A pragmatic approach to adaptive antennas

TLDR

Adaptive antennas differ philosophically from conventional adaptive signal‑processing, and deterministic methods for stochastic, ergodic problems trace back to Wiener and Kolmogorov. The study proposes a pragmatic method for efficiently computing adaptive weights in phased‑array antennas and demonstrates its effectiveness through illustrative examples. The method bypasses the interference covariance matrix, redefines adaptive processing, and offers broader problem flexibility at the cost of slightly fewer degrees of freedom. Results show the approach achieves broader applicability while sacrificing a small number of degrees of freedom.

Abstract

This paper presents a novel approach for efficient computation of adaptive weights in phased-array antennas. The fundamental philosophical differences between adaptive antennas and adaptive signal-processing methodology are also delineated. This approach, unlike the conventional statistical techniques, eliminates the requirement for an interference covariance matrix, and represents a rethinking of the entire conventional approach to adaptive processing. This approach provides greater flexibility in solving a wider class of problems, at the expense of a slightly reduced number of degrees of freedom. It is important to note that the application of a deterministic approach to address stochastic problems with an ergodic structure can be seen in the works of Wiener (1949) and Kolmogorov (1939). This paper presents examples to illustrate the effectiveness and uniqueness of this new pragmatic approach.