A Comparative Analysis of Greedy Sparse Signal Recovery Algorithms for Direction Finding in Electronic Support Measurement Systems
13th International Conference on Electrical and Electronics Engineering, ICEEE 2026, Antalya, Türkiye, 27 - 29 Nisan 2026, ss.272-278, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/iceee69936.2026.11598321
- Basıldığı Şehir: Antalya
- Basıldığı Ülke: Türkiye
- Sayfa Sayıları: ss.272-278
- Anahtar Kelimeler: Compressed Sensing, Direction Finding, Electronic Support, gOMP, OMP, POMP, Sparse Signal Processing
- Gazi Üniversitesi Adresli: Evet
Özet
Direction finding (DF) operations are critical for modern Electronic Support (ES) systems, particularly within complex electromagnetic environments characterized by multiple threats and low signal-to-noise ratios (SNR). This study leverages the spatial sparsity of signal sources to investigate the contributions of sparse estimation algorithms, rooted in compressed sensing principles, to DF performance in ES architectures. In this context, alongside the classical Orthogonal Matching Pursuit (OMP) algorithm, we integrated the Perturbed OMP (POMP)-designed for robustness against model uncertainties and off-grid issues-and the Generalized OMP (gOMP), aimed at optimizing computational overhead, into the ES system framework.The effectiveness of these proposed approaches is numerically validated through comprehensive Monte Carlo simulations using Gaussian-based antenna gain models and various circular antenna array configurations. Our findings demonstrate that the Perturbed OMP algorithm provides a more stable performance by significantly reducing Root Mean Square Error (RMSE) values compared to the classical OMP, especially in detecting off-grid targets and operating at low SNR levels. Conversely, the gOMP algorithm was found to reduce processing time by over 75% while maintaining direction-finding precision in multi-threat scenarios. These results confirm that sparse estimation-based approaches offer a robust and viable alternative to traditional methods for high-precision, real-time electronic warfare applications.