Worst-case analysis of array beampatterns using interval arithmetic

TL;DR

This paper extends interval arithmetic to perform worst-case analysis of array beampatterns, introducing backtracking to identify error realizations causing bounds, applicable to diverse array geometries and mutual coupling effects.

Contribution

It introduces backtracking in interval arithmetic for array beampattern analysis, enabling identification of error sources for worst-case bounds and broadening applicability to complex array configurations.

Findings

Backtracking identifies error realizations leading to worst-case beampatterns.

A simple formula estimates bounds for uniform errors, verified numerically.

Array size and apodization have limited impact on peak sidelobe level.

Abstract

Over the past decade, interval arithmetic (IA) has been utilized to determine tolerance bounds of phased array beampatterns. IA only requires that the errors of the array elements are bounded, and can provide reliable beampattern bounds even when a statistical model is missing. However, previous research has not explored the use of IA to find the error realizations responsible for achieving specific bounds. In this study, the capabilities of IA are extended by introducing the concept of ``backtracking'', which provides a direct way of addressing how specific bounds can be attained. Backtracking allows for the recovery of both the specific error realization and the corresponding beampattern, enabling the study and verification of which errors result in the worst-case array performance in terms of the peak sidelobe level. Moreover, IA is made applicable to a wider range of arrays by adding support for arbitrary array geometries with directive elements and mutual coupling, in addition to element amplitude, phase, and positioning errors. Lastly, a simple formula for approximate bounds of uniformly bounded errors is derived and numerically verified. This formula gives insights into how array size and apodization cannot reduce the worst-case peak sidelobe level beyond a certain limit.