Abstract
Monopulse antennas form an important methodology of realizing tracking radar and they are based on the simultaneous comparison of sum and differencesignals to compute the angle-error and to steer the antenna patterns in the direction of the target (i.e., the boresight direction). In this study, we consider the synthesis problem of difference patterns in monopulse antennas from the perspective of Multi-objective Optimization (MO). The synthesis problem is recast as a multi-objective optimization problem (for the first time, to the best of our knowledge), where the Maximum Side-Lobe Level (MSLL) and Beam Width (BW) of principal lobe are taken as the two objectives. The Optimal Pareto Fronts (OPF) are obtained for different number of elements and subarrays using one of the best-known evolutionary MO algorithms till date, called the Non-dominated Sorting Genetic Algorithm (NSGA-II). The quality of solutions obtained is compared with the help of Pareto fronts on the basis of the two objectives to investigate the dependence of the number of elements and the number of sub-arrays on the final solution. Then we find the best compromise solutions for 20 element array and compare the results with standard single objective algorithms such as the Differential Evolution (DE) that has been reported in literature so far for the synthesis problem.
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Pal, S., Basak, A., Das, S., Suganthan, P.N. (2010). Synthesis of Difference Patterns for Monopulse Antenna Arrays – An Evolutionary Multi-objective Optimization Approach. In: Deb, K., et al. Simulated Evolution and Learning. SEAL 2010. Lecture Notes in Computer Science, vol 6457. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17298-4_54
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DOI: https://doi.org/10.1007/978-3-642-17298-4_54
Publisher Name: Springer, Berlin, Heidelberg
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