volume | PIER Journals

Abstract

Standard radar detection process requires that the sensor output is compared to a predetermined threshold. The threshold is selected based on a-priori knowledge available and/or certain assumptions. However, any knowledge and/or assumptions become inadequate due to the presence of multiple targets with varying signal return and usually non stationary background. Thus, any fixed predefined threshold may result in either increased false alarm rate or increased track loss. Even approaches where the threshold is adaptively varied will not perform well in situations when the signal return from the target of interest is too low compared to the average level of the background. Track-before-detect (TBD) techniques eliminate the need for a detection threshold and provide detecting and tracking targets with lower signal-to-noise ratios than standard methods. However, although TBD techniques eliminate the need for detection threshold at sensor's signal processing stage, they often use tuning thresholds at the output of the filtering stage. This paper presents a Hidden Markov Model (HMM) based target detection method that avoids any thresholding at any stage of the detection process. Moreover, since the proposed HMM method is based on the target motion models, the output of the detection process can easily be employed for manoeuvring target tracking.

1. Buzzi, S., M. Lops, L. Venturino, and M. Ferri, "Trackbefore-detect procedures in a multi-target environment," IEEE Transactions on Aerospace and Electronic Systems, Vol. 44, 1135-1150, 2008.
doi:10.1109/TAES.2008.4655369 Google Scholar

2. Orlando, D., G. Ricci, and Y. Bar-Shalom, "Track-before-detect algorithms for targets with kinematic constraints," IEEE Transactions on Aerospace and Electronic Systems, Vol. 47, 1837-1849, 2011.
doi:10.1109/TAES.2011.5937268 Google Scholar

3. Tonissen, S. M. and R. J. Evans, "Performance of dynamic programming techniques for track-before-detect," IEEE Transactions on Aerospace and Electronic Systems, Vol. 32, 1440-1451, 1996.
doi:10.1109/7.543865 Google Scholar

4. Johnston, L. A. and V. Krishnamurthy, "Performance analysis of a dynamic programming track before detect algorithm," IEEE Transaction on Aerospace and Electronic Systems, Vol. 38, 228-242, 2002.
doi:10.1109/7.993242 Google Scholar

5. Tonissen, , S. M. and R. J. Evans, "Target tracking using dynamic programming: Algorithm and performance," Proceedings of the 34th Conference on Decision and Control, 1995. Google Scholar

6. Bruno, M. G. S., "Bayesian methods for multiaspect target tracking in image sequences," IEEE Transactions on Signal Processing, Vol. 52, No. 7, 1848-1861, 2004.
doi:10.1109/TSP.2004.828903 Google Scholar

7. Rutten, M. G., N. J. Gordon, and S. Maskell, "Recursive track-before-detect with target amplitude fluctuations," IEE Proceedings on Radar, Sonar and Navigation, Vol. 152, No. 5, 345-322, 2005.
doi:10.1049/ip-rsn:20045041 Google Scholar

8. Davey, S., M. Rutten, and B. Cheung, "A comparison of detection performance for several track-before-detect algorithms," EURASIP Journal on Advances in Signal Processing, 2008. Google Scholar

9. Streit, R. L. and R. F. Barrett, "Frequency line tracking using hidden Markov models," IEEE Transactions on Acoustics, Speech and Signal Processing, Vol. 38, No. 4, 586-598, 1990.
doi:10.1109/29.52700 Google Scholar

10. Xie, X. and R. J. Evans, "Multiple target tracking and multiple frequency line tracking using hidden Markov models," IEEE Transactions on Signal Processing, Vol. 39, No. 12, 2659-2676, 1991.
doi:10.1109/78.107416 Google Scholar

11. Martinerie, F., "Data fusion and tracking using HMMs in a distributed sensor network," IEEE Transaction on Aerospace and Electronic Systems, Vol. 33, No. 1, 11-28, 1997.
doi:10.1109/7.570704 Google Scholar

12. Bharadwaj, P. K., P. R. Runkle, and L. Carin, "Target identification with wave based matched pursuits and hidden Markov models," IEEE Transactions on Antennas and Propagation, Vol. 47, No. 10, 1543-1554, 1999.
doi:10.1109/8.805897 Google Scholar

13. Li, X. R. and V. Jilkov, "A survey of maneuvering target tracking --- Part IV: Decision-based methods," Proceedings of SPIE Conference Signal and Data Processing of Small Targets,, Vol. 4728, Orlando, Florida, April 2002. Google Scholar

14. Bar-Shalom, Y. and X. R. Li, Estimation and Tracking: Principles, Techniques and Software, Artech House, 1993.

15. Rabiner, L. R., "A tutorial on hidden Markov models and selected applications in speech recognition," Proceedings of the IEEE, Vol. 77, 257-286, 1989.
doi:10.1109/5.18626 Google Scholar

16. Young, S., D. Kershaw, J. Odell, D. Ollason, V. Caltchev, and P. Woodland, , Version 3.0, 2000. Google Scholar

17. Tugac, S. and M. Efe, "Hidden Markov model based target detection," 13th International Conference on Information Fusion, 2010. Google Scholar

18. Doucet, A., J. F. G. Freitas, and N. J. Gordon, "An introduction to sequential Monte Carlo methods," Sequential Monte Carlo Methods in Practice, A. Doucet, J. F. G. de Freitas, and N. J. Gordon (eds.), Springer-Verlag, NY, 2001. Google Scholar

19. Boers, Y. and J. N. Driessen, "Particle filter based detection for tracking," Proceedings of American Control Conference, 3755-3760, 2001. Google Scholar

20. Boers, Y. and J. N. Driessen, "A particle filter based detection scheme," IEEE Signal Processing Letters, Vol. 10, No. 10, 300-302, 2003.
doi:10.1109/LSP.2003.817175 Google Scholar

21. Hong, S., L. Wang, Z.-G. Shi, and K. S. Chen, "Simplified particle PHD filter for multiple-target tracking: Algorithm and architecture," Progress In Electromagnetics Research, Vol. 120, 481-498, 2011. Google Scholar

22. Wang, X., J.-F. Chen, Z.-G. Shi, and K. S. Chen, "Fuzzy-control-based particle ¯lter for maneuvering target tracking," Progress In Electromagnetics Research, Vol. 118, 1-15, 2011.
doi:10.2528/PIER11051907 Google Scholar

Mr. Serdar Tugac

Dr. Murat Efe