volume | PIER Journals

Abstract

The reliable detection of geometrically-based stealth targets using a conventional single sensor radar system may be extremely difficult. This is because low Radar Cross Section (RCS) from certain angles results in a low Signal to Noise Ratio (SNR). In the present work, multi-target tracking of stealth targets is investigated in a multi-static radar with passive receivers. The Directions of Arrival (DOA) of targets are estimated by the receivers without knowing the number of targets, and their positions are obtained based on the transmitter beam direction. The B2 bomber aircraft model has been used as a stealth target. The RCS of the model has been simulated for all collection of incident and reflected angles from an oblique impinging plane wave. Probability of Detection (Pd) is modeled using a Toeplitz-based method for different SNRs due to different RCS patterns and is fed to an Iterated Corrected Probability Hypothesis Density (IC-PHD) filter. In spite of considering the transmitter and receivers resolution in our input data generation, the proposed algorithm is able to track the targets individually when they are much close to or even cross each other. Simulation results show the improved performance of the proposed method compared to other existing approaches.

1. Pulford, G., "Taxonomy of multiple target tracking methods," IEE Proceedings --- Radar, Sonar and Navigation, Vol. 152, No. 5, 291, 2005.
doi:10.1049/ip-rsn:20045064 Google Scholar

2. Khaleghi, B., A. Khamis, F. Karray, and S. Razavi, "Multisensor data fusion: A review of the state-of-the-art," Information Fusion, Vol. 14, No. 1, 28-44, 2013.
doi:10.1016/j.inffus.2011.08.001 Google Scholar

3. Vo, B.-N., M. Mallick, Y. Bar-Shalom, S. Coraluppi, R. Osborne III, R. Mahler, and B.-T. Vo, "Multitarget tracking," Wiley Encyclopedia of Electrical and Electronics Engineering, Wiley, Sep. 2015. Google Scholar

4. Bar-Shalom, Y., X. Tian, and P.Willett, Tracking and Data Fusion, YBS Publishing, Storrs, Conn., 2011.

5. Blackman, S. and R. Popoli, Design and Analysis of Modern Tracking Systems, Artech House, Boston, 1999.

6. Mahler, R., Statistical Multisource-multitarget Information Fusion, Artech House, Boston, 2007.

7. Mahler, R., Advances in Statistical Multisource-multitarget Information Fusion, Artech House, 2014.

8. Uney, M., D. Clark, and S. Julier, "Distributed fusion of PHD filters via exponential mixture densities," IEEE Journal of Selected Topics in Signal Processing, Vol. 7, No. 3, 521-531, 2013.
doi:10.1109/JSTSP.2013.2257162 Google Scholar

9. Mahler, R., "Multi-target Bayes filtering via first-order multi-target moments," IEEE Trans. Aerosp. Electron. Syst., Vol. 39, No. 4, 1152-1178, Oct. 2003.
doi:10.1109/TAES.2003.1261119 Google Scholar

10. Mahler, R., "PHD filters of higher order in target number," IEEE Trans. Aerosp. Electron. Syst., Vol. 43, No. 4, 1523-1543, 2007.
doi:10.1109/TAES.2007.4441756 Google Scholar

11. Nannuru, S., M. Coates, M. Rabbat, and S. Blouin, "General solution and approximate implementation of the multisensor multitarget CPHD filter," Proc. IEEE ICASSP-15, 4055-4059, Brisbane, Australia, Apr. 2015. Google Scholar

12. Nannuru, S., S. Blouin, M. Coates, and M. Rabbat, "Multisensor CPHD filter," IEEE Trans. Aerosp. Electron. Syst., Vol. 52, No. 4, 1834-1854, 2016.
doi:10.1109/TAES.2016.150265 Google Scholar

13. Vivone, G., P. Braca, K. Granstrom, and P. Willett, "Multistatic Bayesian extended target tracking," IEEE Trans. Aerosp. Electron. Syst., Vol. 52, No. 6, 2626-2643, 2016.
doi:10.1109/TAES.2016.150724 Google Scholar

14. Barbary, M. and P. Zong, "An accurate 3-D netted radar model for stealth target detection based on Legendre orthogonal polynomials and TDOA technique," International Journal of Modeling and Optimization, Vol. 5, No. 1, 22-31, Feb. 2015.
doi:10.7763/IJMO.2015.V5.430 Google Scholar

