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

Abstract

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).

Development of a Non-Iterative Macromodeling Technique by Data Integration and Least Square Method

“…Second, solution (6) will be successful, whereas there is a linear relationship between variable x and the desired transfer function H(x). However, in practical applications, this condition is not provided [25,26]. Furthermore, this method suffers from local control over the unshaped area of the desired data [27].…”

“…The computational complexity of Equation ( 11) is dependent on the value of M. The Nyquist theorem can be helpful to determine the sampling rate Δ. For an arbitrary available data, the following equation can be used to the first approximation of M [26].…”