Design of Sparse MIMO Radar Antenna Arrays Using DPS with Integrated CRB Evaluation
Source: https://repository.tudelft.nl/record/uuid:36b375c9-d7ef-4e3a-92e4-c5ecda6db218 Parent: https://radar.tudelft.nl/Education/mscstudents.php
Design of Sparse MIMO Radar Antenna Arrays Using DPS with Integrated CRB Evaluation
Master Thesis (2025)
Author(s)
J. Li (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Contributor(s)
F. Fioranelli – Mentor (TU Delft - Microwave Sensing, Signals & Systems)
G. Joseph – Mentor (TU Delft - Signal Processing Systems)
Arie G.C. Koppelaar – Mentor (NXP Semiconductors)
Faculty
Electrical Engineering, Mathematics and Computer Science
Sparse antenna array Probabilistic subsampling CRB Distributed radar network
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Publication Year
2025
Language
English
Graduation Date
23-06-2025
Awarding Institution
Delft University of Technology
Programme
['Electrical Engineering | Microelectronics']
Faculty
Electrical Engineering, Mathematics and Computer Science
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Abstract
This thesis presents a novel approach based on Joint Deep Probabilistic Subsampling with Cram´er-Rao Lower Bound Integration (J-DPSC) for sparse antenna array design in distributed radar systems. The method addresses the critical challenge of achieving high angular resolution in autonomous driving applications while maintaining low hardware complexity. The proposed J-DPSC framework extends Deep Probabilistic Subsampling with theoretical foundations based on worst-case dual-target Cram´er-Rao Lower Bound analysis. This enables simultaneous optimization of both transmitter and receiver arrays across mono-static, bi-static, and joint operational modes. A Neighborhood Masking Mechanism ensures physical realizability of the designed arrays. Extensive simulations validate that our proposed sparse arrays achieve performance comparable to fully populated arrays while using significantly fewer elements. The results demonstrate peak sidelobe levels up to −10 dB with minimal resolution sacrifice (≤ 0.1 degree) for two-sensor distributed radar networks. The approach allows extension to three-sensor configurations for potential applications with multi-node networks. Monte Carlo evaluations across varying SNR levels and source (i.e., targets) separations show consistent superiority over random selection methods.\ \ Summarizing, this work advances sparse array design methodology by bridging machine learning techniques with estimation theory, enabling a more generalized and flexible design tool for distributed radar networks in next-generation autonomous driving applications.
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