Abstract
Herein derived are the lower and upper bounds for the number of linearly independent ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2Q</i> )th-order virtual steering vectors of an array of electromagnetic vector-sensors, with <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> being any positive integer over one. These bounds help determine the number of non-Gaussian signals whose directions-of-arrival (DOAs) can be uniquely identified from ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2Q</i> )th-order statistics data. The derived lower bounds increase with <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> , whereas the derived upper bounds often fall below the maximum number of virtual sensors achievable from ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2Q</i> )th-order statistics manipulation. These bounds are independent of the permutation of the ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2Q</i> )th-order statistics entries in the higher order cumulant matrix that has a similar algebraic structure of the classical covariance matrix used in the second-order subspace-based direction-finding algorithms.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call