Super Nested Arrays: Linear Sparse Arrays with Reduced Mutual Coupling – Part I: Fundamentals - 2016 PROJECT TITLE: Super Nested Arrays: Linear Sparse Arrays with Reduced Mutual Coupling – Part I: Fundamentals - 2016 ABSTRACT: In array processing, mutual coupling between sensors has an adverse effect on the estimation of parameters (e.g., DOA). Whereas there are strategies to counteract this through applicable modeling and calibration, they are usually computationally expensive, and sensitive to model mismatch. On the opposite hand, sparse arrays, like nested arrays, coprime arrays, and minimum redundancy arrays (MRAs), have reduced mutual coupling compared to uniform linear arrays (ULAs). With N denoting the number of sensors, these sparse arrays provide O(N2) freedoms for supply estimation as a result of their difference coarrays have O(N2)-long ULA segments. However these well-known sparse arrays have disadvantages: MRAs do not have easy closed-type expressions for the array geometry; coprime arrays have holes in the coarray; and nested arrays contain a dense ULA in the physical array, ensuing in significantly higher mutual coupling than coprime arrays and MRAs. This paper introduces a brand new array referred to as the super nested array, which has all the nice properties of the nested array, and at the identical time achieves reduced mutual coupling. There is a systematic procedure to work out sensor locations. For fastened N, the super nested array has the same physical aperture, and the identical hole-free coarray as will the nested array. However the number of sensor pairs with small separations (?/two,a pair of×?/two, etc.) is considerably reduced. Several theoretical properties are proved and simulations are included to demonstrate the superior performance of those arrays. In the companion paper, a more extension known as the Qth-order super nested array is developed, which any reduces mutual coupling. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Robust Convex Approximation Methods for TDOA-Based Localization under NLOS Conditions - 2016 Generalized Correntropy for Robust Adaptive Filtering - 2016
