Linear Array Thinning Exploiting Almost Difference Sets

Abstract

This paper describes a class of linear thinned arrays with predictable and well-behaved sidelobes. The element placement is based on almost difference sets and the array power pattern is forced to pass through N uniformly-spaced values that, although neither equal nor constant as for difference sets, are a-priori known from the knowledge of the aperture size, the number of active array elements K, and the features of the correlation function. Such a property allows one to predict the bounds of the confidence range of the peak sidelobe of the admissible arrays obtainable through simple shift operations on a binary sequence.The expected peak sidelobe performances turn out to be comparable with those from difference sets, even though obtainable in a wider set of array configurations, and improved in comparison with cut-and-try random-placements.

Synthesis of Thinned Array With Side Lobe Levels Reduction Using Improved Binary Invasive Weed Optimization

“…Therefore, many stochastic, probabilistic or evolutionary optimization approach, such as simulated annealing [5], genetic algorithm [1], immune algorithm [6], ant colony optimization [7], different evolutions [8], particle swarm optimization algorithm [9] were used and shown to be effective for the synthesis of thinned arrays. Some other approaches [10,11] have also been proposed.…”

Fully interleaved linear arrays with predictable sidelobes based on almost difference sets

“…ADSs are binary sequences characterized by a three-level autocorrelation [7]. They constitute a generalization of Difference Sets [8] and have been used to design thinned arrays with predictable sidelobes [9]. In order to exploit ADSs for the synthesis of interleaved arrangements, let us consider the following properties:…”

“…• ADS arrangements can be analytically (i.e., without any optimization) designed whatever the aperture size [9].…”

“…Let also notice that an extension or application of the P SL estimators obtained in [9] for ADSbased thinned arrays to interleaved distributions is not trivial. As a matter of fact, the bounds deduced in [9] refer to the best thinned array among those obtained by cyclically shifting a reference ADS sequence.…”

“…As a matter of fact, the bounds deduced in [9] refer to the best thinned array among those obtained by cyclically shifting a reference ADS sequence. However, such a configuration is not generally the best one when shared apertures are of interest, since the complementary array can exhibit an unsatisfactory P SL.…”