Tracking the direction-of-arrival of multiple moving targets by passive arrays: algorithm
We propose a maximum likelihood (ML) approach for tracking the direction-of-arrival (DOA) of multiple moving targets by a passive array. A locally linear model is assumed for the target motion, and the multiple target states (MTSs) are defined to describe the states of the target motion, The locally...
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| Published in | IEEE transactions on signal processing Vol. 47; no. 10; pp. 2655 - 2666 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York, NY
IEEE
1999
Institute of Electrical and Electronics Engineers |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1053-587X |
| DOI | 10.1109/78.790648 |
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| Abstract | We propose a maximum likelihood (ML) approach for tracking the direction-of-arrival (DOA) of multiple moving targets by a passive array. A locally linear model is assumed for the target motion, and the multiple target states (MTSs) are defined to describe the states of the target motion, The locally linear model is shown to be strongly locally observable almost everywhere. The approach is to estimate the initial MTS by maximizing the likelihood function of the array data. The tracking is implemented by prediction through the target motion dynamics using the initial MTS estimate. By incorporating the target motion dynamics, the algorithm is able to eliminate the spread spectrum effects due to target motion. A modified Newton-type algorithm is also presented, which ensures fast convergence of the algorithm. Finally, numerical simulations are included to show the effectiveness of the proposed algorithm. |
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| AbstractList | We propose a maximum likelihood (ML) approach for tracking the direction-of-arrival (DOA) of multiple moving targets by a passive array. A locally linear model is assumed for the target motion, and the multiple target states (MTSs) are defined to describe the states of the target motion, The locally linear model is shown to be strongly locally observable almost everywhere. The approach is to estimate the initial MTS by maximizing the likelihood function of the array data. The tracking is implemented by prediction through the target motion dynamics using the initial MTS estimate. By incorporating the target motion dynamics, the algorithm is able to eliminate the spread spectrum effects due to target motion. A modified Newton-type algorithm is also presented, which ensures fast convergence of the algorithm. Finally, numerical simulations are included to show the effectiveness of the proposed algorithm We propose a maximum likelihood (ML) approach for tracking the direction-of-arrival (DOA) of multiple moving targets by a passive array. A locally linear model is assumed for the target motion, and the multiple target states (MTSs) are defined to describe the states of the target motion, The locally linear model is shown to be strongly locally observable almost everywhere. The approach is to estimate the initial MTS by maximizing the likelihood function of the array data. The tracking is implemented by prediction through the target motion dynamics using the initial MTS estimate. By incorporating the target motion dynamics, the algorithm is able to eliminate the spread spectrum effects due to target motion. A modified Newton-type algorithm is also presented, which ensures fast convergence of the algorithm. Finally, numerical simulations are included to show the effectiveness of the proposed algorithm. In this paper, we propose a maximum likelihood (ML) approach for tracking the direction-of-arrival (DOA) of multiple moving targets by a passive array. A locally linear model is assumed for the target motion, and the multiple target states (MTS's) are defined to describe the states of the target motion. The locally linear model is shown to be strongly locally observable almost everywhere. The approach is to estimate the initial MTS by maximizing the likelihood function of the array data. The tracking is implemented by prediction through the target motion dynamics using the initial MTS estimate. By incorporating the target motion dynamics, the algorithm is able to eliminate the spread spectrum effects due to target motion. A modified Newton-type algorithm is also presented, which ensures fast convergence of the algorithm. Finally, numerical simulations are included to show the effectiveness of the proposed algorithm. |
| Author | Yip, P.C. Yifeng Zhou Leung, H. |
