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Sequence optimization for integrated radar and communication systems using meta-heuristic multiobjective methods
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
Middlesex University, GBR.
2017 (engelsk)Inngår i: 2017 IEEE Radar Conference, RadarConf 2017, Institute of Electrical and Electronics Engineers Inc. , 2017, s. 0502-0507Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

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. © 2017 IEEE.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers Inc. , 2017. s. 0502-0507
Serie
IEEE Radar Conference, ISSN 1097-5764
Emneord [en]
Autocorrelation, Cost functions, Evolutionary algorithms, Heuristic algorithms, Multiobjective optimization, Optimization, Radar, Communication application, Conflicting objectives, Gradient-based optimization method, Meta heuristic algorithm, Multi-objective evolutionary, Multi-objective problem, Multiobjective method, Simultaneous optimization, Heuristic methods
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-15010DOI: 10.1109/RADAR.2017.7944255ISI: 000405307600096Scopus ID: 2-s2.0-85021424787ISBN: 9781467388238 (tryckt)OAI: oai:DiVA.org:bth-15010DiVA, id: diva2:1135423
Konferanse
2017 IEEE Radar Conference, RadarConf, Seattle
Tilgjengelig fra: 2017-08-23 Laget: 2017-08-23 Sist oppdatert: 2017-08-24bibliografisk kontrollert

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