Synthesis of satellite footprint patterns from rectangular planar array antenna by using swarm-based optimization algorithms

• Published in: International journal of satellite communications and networking Vol. 32; no. 1; pp. 25 - 47
• Main Authors: Chatterjee, Anirban, Mahanti, Gautam Kumar, Ghatak, Gourab
• Format: Journal Article
• Language: English
• Published: Chichester Blackwell Publishing Ltd 01.01.2014; Wiley Subscription Services, Inc
• Subjects: Algorithms; Artificial Bee Colony (ABC) algorithm; Firefly Algorithm (FA); footprint patterns; Genetic Algorithm (GA); peak side lobe level (peak SLL); rectangular planar array; ripple
• ISSN: 1542-0973; 1542-0981
• DOI: 10.1002/sat.1055

SUMMARY A pattern synthesis method based on Firefly Algorithm (FA) and Artificial Bee Colony (ABC) optimization has been presented to generate satellite footprint patterns from a rectangular planar array of isotropic antennas by modifying the amplitude, phase, and the state of the array elements. Three cases comprising three different footprints of rectangular, square, and circular boundary are generated from the same array by using two different swarm‐based optimization algorithms FA and ABC. Both the algorithms, following the proposed procedures are able to generate the three different footprint patterns while maintaining a satisfactory lower peak side lobe level and ripple. A comparative analysis has been carried out between FA, ABC, and Genetic Algorithm (GA) for the presented problem in terms of fitness value for the three different cases. The superiority of FA and ABC over GA has been established in terms of finding better solutions for all the three cases of the proposed problem. Copyright © 2013 John Wiley & Sons, Ltd. Synthesis of satellite footprint patterns from a rectangular array is presented. The proposed method incorporates modification of element excitations and their corresponding states (‘on’/‘off’) for generating three different footprints of rectangular, square, and circular boundary. Firefly Algorithm (FA) and Artificial Bee Colony (ABC) optimization are applied individually for determining optimum elements excitations and there corresponding states. The performance of FA and ABC are compared with Genetic Algorithm (GA) by generating the same footprints by using GA following the presented method.