| Author_xml | – sequence: 1 surname: Yifeng Zhou fullname: Yifeng Zhou organization: Electron. Support Meas. Sect., Defence Res. Establ., Ottawa, Ont., Canada – sequence: 2 givenname: P.C. surname: Yip fullname: Yip, P.C. – sequence: 3 givenname: H. surname: Leung fullname: Leung, H. |
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| Keywords | Mobile radiocommunication Direction of arrival Moving target Motion estimation Optimization method Satellite telecommunication Signal processing Multiple target Maximum likelihood Newton method Observability |
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| References | ref35 ref13 ref12 ref30 ref33 ref11 ref10 dennis (ref18) 1983 banks (ref17) 1988 ref2 ref1 ref16 ref19 nijeijer (ref15) 1982; 36 zhou (ref31) 1995; ii ligget (ref5) 1973 schmidt (ref3) 1981 ref24 ref23 ref26 zhou (ref32) 1995 ref25 ref20 levenberg (ref29) 1944; 2 ref22 ref21 van tree (ref36) 1986 ref28 ref27 bar-shalom (ref14) 1988 ref8 ref7 ref9 ref4 ref6 bellman (ref34) 1972 |
| References_xml | – year: 1995 ident: ref32 publication-title: Tracking of DOA s of multiple moving targets by passive arrays and asymptotic performance analysis – ident: ref35 doi: 10.1109/29.61541 – ident: ref4 doi: 10.1109/78.80966 – ident: ref6 doi: 10.1109/ICASSP.1988.197258 – ident: ref19 doi: 10.1109/29.31267 – ident: ref33 doi: 10.1007/BF01932995 – ident: ref16 doi: 10.1137/0317011 – ident: ref26 doi: 10.1109/TASSP.1983.1164183 – year: 1988 ident: ref14 publication-title: Tracking and Data Association – year: 1973 ident: ref5 article-title: passive sonar: fitting models to multiple time series publication-title: NATO ASI on Signal Processing – ident: ref2 doi: 10.1109/PROC.1969.7278 – volume: 36 start-page: 867 year: 1982 ident: ref15 article-title: observability of autonomous discrete time nonlinear system publication-title: Int J Contr doi: 10.1080/00207178208932936 – ident: ref10 doi: 10.1109/7.249135 – ident: ref7 doi: 10.1109/ICASSP.1991.150711 – ident: ref22 doi: 10.1109/ICASSP.1988.197217 – year: 1988 ident: ref17 publication-title: Mathematical Theories of Nonlinear Systems – ident: ref12 doi: 10.1109/78.295205 – year: 1981 ident: ref3 publication-title: A Signal Subspace Approach to Multiple Emitter Location and Spectral Estimation – ident: ref30 doi: 10.2307/1909768 – ident: ref27 doi: 10.1109/78.139255 – ident: ref1 doi: 10.1109/PROC.1982.12430 – volume: ii start-page: 1087 year: 1995 ident: ref31 article-title: asymptotic efficiency of the maximum likelihood tracking algorithm for passive arrays publication-title: Proc ICNNSP – year: 1983 ident: ref18 publication-title: Numerical Methods for Unconstrained Optimization and Nonlinear Equations – volume: 2 start-page: 164 year: 1944 ident: ref29 article-title: a method for the solution of certain nonlinear problems in least squares publication-title: Quart Appl Math doi: 10.1090/qam/10666 – ident: ref20 doi: 10.1109/29.1552 – ident: ref23 doi: 10.1109/ICASSP.1988.197061 – ident: ref13 doi: 10.1109/78.398728 – ident: ref8 doi: 10.1109/7.53463 – ident: ref25 doi: 10.1109/78.97999 – year: 1986 ident: ref36 publication-title: Detection Estimation and Modulation Theory Part I – ident: ref21 doi: 10.1109/29.60075 – ident: ref28 doi: 10.1137/0710036 – ident: ref9 doi: 10.1109/78.80796 – ident: ref11 doi: 10.1109/7.272289 – year: 1972 ident: ref34 publication-title: Introduction to Matrix Analysis – ident: ref24 doi: 10.1109/ICASSP.1989.267029 |
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| Snippet | We propose a maximum likelihood (ML) approach for tracking the direction-of-arrival (DOA) of multiple moving targets by a passive array. A locally linear model... In this paper, we propose a maximum likelihood (ML) approach for tracking the direction-of-arrival (DOA) of multiple moving targets by a passive array. A... |
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| SubjectTerms | Algorithms Applied sciences Array signal processing Arrays Artificial satellites Convergence Covariance matrix Detection, estimation, filtering, equalization, prediction Direction of arrival estimation Dynamics Equipments and installations Estimates Exact sciences and technology Information, signal and communications theory Mathematical models Maximum likelihood estimation Mobile communication Mobile radiocommunication systems Moving targets Radiocommunications Sensor arrays Signal and communications theory Signal processing algorithms Signal, noise Spread spectrum communication Target tracking Telecommunications Telecommunications and information theory Tracking |
| Title | Tracking the direction-of-arrival of multiple moving targets by passive arrays: algorithm |
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