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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Veröffentlicht in:Proceedings of the IEEE National Radar Conference (1996) S. 0502 - 0507
Hauptverfasser: Jamil, Momin, Zepernick, Hans-Jurgen, Xin-She Yang
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.05.2017
Schlagworte:
Algorithm design and analysis
Communication systems
Correlation
Linear programming
Measurement
Optimization
Radar
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.
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
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  surname: Jamil
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  givenname: Hans-Jurgen
  surname: Zepernick
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  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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StartPage 0502
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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