Calibration of active phased array antennas in constrained scenarios
Muhammad Muhammad Rizqi (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Y. Aslan – Graduation committee member (Microwave Sensing, Signals & Systems)
Alexander Yarovoy – Graduation committee member (Microwave Sensing, Signals & Systems)
Guilherme Theis – Mentor (Robin Radar Systems B.V.)
M. Spirito – Graduation committee member (TU Delft - Electrical Engineering, Mathematics and Computer Science)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
The literature on calibration of active phased array antennas (APAAs) lacks a consistent, bias-free framework for evaluating and comparing calibration techniques. Existing approaches typically validate a proposed technique against another calibration method rather than against the ideal array response. To address this, a new set of pattern-derived figures-of-merit is proposed, using the ideal array factor (AF) pattern as ground truth. These metrics, including pattern root mean square error (RMSE), steering angle error, and maximum side lobe level (SLL), provide a direct measure of calibration performance in terms of beamforming quality.
The framework is applied to calibration in constrained scenarios, where full anechoic control of the measurement environment is unavailable, as can occur when APAAs are calibrated after deployment. Three constraints are investigated: finite calibration-angle sampling, reduced measurement signal-to-noise ratio (SNR), and multipath. Three phaseless calibration techniques: rotating electric-field vector (REV), singlechannel sequential null steering (SCSNS), and single-channel sequential lobe steering (SCSLS), are evaluated using the CN0566 ADALM-PHASER phased array platform.
Finite calibration-angle sampling produces consistent degradation across all techniques: every 10° increase in the difference between calibration and steering angle increases steering angle error by at least 0.3° and pattern RMSE by approximately 1.4%, with REV and SCSNS outperforming SCSLS. Below technique-dependent SNR thresholds, calibration fails, while further SNR provides little benefit; the thresholds are −13.45 dB (SCSNS), −10.42 dB (SCSLS), and −8.87 dB REV).
For echoic environments, two novel echo-aware calibration methods are proposed: frequency-domain averaging (FDA), which isolates the line-of-sight component through solely frequency diversity, and geometryinformed echo de-embedding (GIED), which uses ray-tracing to model and compensate for multipath. Both substantially outperform conventional calibration, which exhibits 2.088 dB and 9.85° magnitude/phase calibration error standard deviation under multipath. With fully known scatterer geometry, GIED (0.363 dB, 3.19°) outperforms the FDA (0.989 dB, 3.36°). However, FDA demonstrates more robustness to incomplete geometry information.
This work provides the first ground-truth-based framework for comparing APAA calibration techniques under constrained scenarios and the first demonstration of active multipath compensation for calibration in echoic environments.