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Journal Article
High-Resolution Multi-Target Angle Estimation for IoT-Enabled Drone Detection Using Adaptive Monopulse Beamforming
- Authors
- Huijea Park, Jiwon Kim, Jaehyun Park, Jae Cheol Park, Jungick Moon
- Issue Date
- 2026-01
- Citation
- IEEE Access, v.14, pp.2607-2618
- ISSN
- 2169-3536
- Publisher
- IEEE
- Language
- English
- Type
- Journal Article
- Abstract
- This paper presents an iterative monopulse beamforming method for accurate angle-of-arrival (AoA) estimation of multiple closely spaced targets in IoT-enabled frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar systems, with a specific focus on drone detection and tracking applications. As the Internet of Things (IoT) continues to expand, the need for reliable and efficient drone detection systems has become increasingly critical for security, surveillance, and airspace management. Unlike conventional monopulse beamforming that suffers from limited resolution and interference in multi-target environments, the proposed method formulates a constrained convex optimization problem to design sum and difference beams with precise control over mainlobe gain, sidelobe suppression, and monopulse slope. To further improve robustness against closely spaced targets, we introduce a nulling method followed by an iterative refinement algorithm that alternately updates beamformers while incorporating interference suppression constraints. Additionally, we develop a low-complexity iterative nulling algorithm based on orthogonal projection with slope and bias calibration to reduce computational complexity, making it suitable for resource-constrained IoT devices. Both computer simulations and laboratory experiments using commercial radar modules demonstrate that our proposed methods significantly outperform conventional monopulse techniques, particularly when targets are closely spaced. The iterative nulling approach achieves up to 70% reduction in mean square error (MSE) compared to conventional methods across various signal-to-noise ratio (SNR) levels, making it highly suitable for IoT-based drone detection and tracking applications.
- Keyword
- Angle-of-Arrival(AoA) estimation, Drone detection and tracking, FMCW MIMO radar, IoT-enabled radar systems, Iterative monopulse beamforming
- KSP Keywords
- Adaptive monopulse, Angle Estimation, Angle-of-arrival (AoA) estimation, Bias calibration, Computational complexity, Computer simulation(MC and MD), Constrained convex optimization, Conventional methods, Detection Systems(IDS), Detection and tracking, Drone detection
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
