Synthesis of Difference Patterns for Monopulse Antennas With Optimal Combination of Array-Size and Number of Subarrays --- A Multi-Objective Optimization Approach

Abstract

Monopulse antennas form an important methodology of realizing tracking radar. They are based on the simultaneous comparison of sum and difference signals to compute the angleerror 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 of monopulse antennas in the framework of Multi-objective Optimization (MO). The synthesis problem is recast as an MO 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 to be minimized simultaneously. The approximated Pareto Fronts (PFs) are obtained for different number of elements and sub-arrays using a recently developed and very competitive Multi-Objective Evolutionary Algorithm (MOEA) called MOEA/D-DE that uses a decomposition approach for converting the problem of approximation of the PF into a number of single objective optimization problems. This algorithm employs Differential Evolution (DE), one of the most powerful real parameter optimizers in current use, as the search method. The quality of solutions obtained is compared with the help of the trade-off graphs (plots of the approximated PF) generated by MOEA/D-DE on the basis of the two objectives to investigate the dependence of the number

Linear Region Extension of MR Curve in ML Based Monopulse

“…ML(Maximum Likelihood) 기반의 모노 펄 스의 경우, 목표물로부터 송신된 신호의 확률밀도 함수를 최대로 만드는 방향 추정 값을 목표물의 방 향으로 추정하는 방식으로 높은 정확도를 산출하며, CRLB(Cramer-Rao Lower Bound)를 이용하여 모노 펄 스 estimator의 성능을 평가한다 [3] . 하지만 모노 펄스 추정의 본질적인 성능은 MR 커브의 특성에 의해 결 정되며, 이에 따른 모노 펄스 배열 안테나에서 다목 적함수를 만족시키는 합 빔과 차 빔 패턴의 합성을 이용하여 모노 펄스 추정 성능을 향상시키는 연구가 수행되고 있다 [4] . Nickel이 제안한 모노 펄스 방식은 합 빔의 가중치 벡터의 미분 값을 이용하여 차 빔 패 턴을 형성하는 방법이다.…”

Optimization of partially thinned subarrays based on two‐stage random search method for large phased array antenna

“…So far, many studies to optimize subarray structure focus on G/T and monopulse systems. [5][6][7][8][9] Also, optimization methods are introduced to minimize side lobes generated by grating lobes and to cancel interferences. [10][11][12] Some studies are multiobjective, 13,14 and deal with overlapped, tiled, and reconfigurable subarray structures.…”

“…In addition, the subarray structures can form the SLB channels by using some. So far, many studies to optimize subarray structure focus on G / T and monopulse systems 5–9 . Also, optimization methods are introduced to minimize side lobes generated by grating lobes and to cancel interferences 10–12 .…”