle><date>2010-07</date><risdate>2010</risdate><volume>58</volume><issue>7</issue><spage>3661</spage><epage>3680</epage><pages>3661-3680</pages><issn>1053-587X</issn><eissn>1941-0476</eissn><coden>ITPRED</coden><abstract>This paper presents an analysis of the joint estimation of target location and velocity using a multiple-input multiple-output (MIMO) radar employing noncoherent processing for a complex Gaussian extended target. A MIMO radar with M transmit and N receive antennas is considered. To provide insight, we focus on a simplified case first, assuming orthogonal waveforms, temporally and spatially white noise-plus-clutter, and independent reflection coefficients. Under these simplifying assumptions, the maximum-likelihood (ML) estimate is analyzed, and a theorem demonstrating the asymptotic consistency, large MN , of the ML estimate is provided. Numerical investigations, given later, indicate similar behavior for some reasonable cases violating the simplifying assumptions. In these initial investigations, we study unconstrained systems, in terms of complexity and energy, where each added transmit antenna employs a fixed energy so that the total transmitted energy is allowed to increase as we increase the number of transmit antennas. Following this, we also look at constrained systems, where the total system energy and complexity are fixed. To approximate systems of fixed complexity in an abstract way, we restrict the total number of antennas employed to be fixed. Here, we show numerical examples which indicate a preference for receive antennas, similar to MIMO communications, but where systems with multiple transmit antennas yield the smallest possible mean-square error (MSE). The joint Cramér-Rao bound (CRB) is calculated and the MSE of the ML estimate is analyzed. It is shown for some specific numerical examples that the signal-to-clutter-plus-noise ratio (SCNR) threshold, indicating the SCNRs above which the MSE of the ML estimate is reasonably close to the CRB, can be lowered by increasing MN . The noncoherent MIMO radar ambiguity function (AF) is developed in two different ways and illustrated by examples. It is shown for some specific examples that the size of the product MN controls the levels of the sidelobes of the AF.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TSP.2010.2044613</doi><tpages>20</tpages></addata></record> |
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subjects | Acoustic reflection Ambiguity function (AF) Antennas Asymptotic properties Complexity Cramér-Rao bound (CRB) Energy transmission Estimates joint estimation Mathematical analysis Maximum likelihood estimation mean-square error (MSE) MIMO noncoherent MIMO radar Performance analysis Position (location) Radar Radar antennas Receiving antennas Size control Transmitting antennas White noise |
title | Noncoherent MIMO Radar for Location and Velocity Estimation: More Antennas Means Better Performance |
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