Pattern synthesis for arbitrary arrays using an adaptive array method

Abstract

This paper presents a new pattern synthesis algorithm for arbitrary arrays based on adaptive array theory. With this algorithm, the designer can efficiently control both mainlobe shaping and sidelobe levels. The element weights optimize a weighted L 2 norm between desired and achieved patterns. The values of the weighting function in the L 2 norm, interpreted as imaginary jammers as in Olen and Compton's method, are iterated to minimize exceedance of the desired sidelobe levels and minimize the absolute difference between desired and achieved mainlobe patterns. The sidelobe control can be achieved by iteration only on sidelobe peaks. In comparison to Olen and Compton's method, the new algorithm provides a great improvement in mainlobe shaping control. Example simulations, including both nonuniform linear and planar arrays, are shown to illustrate the effectiveness of this algorithm.

GA based synthesis of satellite-borne multi-beam planar array with arbitrary geometry

“…It is observed that the mainlobe satisfy the requirement, and sidelobe level suppression reaches 30dB which is outstanding compared with the results of Refs. [7,8]. If the dynamic range ratio |I max /I min | is too large, the excitation will not easy to realize.…”

“…(3) Choose couples (mating); (4) Breed them together (crossover); (5) Evaluate each individual; (6) Selection; (7) Mutation; (8) If the pool has converged, or a number of pre-determined cycles have been completed, finish the cycle. If not, return to (3).…”

Convex Optimization and Array Orientation Diversity-Based Sparse Array Beampattern Synthesis

“…In order to solve Eqs. (7) and 8, we can assume that all the antenna elements are equally spaced from Àd s to d s with a small inter-element spacing Δd. Although it is supposed that there is one element at each position, not each antenna element is necessarily radiating waves or excited with current.…”

“…For APS problem, which can also be formulated as a quadratic programming problem [4,5], the objective function is to minimize the squared errors between the synthesized pattern and the desired pattern. Besides, additional linear constraints [4] or weighting functions [7] are also added to the quadratic objective function to minimize the peaks of the synthesis error. The challenge to weighting functions in the quadratic programming is that it has to be adjusted in an ad hoc manner.…”

An optimal design of a cylindrical polarimetric phased array radar for weather sensing

“…On the other hand, the common mathematical and computational simplifications of planar array cannot be applied directly to conformal arrays. Direct array pattern synthesis techniques including Fourier methods [Taylor, 1952;Josefsson and Persson, 2006], aperture projection methods [Chiba et al, 1989;Schuman, 1994], adaptive array methods [Zhou and Ingram, 1999;Sureau and Keeping, 1982], alternative projection methods [Steyskal, 2002;Vescovo, 1995] and least mean square methods [Vaskelainen, 1997;Dinnichert, 2000] have been used for pattern synthesis of conformal array antennas. Although these techniques are fast, the effect of coupling between elements is not usually taken into account.…”