Side Lobe Level Reduction of Any Type of Linear Equally Spaced Array Using the Method of Convolution

Alijani, Mohammad G. H.; Neshati, Mohammad Hassan; Boozari, Mahdi

doi:10.2528/pierl16121608

Cited by 19 publications

(13 citation statements)

References 3 publications

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“…Based on Nyquist sampling theorem, the given condition in Equation (7) have to be satisfied, 14,16 in which d max is maximum value of d i , i = 1, 2, …, M and d max is chosen by designer.…”

Section: Problem Formulationmentioning

confidence: 99%

“…The other advantages of UESLA compared to ESLA are low cost and simple feeding structure of array elements. [7][8][9] Finding the locations of the elements in UESLAs is a significant task in AF syenthesis and variety of methods has been reported in literature, which are categorized into two different techniques. The first type is based on evolutionary algorithms including Genetic Algorithm, Particle Swarm Optimization, Dynamic Programming, and Differential Evolution Algorithm, which are used to determine the optimum position of array elements.…”

Section: Introductionmentioning

confidence: 99%

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A new noniterative method for pattern synthesis of unequally spaced linear arrays

Alijani

Neshati

2019

Int J RF Microw Comput Aided Eng

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In this article, a new noniterative beam shaping method is introduced to synthesise array factor (AF) of an unequally spaced linear array (UESLA). The proposed method is based on eigenvector decomposition of sampled data matrix of a given pattern. Using matrix analysis, the eigenvalues and their corresponding eigenvectors of the sampled matrix are determined. It is shown that the eigenvalues and eigenvectors of the sampled matrix are related to the locations and complex excitation coefficients of the array elements. According to the concept of generalized eigenvalue concept, the solution of locations and excitation coefficients is derived using least square method. In order to reduce the number of array elements, singular value decomposition is applied to obtain a low ranked matrix using rejection of nonzero eigenvalues. Using the approximated sampled matrix, the excitation and locations of the optimized array elements are calculated. A few comprehensive examples are investigated to verify the accuracy of the proposed method and the obtained results are compared with those of an equally spaced linear array (ESLA).It is shown that the total number of array elements in an UESLA is less than that of ESLA, which is the most advantage of the introduced method in AF synthesis. K E Y W O R D Santenna array, array factor synthesis, unequally spaced linear array

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Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new noniterative method for pattern synthesis of unequally spaced linear arrays

Alijani

Neshati

2019

Int J RF Microw Comput Aided Eng

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“…Nowadays, in spite of long research on this subject, still, it is an interesting challenge for researchers and a variety of algorithms including stochastic and analytical methods has been reported in the literature [5,6]. A common type of antenna array pattern synthesis method is producing a pattern, which holds the main beam toward the required direction for a specified value of beam width, while Side Lobe Level (SLL) is kept small to prevent interference with other radiating sources [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Development a New Technique Based on Least Square Method to Synthesize the Pattern of Equally Space Linear Arrays

Alijani

Neshati

2019

IJE

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Using the sampled data of a desired pattern is a common technique in pattern synthesizing of array factor (AF) of antenna arrays. Based on the obtained data matrix, Least Square Method (LSM) is used to calculate the exciting coefficient of array elements. The most important parameter, which involves the accuracy and complexity of calculation, is the sampling rate of the desired pattern. Classical Least Square Method (CLSM) uses a linear combination of the samples, which provides low accuracy. In this paper, a new method is proposed by introducing a correction factor (CF) to increase the accuracy of the pattern estimation, while the design complexity is not increased basically. A normalized error between the desired and estimated pattern is considered and its variation versus CF is investigated. It is shown that for an optimum value of correction factor, CFopt, the defined error is minimum. The proposed method is examined for a few well-known arrays and the obtained results are reported and compared with those of classical LSM. It is shown that the introduced method accurately estimates the required pattern of array factors of equally spaced linear arrays (EALAs).

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“…Therefore, to achieve the higher convergence speed and low steady state error a judicious strategy is required to modify the LMS algorithm. Numerous methods have been proposed by researches to overcome the drawbacks of LMS algorithm. Some of the important features of these algorithms are outlined here.…”

Section: Introductionmentioning

confidence: 99%

Design of digital signal processor based adaptive beamformer using hybrid residual least mean square algorithm for improved performance

Duppala

Misra

Sanyal

2019

Int J RF Microw Comput Aided Eng

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This paper proposes a novel hybrid residual least mean square (HRLMS) algorithm for adaptive filtering followed by an antenna beamformer using 16-element linear array. The hybridization process involves a switching between the residual-LMS (RLMS) and the conventional-LMS (CLMS) algorithms after the eighth iteration, if the square errors for four consecutive iterations are less than a threshold. The novelty of HRLMS lies in estimating best step size factor through residuals for speedy convergence followed by the CLMS switching for minimum steady state error (SSE). The novelty also includes in realizing a real-time antenna beamformer with significant sidelobe level (SLL) reduction and improved interference nulling by integration of HRLMS and space selective digital filter (SSDF). The adaptive filter and smart beamformer, based on HRLMS and HRLMS-SSDF have been implemented on TMS320VC5416 digital signal processor. The comparative performance evaluation of HRLMS has been done for convergence speed, SSE, interference nulling and SLL reduction with the existing variable step size LMS (VSSLMS) algorithms. The iteration count for convergence has been reduced by about 50% with paltry additional computational burden over the other VSSLMS algorithms. The HRLMS-SSDF provide attenuations of about 76, 33, and >50 dB, respectively for interfering signals, first SLL and higher order SLLs of beamformer. K E Y W O R D Sadaptive antenna beamformer, convergence speed, HRLMS algorithm, interference nulling, residuals, SLL

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Side Lobe Level Reduction of Any Type of Linear Equally Spaced Array Using the Method of Convolution

Cited by 19 publications

References 3 publications

A new noniterative method for pattern synthesis of unequally spaced linear arrays

A new noniterative method for pattern synthesis of unequally spaced linear arrays

Development a New Technique Based on Least Square Method to Synthesize the Pattern of Equally Space Linear Arrays

Design of digital signal processor based adaptive beamformer using hybrid residual least mean square algorithm for improved performance

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