Abstract
Target detection is one of the important subfields in the research of synthetic aperture radar (SAR). It faces many challenges, due to the stationary objects, leading to the presence of a scatter signal. Many researchers have been done on target detection, and most of them prefer filter based techniques. In this work, the moving target detection in SAR using decision fusion method is proposed. The newly developed scheme is named Bayesian fusion for moving target detection (BF-MTD) as the scheme utilizes the Bayesian model for identifying the target location. Initially, the received signals from the SAR are fed through the short-time Fourier transform (STFT) and the matching filters for identifying the target location. Then, the results are fused together by the Bayesian fusion strategy for finding the actual target. For the fusion, the Naive Bayes classifier is used for determining the optimal parameter for the target detection. The simulation of the proposed BF-MTD model is evaluated by varying target, iteration; pulse repetition level and antenna turn velocity of the SAR. Simulation results reveal that the proposed BF-MTD has achieved significant performance for a detection time, missed target rate, and mean square error, respectively.
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References
Cerutti-Maori D, Sikaneta I (2012) A generalization of DPCA processing for multichannel SAR/GMTI radars. IEEE Trans Geosci Remote Sens 51(1):560–572
Wang H, Chen Z, Zheng S (2017) Preliminary research of low-RCS moving target detection based on Ka-band video SAR. IEEE Geosci Remote Sens Lett 14(6):811–815
Ghuge CA, Ruikar SD, Chandra Prakash V (2018) Support vector regression and extended nearest neighbor for video object retrieval. Evol Intell. https://doi.org/10.1007/s12065-018-0176-y
Daga BS, Ghatol AA (2016) Detection of objects and activities in videos using spatial relations and ontology based approach in video database system. Int J Adv Eng Technol 9(6):640–650
Henke D, MendezDominguez E, Small D, Schaepman ME, Meier E (2018) Moving target tracking in SAR data using combined exo- and endo-clutter processing. IEEE Trans Geosci Remote Sens 56(1):251–263
Zink M, Bachmann M, Bräutigam B, Fritz T, Hajnsek I, Krieger G, Moreira A, Wessel B (2014) TanDEM-X: the new global DEM takes shape. IEEE Geosci Remote Sens Mag 2(2):8–23
Xu H, Yang Z, Tian M, Sun Y, Liao G (2017) An extended moving target detection approach for high-resolution multichannel SAR-GMTI systems based on enhanced shadow-aided decision. IEEE Trans Geosci Remote Sens 56(2):715–729
Fan X, Cheng Y, Fu Q (2015) Moving target detection algorithm based on Susan edge detection and frame difference. In: Proceedings of 2nd international conference on information science and control engineering, April 2015
Srinivas V, Santhirani C (2020) Hybrid particle swarm optimization-deep neural network model for speaker recognition. Multimed Res 3(1):1–10
Kang Z, Guo Y, Chen G (2010) Moving target detection based on particle swarm optimization. In: Proceeding of the 2nd international conference on information science and engineering, Hangzhou, China, December 2010
Yao W, Shan W (2010) A novel algorithm of coherent integration for moving target detection. In: Proceedings of 2nd international conference on advanced computer Control, March 2010
Chen X, Chen B, Guan J, Huang Y, He Y (2018) Space-range-doppler focus-based low-observable moving target detection using frequency diverse array MIMO radar. IEEE Access 6:43892–43904
Sjögren T, Vu V (2015) Detection of slow and fast moving targets usinghybrid CD-DMTF SAR GMTI mode. In: Proceedings of IEEE 5th Asia-Pacific conference on synthetic aperture radar (APSAR), September 2015
Chen X, Guan J, Liu N, He Y (2014) Maneuvering target detection via radon-fractional fourier transform-based long-time coherent integration. IEEE Trans Signal Process 62(4):939–953
Xu J, Yu J, Peng YN, Xia XG (2011) Radon-Fourier transform for radar target detection, I: generalized doppler filter bank. IEEE Trans Aerosp Electron Syst 47(2):1186–1202
Yu W, Su W, Gu H (2018) Ground maneuvering target detection based on discrete polynomial-phase transform and Lv’s distribution. Sig Process 144:364–372
Li Z, Santi F, Pastina D, Lombardo P (2016) Multi-frame fractional Fourier transform technique for moving target detection with space-based passive radar. IET Radar Sonar Navig 11(5):822–828
Fienup JR (2001) Detecting moving targets in SAR imagery by focusing. IEEE Trans Aerosp Electron Syst 37(3):794–809
Newey M, Benitz GR, Barrett DJ, Mishra S (2018) Detection and imaging of moving targets with LiMIT SAR data. IEEE Trans Geosci Remote Sens 56(6):3499–3510
Suwa K, Yamamoto K, Tsuchida M, Nakamura S, Wakayama T, Hara T (2017) Image-based target detection and radial velocity estimation methods for multichannel SAR-GMTI. IEEE Trans Geosci Remote Sens 55(3):1325–1338
Li J, Huang Y, Liao G, Xu J (2016) Moving target detection via efficient ATI-GoDec approach for multichannel SAR system. IEEE Geosci Remote Sens Lett 13(9):1320–1324
Xu H, Yang Z, Chen G, Liao G, Tian M (2016) A ground moving target detection approach based on shadow feature with multichannel high-resolution synthetic aperture radar. IEEE Geosci Remote Sens Lett 13(10):1572–1576
Xu L, Gianelli C, Li J (2016) Long-CPI multichannel SAR-based ground moving target indication. IEEE Trans Geosci Remote Sens 54(9):5159–5170
Jaya E, Krishna BT (2019) Moving target detection in multichannel SAR framework using adaptive neuro fuzzy decisive technique. Int J Recent Technol Eng 8(2):4517–4523
Taylor A, Oriot H, Savy L, Daout F, Forster P (2017) Moving targets detection capacities improvement in multichannel SAR framework. IEEE Trans Geosci Remote Sens 55(6):3248–3260
Tian J (2017) Radon-NUFrFT for random PRI radar. IEEE Trans Aerosp Electron Syst 53(4):2101–2109
Durak L, Arikan O (2003) Short-time Fourier transform: two fundamental properties and an optimal implementation. IEEE Trans Signal Process 51(5):1231–1242
Conte E, Lops M, Ricci G (1996) Adaptive matched filter detection in spherically invariant noise. IEEE Signal Process Lett 3(8):248–250
Zheng S (2014) Naïve Bayes classifier: a MapReduce approach. Graduate Faculty of the North Dakota State University of Agriculture and Applied Science
Yu G, Piao S, Han X (2017) Fractional Fourier transform-based detection and delay time estimation of moving target in strong reverberation environment. IET Radar Sonar Navig 11(9):1367–1372
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Bharat Kumar, M., Rajesh Kumar, P. Bayesian fusion strategy for moving target detection in multichannel SAR framework. Evol. Intel. 15, 1411–1424 (2022). https://doi.org/10.1007/s12065-020-00445-1
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DOI: https://doi.org/10.1007/s12065-020-00445-1