No Access

A Particle Fuzzy Decisive Framework for Moving Target Detection in the Multichannel SAR Framework

Eppili Jaya

ECE, JNTUK University, Kakinada, Andhra Pradesh 533003, India

Department of ECE, Aditya Institute of Technology and Management, Tekkali, K Kotturu, Andhra Pradesh 532201, India

E-mail Address: jaya.baratam@gmail.com

Corresponding author.

Search for more papers by this author

and

B. T. Krishna

Department of ECE, JNTUK University, Kakinada, Andhra Pradesh 533003, India

Search for more papers by this author

https://doi.org/10.1142/S1469026820500327Cited by:2 (Source: Crossref)

Abstract

Target detection is one of the important subfields in the research of Synthetic Aperture Radar (SAR). It faces several challenges, due to the stationary objects, leading to the presence of scatter signal. Many researchers have succeeded on target detection, and this work introduces an approach for moving target detection in SAR. The newly developed scheme named Adaptive Particle Fuzzy System for Moving Target Detection (APFS-MTD) as the scheme utilizes the particle swarm optimization (PSO), adaptive, and fuzzy linguistic rules in APFS for identifying the target location. Initially, the received signals from the SAR are fed through the Generalized Radon-Fourier Transform (GRFT), Fractional Fourier Transform (FrFT), and matched filter to calculate the correlation using Ambiguity Function (AF). Then, the location of target is identified in the search space and is forwarded to the proposed APFS. The proposed APFS is the modification of standard Adaptive genetic fuzzy system using PSO. The performance of the MTD based on APFS is evaluated based on detection time, missed target rate, and Mean Square Error (MSE). The developed method achieves the minimal detection time of 4.13s, minimal MSE of 677.19, and the minimal moving target rate of 0.145, respectively.

Keywords:

Remember to check out the Most Cited Articles!
Check out these titles in artificial intelligence!

References

1. H. Wang, Z. Chen and S. Zheng, Preliminary Research of low-RCS moving target detection based on Ka-band video SAR, IEEE Geosci. Remote Sens. Lett. 14(6) (2017) 811–815. Crossref, Google Scholar
2. D. Henke, E. M. Dominguez, D. Small, M. E. Schaepman and E. Meier, Moving target tracking in SAR data using combined exo- and endo-clutter processing, IEEE Trans. Geosci. Remote Sens. 56(1) (2018) 251–263. Crossref, Google Scholar
3. H. Xu, Z. Yang, M. Tian, Y. Sun and G. Liao, 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) (2018). Crossref, Google Scholar
4. M. Newey, G. R. Benitz, D. J. Barrett and S. Mishra, detection and imaging of moving targets with limit SAR data, IEEE Trans. Geosci. Remote Sens. 56(6) (2018) 3499–3510. Crossref, Google Scholar
5. Z. Li, F. Santi, D. Pastina and P. Lombardo, Multi-frame fractional Fourier transform technique for moving target detection with space-based passive radar, IET Radar, Sonar Navig. 11(5) (2016) 822–828. Crossref, Google Scholar
6. G. Yu and S. Piao, Multiple moving targets detection and parameters estimation in strong reverberation environments, Shock Vibr. 2016 (2016) 1–11. Google Scholar
7. Y. Li and T. Wang, Detection of moving targets by refocusing for airborne CSSAR, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9(7) (2016) 3026–3036. Crossref, Google Scholar
8. A. Taylor, H. M. Oriot, L. Savy, F. Daout and P. Forster, Moving targets detection capacities improvement in multichannel SAR framework, IEEE Trans. Geosci. Remote Sens. 55(6) (2017) 3248–3260. Crossref, Google Scholar
9. D. Cerutti-Maori and I. Sikaneta, A generalization of DPCA processing for multichannel SAR/GMTI Radars, IEEE Trans. Geosci. Remote Sens. 51(1) (2013). Crossref, Google Scholar
10. M. Zink, M. Bachmann, B. Bräutigam, T. Fritz, I. Hajnsek, G. Krieger, A. Moreira and B. Wessel, TanDEM-X: The new global DEM takes shape, IEEE Geosci. Remote Sens. Mag. 2(2) (2014) 8–23. Crossref, Google Scholar
11. W. Yao and W. Shan, A novel algorithm of coherent integration for moving target detection, in Proc. 2nd Int. Conf. Advanced Computer Control (Shenyang, China, 2010), pp. 235–239. Google Scholar
12. X. Chen, B. Chen, J. Guan, Y. Huang and Y. He, Space-range-doppler focus-based low-observable moving target detection using frequency diverse array MIMO radar, IEEE Access 6 (2018) 43892–43904. Crossref, Google Scholar
13. X. Fan, Y. Cheng and Q. Fu, Moving target detection algorithm based on Susan edge detection and frame difference, in Proc. 2nd Int. Conf. Information Science and Control Engineering (Shanghai, China, 2015), pp. 323–326. Crossref, Google Scholar
14. T. Sjögren and V. Vu, Detection of slow and fast moving targets using hybrid CD-DMTF SAR GMTI mode, in Proc. IEEE 5th Asia-Pacific Conf. Synthetic Aperture Radar (APSAR) (Singapore, 2015), pp. 818–828. Crossref, Google Scholar
15. W. Yu, W. Su and H. Gu, Ground maneuvering target detection based on discrete polynomial-phase transform and Lv’s distribution, Signal Process. 144 (2018) 364–372. Crossref, Google Scholar
16. J. Xu, J. Yu, Y.-N. Peng and X. Gen Xia, Radon-Fourier transform for radar target detection, i: Generalized doppler filter bank, IEEE Trans. Aerosp. Electron. Syst. 47(2) (2011) 1186–1202. Crossref, Google Scholar
17. X. Chen, J. Guan, N. Liu and Y. He, Maneuvering target detection via radon-fractional fourier transform-based long-time coherent integration, IEEE Trans. Signal Process. 62(4) (2014) 939–953. Crossref, Google Scholar
18. C.-F. Juang and C.-Y. Wang, A self-generating fuzzy system with ant and particle swarm cooperative optimization, Expert Syst. Appl. 36 (2009) 5362–5370. Crossref, Google Scholar
19. J. R. Fienup, Detecting moving targets in SAR imagery by focusing, IEEE Trans. Aerosp. Electron. Syst. 37(3) (2001) 794–809. Crossref, Google Scholar
20. G. Yu, S.-c. Piao and X. Han, Fractional Fourier transform-based detection and delay time estimation of moving target in strong reverberation environment, IET Radar, Sonar Navig. 11(9) (2017) 1367–1372. Crossref, Google Scholar
21. W. Xia, Y. Zhou, X. Jin and J. Zhou, A fast algorithm of generalized radon-Fourier transform for weak maneuvering target detection, Int. J. Antennas Propag. 2016(9) (2016) 1–10. Google Scholar
22. B. Dennis and S. Muthukrishnan, AGFS: Adaptive genetic fuzzy system for medical data classification, Appl. Soft Comput. 25 (2014) 242–252. Crossref, Google Scholar