15. Schmidt, R. O., "Multiple emitter location and signal parameter estimation," IEEE Transitions on Antennas and Propagation, Vol. 34, No. 3, 276-280, Mar. 1986.
doi:10.1109/TAP.1986.1143830 Google Scholar

16. Zhang, Y. and B. P. Ng, "MUSIC-like DOA estimation without estimating the number of sources," IEEE Transactions on Signal Processing, Vol. 58, No. 3, 1668-1676, 2010.
doi:10.1109/TSP.2009.2037074 Google Scholar

17. Qian, C., L. Huang, W. Zeng, and H. So, "Direction-of-arrival estimation for coherent signals without knowledge of source number," IEEE Sensors Journal, Vol. 14, No. 9, 3267-3273, 2014.
doi:10.1109/JSEN.2014.2327633 Google Scholar

18. Li, W., Z.Wang, G.Wei, L. Ma, J. Hu, and D. Ding, "A survey on multisensor fusion and consensus filtering for sensor networks," Discrete Dynamics in Nature and Society, Vol. 2015, 1-12, 2015. Google Scholar

19. Harrington, R., Time-harmonic Electromagnetic Fields, IEEE Press, New York, 2001.
doi:10.1109/9780470546710

20. Ufimtsev, P., Method of Edge Waves in the Physical Theory of Diffraction, NTIS, Springfield, Va., 1980.

21. Kouyoumjian, R. and P. Pathak, "A uniform geometrical theory of diffraction for an edge in a perfectly conducting surface," Proceedings of the IEEE, Vol. 62, No. 11, 1448-1461, 1974.
doi:10.1109/PROC.1974.9651 Google Scholar

22. Yu, D. and M. Zhang, "Comparison of various full-wave methods in calculating the RCS of inlet," 2008 International Conference on Microwave and Millimeter Wave Technology, 1018-1021, Nanjing, 2008. Google Scholar

23. Gibson, W. C., The Method of Moments in Electromagnetics, CRC Press, 2014.
doi:10.1201/b17119

24. Skolnik, M., Radar Handbook, McGraw-Hill, New York, 2009.

25. Vo, B. and W. Ma, "The Gaussian mixture probability hypothesis density filter," IEEE Transactions on Signal Processing, Vol. 54, No. 11, 4091-4104, 2006.
doi:10.1109/TSP.2006.881190 Google Scholar

26. Huang, Z., S. Sun, and J. Wu, "A new data association algorithm using probability hypothesis density filter," Journal of Electronics (China), Vol. 27, No. 2, 218-223, 2010.
doi:10.1007/s11767-010-0304-0 Google Scholar

27. Mahler, R., B. Vo, and B. Vo, "CPHD filtering with unknown clutter rate and detection profile," IEEE Transactions on Signal Processing, Vol. 59, No. 8, 3497-3513, 2011.
doi:10.1109/TSP.2011.2128316 Google Scholar

28. Saucan, A.-A., M. Coates, and M. Rabbat, "A multi-sensor multi-Bernoulli filter," IEEE Transactions on Signal Processing, Vol. 65, No. 20, 5495-5509, 2017.
doi:10.1109/TSP.2017.2723348 Google Scholar

29. Vo, B., B. Vo, R. Hoseinnezhad, and R. Mahler, "Robust multi-bernoulli filtering," IEEE Journal of Selected Topics in Signal Processing, Vol. 7, No. 3, 399-409, 2013.
doi:10.1109/JSTSP.2013.2252325 Google Scholar

30. CST Microwave Studio, Computer Simulation Technology AG [Online], , available: http://www.cstamerica.com. Google Scholar

31. Schuhmacher, D., B.-T. Vo, and B.-N. Vo, "A consistent metric for performance evaluation of multi-object filters," IEEE Transactions on Signal Processing, Vol. 56, No. 8, 3447-3457, Aug. 2008.
doi:10.1109/TSP.2008.920469 Google Scholar

Dr. Amin Razmi

Dr. Mohammad Ali Masnadi-Shirazi

Dr. Alireza Masnadi-Shirazi