Chebyshev-based array for beam steering and null positioning using modified ant lion optimization

Pradhan, Hrudananda; Mangaraj, Biswa Binayak; Behera, Santanu Kumar

doi:10.1017/s1759078721000295

Cited by 7 publications

(7 citation statements)

References 48 publications

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“…In this section, we discuss the performance of the proposed algorithm, which is determined through a numerical simulation and performance comparison with existing methods based on other heuristic optimization algorithms, such as FA [ 19 ], SSA [ 21 ], MALO [ 26 ] and QPSO [ 27 ].…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Although there have been several heuristic optimization studies applied for antenna array, the works are focused on narrowband NUSLA. For example, null generation in narrowband NUSLA by using various heuristic optimization algorithms such as bat algorithm (BA) [ 24 ], firework algorithm (FWA) [ 25 ], and modified ant lion optimization (MALO) [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

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

Kang

Kim

Park

et al. 2022

Sensors

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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.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Kang

Kim

Park

et al. 2022

Sensors

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“…하지만, 이러한 방 법은 재밍(jamming)과 방송 신호와 같이 강한 파워를 갖 는 간섭(interference) 신호가 수신되는 경우에 효과적이지 않다. 따라서, 간섭 신호의 영향을 효과적으로 감쇄하기 위해 간섭 신호가 들어오는 방향에 널(null)을 생성하는 여러 연구가 이루어졌다 [8]∼ [11] .…”

Section: ⅰ 서 론unclassified

“…본 논문에서는 시뮬레이션을 통해 제안 방법의 성능을 검증하기 위해서 제안된 MORELA의 성능과 기존의 최적 화 알고리즘인 MALO(modified ant lion optimization) 알고 리즘 [11] 과 QPSO(quantum particle swarm optimization) 알고 리즘 [15]…”

Section: ⅴ 시뮬레이션 결과unclassified

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

Kang¹,

Kim²,

Park³

et al. 2022

J. Korean Inst. Electromagn. Eng. Sci.

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In this paper, we present a novel design approach for a wideband non-uniformly spaced linear array (NUSLA) to suppress the effect of interference signals. Notably, a uniform linear array (ULA), which is easy to handle, is widely utilized to generate nulls over a wide band; however, its beamforming performance is limited owing to the ULA structure. Although a NUSLA with nonlinear spacing addresses nonlinear problems that are difficult to handle mathematically, it can exhibit performance improvements surpassing those of ULA structures. However, an additional constraint is required to generate nulls at a specific position, and this has not yet been studied for null generation using wideband NUSLA. In this paper, we propose a novel cost function for designing a wideband NUSLA, which generates a null at the desired position, and we utilize the modified reinforcement learning algorithm (MORELA), which is a heuristic optimization algorithm based on reinforcement learning (RL), to minimize the proposed cost function and analyze the optimized antenna array and weights. Further, we compare the performance of the proposed MORELA based on RL with that of existing heuristic optimization algorithms via computer simulations.

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“…Furthermore, they used the differential evolution (DE) method to optimize the switching duration to get a low sidelobe by rejecting the sideband radiation [5]. Bio-inspired optimization methods, the ant lion optimization (ALO) in [9]- [11] and grasshopper optimization algorithms (GOA) in [12]- [14], were implemented to minimize the sidelobe level in a symmetric linear array. They delivered better results compared to well-known optimization methods such as  ISSN: 2502-4752 Indonesian J Elec Eng & Comp Sci, Vol.…”

Section: Introductionmentioning

confidence: 99%

Optimized multibeam synthesis of time-modulated microstrip arrays

Alaydrus

Attamimi

Umaisaroh

2022

IJEECS

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The capability to form the main beam of antennas improves the energy efficiency of wireless systems and enhances radar systems' effectiveness in detecting and tracking objects. The time-modulated array can generate multibeam with arbitrary directions in different spectral parts. This work synthesizes several linear arrays to achieve particular main beams and additional restrictions. We modify the pulse durations and the switch-on instants of the switches in each antenna element in an optimization procedure. The parameters are varied through the simulated annealing by minimizing certain cost functions. We designed three linear arrays: model 1 consists of six elements and has two beams. Model 2 is the improved version of model 1 by rejecting the sidelobes of other spectral contributions at the main beam under consideration. Model 3 consists of sixteen elements and has three different beams. The analytical results were verified by modeling the arrays in the form of microstrip arrays in a numerical simulation with HFSS. We obtained very accurate verification. Distinctive devitations at the region ϑ<-60o and ϑ>60o originate from the radiation characteristics of microstrip antennas, which focus the energy upwards to the region around ϑ=0o.

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Chebyshev-based array for beam steering and null positioning using modified ant lion optimization

Cited by 7 publications

References 48 publications

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

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

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

Optimized multibeam synthesis of time-modulated microstrip arrays

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