Sequence optimization for integrated radar and communication systems using meta-heuristic multiobjective methods
In real-world engineering problems, several conflicting objective functions have often to be optimized simultaneously. Typically, the objective functions of these problems are too complex to solve using derivative-based optimization methods. Integration of navigation and radar functionality with com...
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| Vydáno v: | Proceedings of the IEEE National Radar Conference (1996) s. 0502 - 0507 |
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| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
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IEEE
01.05.2017
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| ISSN: | 2375-5318 |
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| Abstract | In real-world engineering problems, several conflicting objective functions have often to be optimized simultaneously. Typically, the objective functions of these problems are too complex to solve using derivative-based optimization methods. Integration of navigation and radar functionality with communication applications is such a problem. Designing sequences for these systems is a difficult task. This task is further complicated by the following factors: (i) conflicting requirements on autocorrelation and crosscorrelation characteristics; (ii) the associated cost functions might be irregular and may have several local minima. Traditional or gradient based optimization methods may face challenges or are unsuitable to solve such a complex problem. In this paper, we pose simultaneous optimization of autocorrelation and crosscorrelation characteristics of Oppermann sequences as a multiobjective problem. We compare the performance of prominent state-of-the-art multiobjective evolutionary meta-heuristic algorithms to design Oppermann sequences for integrated radar and communication systems. |
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| AbstractList | In real-world engineering problems, several conflicting objective functions have often to be optimized simultaneously. Typically, the objective functions of these problems are too complex to solve using derivative-based optimization methods. Integration of navigation and radar functionality with communication applications is such a problem. Designing sequences for these systems is a difficult task. This task is further complicated by the following factors: (i) conflicting requirements on autocorrelation and crosscorrelation characteristics; (ii) the associated cost functions might be irregular and may have several local minima. Traditional or gradient based optimization methods may face challenges or are unsuitable to solve such a complex problem. In this paper, we pose simultaneous optimization of autocorrelation and crosscorrelation characteristics of Oppermann sequences as a multiobjective problem. We compare the performance of prominent state-of-the-art multiobjective evolutionary meta-heuristic algorithms to design Oppermann sequences for integrated radar and communication systems. |
| Author | Xin-She Yang Jamil, Momin Zepernick, Hans-Jurgen |
| Author_xml | – sequence: 1 givenname: Momin surname: Jamil fullname: Jamil, Momin email: momin.jamil@bth.se organization: Blekinge Inst. of Technol., Karlskrona, Sweden – sequence: 2 givenname: Hans-Jurgen surname: Zepernick fullname: Zepernick, Hans-Jurgen email: hans-jurgen.zepernick@bth.se organization: Blekinge Inst. of Technol., Karlskrona, Sweden – sequence: 3 surname: Xin-She Yang fullname: Xin-She Yang email: x.yang@mdx.ac.uk organization: Sch. of Sci. & Technol., Middlesex Univ., London, UK |
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| Snippet | In real-world engineering problems, several conflicting objective functions have often to be optimized simultaneously. Typically, the objective functions of... |
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| SubjectTerms | Algorithm design and analysis Communication systems Correlation Linear programming Measurement Optimization Radar |
| Title | Sequence optimization for integrated radar and communication systems using meta-heuristic multiobjective methods |
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