Auto-Calibration of Antenna Array with Quantum Annealing

초록

This paper proposes a quantum annealing (QA)-based auto-calibration method that efficiently estimates calibration parameters of array antennas. Gain and phase errors in individual array elements hinder the accuracy of direction of arrival (DOA) estimation. In large-scale antenna arrays, such as AESA and space surveillance radar systems, auto-calibration is time-prohibitive due to the need to optimize an extremely large number of calibration parameters. The core approach of this paper is to transform the auto-calibration task into a quadratic unconstrained binary optimization (QUBO) model that can be solved by quantum annealing. This is achieved by first encoding the gain and phase errors into a binary format. A Factorization Machine (FM) then learns the relationship between these binary errors and their resulting performance cost, mapping out the problem's cost surface. The resulting QUBO can be directly solved using the D-Wave quantum annealing hardware for fast and effective optimization. Simulation results of auto-calibration based on MUSIC algorithm showed improvement in DOA accuracy by 0.01 to 0.3 degrees and sidelobe suppression by 1 to 17 dB. To validate the effectiveness of the proposed method, real-world radar measurements were performed, and its calibration performance was evaluated. In addition, applicability to other DOA estimation methods such as Capon was investigated. The proposed approach significantly reduces calibration time while maintaining high estimation performance, demonstrating its potential for practical deployment in large-scale radar systems.

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