Design Method for a Wideband Non-Uniformly Spaced Linear Array Using the Modified Reinforcement Learning Algorithm

Abstract

In this paper, we present a design method for a wideband non-uniformly spaced linear array (NUSLA), with both symmetric and asymmetric geometries, using the modified reinforcement learning algorithm (MORELA). We designed a cost function that provided freedom to the beam pattern by setting limits only on the beam width (BW) and side-lobe level (SLL) in order to satisfy the desired BW and SLL in the wide band. We added the scan angle condition to the cost function to design the scanned beam pattern, as the ability to scan a beam in the desired direction is important in various applications. In order to prevent possible pointing angle errors for asymmetric NUSLA, we employed a penalty function to ensure the peak at the desired direction. Modified reinforcement learning algorithm (MORELA), which is a reinforcement learning-based algorithm used to determine a global optimum of the cost function, is applied to optimize the spacing and weights of the NUSLA by minimizing the proposed cost function. The performance of the proposed scheme was verified by comparing it with that of existing heuristic optimization algorithms via computer simulations.

Design Approach of a Wideband Non-Uniformly Spaced Linear Array Based on Reinforcement Learning to Suppress Multiple Interference Signals

“…참고문헌 [13]에 서는 배열 안테나의 수신 신호로부터 샘플 공분산 행렬 (sample covariance matrix)과 널을 생성하기 위한 taper matrix를 계산하여 새로운 공분산 행렬을 만든 다음 LMS(least mean squares) 알고리즘을 사용하여 모든 주파 수에서 널을 생성하기 위한 가중치를 최적화하는 시도가 있었다. 참고문헌 [13]의 성능을 개선한 참고문헌 [14]는 참고문헌 [13] [15], [16] . 참고문헌 [15]와 참고문헌 [16]…”