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The paper explores the potential to enhance aviation safety, particularly in militarized regions, by outfitting aircraft with Side Looking Airborne Radar (SLAR) and employing space-time adaptive processing (STAP) algorithms. The research objective revolves around implementing a model of side-looking airborne radar and the corresponding STAP algorithms. This technology enables the detection of slow-moving targets amidst strong interference, encompassing both passive (clutter) and active (jammer) elements. Slow-moving targets relative to the aircraft's speed include tanks, combat vehicles, command vehicles, artillery, and logistical assets of enemy forces. The theoretical framework of space-time adaptive processing is presented, elucidating the sequential steps of the classical Sample Matrix Inversion Space-Time Adaptive Processing (SMI STAP) algorithm. The paper underscores the significance of characteristic parameters delineating a linear STAP processor. The proposed solution facilitates the detection of enemy combat measures and enhances aviation safety. It outlines a radar model installed beneath the aircraft's fuselage and elucidates algorithms for space-time adaptive processing of radar signals. The simulations conducted within the article were executed using the MATLAB environment. The simulation results indeed suggest that the proposed solution holds promise for deployment in equipping aircraft of one's own military and those engaged in operations within conflict zones. This paper stands as one of the few contributions in the literature addressing the augmentation of aircraft safety through radar and space-time adaptive processing.
Rocznik
Tom
Strony
335--346
Opis fizyczny
Bibliogr. 19 poz.
Twórcy
autor
- Faculty of Aviation Division, Polish Air Force University, Dywizjonu 303 no 35 Street, 08-520 Dęblin, Poland
autor
- Institute of Navigation, Polish Air Force University, Dywizjonu 303 no 35 Street, 08-520 Dęblin, Poland
autor
- Faculty of Mechatronics, Armament and Aerospace, Military University of Technology, gen. Sylwestra Kaliskiego 2 Street, 00 -908 Warszawa, Poland
Bibliografia
- 1. Klemm Richard. 1998. Space-time Adaptive Processing: Principles and Applications. The Institution of Electrical Engineers. ISBN: 978-0-8529-6946-5.
- 2. Reed Irving, John Mallett, Lawrence Brennan. 1974. „Rapid convergence rate in adaptive arrays”. IEEE Trans. Aerosp. Electron. Syst. 10(6): 853-863. DOI: 10.1109/TAES.1974.307893.
- 3. Ward James. 1994. Space-Time Adaptive Processing for Airborne Radar. Lincoln Laboratory Technical Report 1015.
- 4. Guerci Joseph. 2014. Space-Time Adaptive Processing for Radar. Artech House. ISBN: 978-1-6080-7820-2.
- 5. Sarkar Tapan, Srikanth Nagraja, Michael Wicks. 1998. „A deterministic direct data domain approach to signal estimation utilizing non uniform and uniform 2D arrays”. Dig. Sig. Proc. 8: 114-125.
- 6. Carlo Jeffrey, Tapan Sarkar, Michael Wicks. 2003. „A Least Squares Multiple Constraint Direct Data Domain Approach for STAP”. In: 2003 IEEE Radar Conference: 431-438. 5-8 May 2003, Huntsville, AL, United States.
- 7. Melvin Wiliam, Gregory Showman. 2006. „An approach to knowledge-aided covariance estimation”. IEEE Transactions on Aerospace and Electronic Systems 42(3): 1021-1042. DOI: 10.1109/TAES.2006.248216.
- 8. Zhu Xumin, Peter Stoica. 2011. „Knowledge-aided space-time adaptive processing”. IEEE Trans. Aerosp. Electron. Syst. 47(2): 1325-1333. DOI: 10.1109/TAES.2011.5751261.
- 9. Peng Hao, Yuze Sun, Yang Xiaopeng. 2019. „Robust knowledge-aided sparse recovery STAP method for non-homogeneity clutter suppression”. The Journal of Engineering 20: 6373-6376. DOI: 10.1049/joe.2019.0273.
- 10. Chen Jie, Xiaoming Huo. 2006. „Theoretical results on sparse representations of multiple-measurement vectors”. IEEE Trans. on Signal Processing 54(12): 4634-4643. DOI: 10.1109/TSP.2006.881263.
- 11. Duan Keqing, Zetao Wang, Wenchong Xie. 2017. „Sparsity-based STAP algorithm with multiple measurement vectors via sparse Bayesian learning strategy for airborne radar”. IET Signal Processing 11(5): 544-553. DOI: https://doi.org/10.1049/iet-spr.2016.0183.
- 12. Zang Wei. 2019. „Reduced dimension STAP based on sparse recovery in heterogeneous clutter environments”. IEEE Trans. on Aerospace and Electronics Systems 56(1): 785-795. DOI: 10.1109/TAES.2019.2921141.
- 13. Cristallini Diego, Wolfram Bürger. 2012. „A robust direct data domain approach for STAP”. IEEE Trans. on Sig. Proc. 60(3):1283-1294. DOI: 10.1109/TSP.2011.2176335.
- 14. Cristallini Diego. 2012. „Exploiting robust direct data domain STAP for GMTI in very high resolution SAR”. In: 2012 IEEE Radar Conference: 0348-0353. 7-11 May 2012, Atlanta, GA, United States.
- 15. Guo Yiduo, Guisheng Liao, Weike Feng. 2017. „Sparse representation-based algorithm for airborne radar in beam-space post-Doppler reduced-dimension space-time adaptive processing”. IEEE Access 5: 5896-5903. DOI: 10.1109/ACCESS.2017.2689325.
- 16. Li Ming, Guohao Sun, Zishu He. 2019. „Direct Data Domain STAP Based on Atomic Norm Minimization”. In: 2019 IEEE Radar Conference: 1-6. 22-26 April 2019, Boston, Massachusetts, United States.
- 17. Ma Zeqiang, Yimin Liu, Huadong Meng. 2013. „Jointly sparse recovery of multiple snapshots in STAP”. In: 2013 IEEE Radar Conference. 1-4. 29 April - 3 May 2013, Ottawa, Ontario, Canada.
- 18. Satyabrata Sen. 2015. „Low-rank matrix decomposition and spatio-temporal sparse recovery for STAP radar”. IEEE Journal of Selected Topics in Signal Processing 9(8): 1510-1523. DOI: 10.1109/JSTSP.2015.2464187.
- 19. Knee Peter. 2012. Sparse representations for Radar with MATLAB. Examples. Morgan & Claypool. ISBN: 978-1-6270-5034-0.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-af817948-afa1-4733-bfe1-cdeb3e6dad89
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