استفاده از تخمین بیزی هدف برای آشکارسازی اهداف راداری در کلاتر گوسی
محورهای موضوعی : مهندسی برق و کامپیوترمحمدفرزان صباحی 1 * , سیدمحمود مدرس هاشمی 2 , عباس شیخی 3
1 - دانشگاه اصفهان
2 - دانشگاه صنعتی اصفهان
3 - دانشگاه شیراز
کلید واژه: آشکارسازی راداریتخمین بیزیکلاتر AR,
چکیده مقاله :
در بسياري از مسائل آشکارسازي مدل سيگنال دريافتي تحت فرضيه 0H همان مدل سيگنال دريافتي تحت فرضيه 1H است با اين تفاوت که بعضي از پارامترهاي مدل مقادير ثابت و مشخصي فرض مي‌شوند. چنين مدل‌هايي اصطلاحاً مدل‌هاي تودرتو ناميده مي‌شوند. يک مثال از چنين مسائلي آشکارسازي يک هدف با دامنه مجهول در کلاتر محيط است. در اين حالت فرضيه 0H همان فرضيه 1H است که دامنه هدف در آن صفر فرض شده است. با در نظر گرفتن ديدگاه بيزي در مورد پارامترهاي مجهول، نسبت درست‌نمايي را مي‌توان با استفاده از توزيع‌هاي پسين و پيشين پارامترها به دست آورد. در اين مقاله با استفاده از اين روش يک آشکارساز با ساختار ساده براي کلاتر گوسي ارائه مي‌شود که در مقايسه با آشکارسازهاي GLRT مرسوم برتري قابل توجهی دارد. همچنین نشان می‌دهیم که با اصلاحاتی در قاعده آشکارسازی می‌توان به خاصیت CFAR دست یافت
In many of detection problems the received signals models under two hypotheses, H0 and H1, are the same except that some model parameters have fixed value under H0. These models are so called Nested Models. One of the most important examples is detection of a target with unknown amplitude in the clutter. In this problem, one can assume similar models for received signals under H0 and H1 unless the target amplitude is assumed to be zero under H0. If the Bayesian approach used for treating unknown parameters, it can be shown that the likelihood ratio can be calculated as the ratio of the posterior and the prior probability of unknown parameters. Using this method a new detector for detection in Gaussian clutter is presented in this paper. Simulation results show that the proposed detector has much better performance compared with conventional GLRT detectors. It is also shown that a CFAR property is achieved provided that a small modifications in decision rule.
[1] S. Haykin, Adaptive Radar Detection and Estimation, John Wiley,1st edn, New York, 1992.
[2] E. J. Kelly, "An adaptive detection algorithm," IEEE Trans on AES,vol. 22, no. 2, pp. 115-127, Mar. 1986.
[3] H. Wang and L. Cai, "Performance comparisons of modified SMIand GLR algorithms," IEEE Trans on AES, vol. 27, no. 3, pp. 487- 491, May 1991
[4] A. Nelander, "Model-based adaptive detection of fluctuating targets," in Proc. of IEEE Int. Radar Conf., pp. 381-386, May 2000
[5] G. Alfano, A. De Maio, and A. Farina, "Model - based adaptivedetection of range- spread targets," in IEE Proc. Radar Sonar Navig., vol. 151, no. 1, pp. 2-10, Feb. 2004.
[6] E. Conte, A. De Maio, and G. Ricci, "GLRT-based adaptive detection algorithms for range-spread targets," IEEE Trans. Signal Process., vol. 49, no. 7, pp. 1336-1348, Jul. 2001.
[7] K. Gerlach and M. J. Steiner, "Adaptive detection of range distributed targets," IEEE Trans. Signal Process., vol. 47, no. 7, pp. 1844-1851, Jul. 1999.
[8] E. Fishler, A. Haimovich, R. Blum, L. Cimini, D. Chizhik, and R.Valenzuela, "MIMO radar: an idea whose time has come," in Proc. of the IEEE Radar Conf., pp. 71-78, Apr. 2004.
[9] E. Fishler, A. Haimovich, R. Blum, L. Cimini, D. Chizhik, and R.Valenzuela, "Performance of MIMO radar systems: advantages of angular diversity," in Proc. the 38th Asilomar Conf. on Signals,Systems, and Computers, vol. 1, pp. 305-309, Nov. 2004
[10] M. I. Skolnik, Introduction to Radar Systems, McGrawHill, 3rdedn., 2001.
[11] S. M. Kay, Fundamental of Statistical Signal Processing: Detection Theory, Prentice Hall, 1998.
[12] J. M. Dickey, "The weighted likelihood ratio, linear hypothesis on normal location parameters," Annals of Mathematical Statistics, vol. 42, no. 1, pp 204-223, Feb. 1971.
[13] A. Gelfand and A. F. M. Smith, "Sampling - based approaches to calculating marginal densities," J. of American Statistical Association, vol. 85, no. 410, pp. 398-409, Jun. 1990.
[14] M. H. Chen, "Importance weighted marginal bayesian posterior density estimation," J. of American Statistical Association, vol. 85, no. 427, pp. 818-824, Sep. 1994.
[15] M. A. Newton and A. E. Raftery, "Approximate Bayesian inference with the weighted likelihood bootstrap," J. of Royal Statistical Society B, vol. 56, no. 1, pp. 3-48, Oct. 1994.
[16] S. M. Kay, Fundamental of Statistical Signal Processing: Estimation Theory, Prentice Hall, 1993.
[17] F. C. Robby, E. J. Kelly, and R. Nitzberg, "A CFAR adaptive matched filter detector," IEEE Trans on AES, vol. 28, no. 1, pp. 208- 216, Jan. 1992.
[18] A. Sheikhi, M. M. Nayebi, and M. R. Aref, "Adaptive detection algorithm for radar signals in autoregressive interference," IEE Proceedings Radar, Sonar, Navig., vol. 145, no. 5, pp. 309-314,Oct. 1998.
[19] N. Giri, "On the complex analysis of T2 and R2 tests," Annals of Mathematical Statistics, vol. 36, no. 2, pp. 664-670, Nov. 1965.
[20]ع.شیخی،آشکارسازی وقفی اهداف راداری، پایان¬نامه دکترا،دانشگاه صنعتی شریف، 1378
[21] J. H. Michels, B. Himed, and M. Rangaswamy, "Robust STAP detection in a dense signal airborne radar environment," Signal Processing vol. 84, no. 9, pp. 1625-1636, Sep. 2004.
[22] A. Shahraki, M. Modarres Hashemi, and A. Sheikhi, "Adaptive radar detection of fluctuating target in autoregressive interference," Iranian J. of Science & Technology Transaction B: Technology,vol. 31, no. 3, pp. 345-360, Jun. 2007.
[23] S. Souvorova, B. Moran, and M. Viola, "Adaptive modelling of sea clutter and detection of small targets in heavy clutter," in Proc. IEEE Int. Conf. on Radar, vol. 1, pp 614-618, Adelaide, Australia,Sep. 2003.
[24] A. De Maio, G. Foglia, E. Conte, and A. Farina, "CFAR behavior of adaptive detectors: an experimental analysis," IEEE Trans. on AES,vol. 41, no. 1, pp. 233-251, Jan. 2005.
[25] S. Haykin and P. A. S. Metford, "Some limiting forms of an innovations-based discrete time detection algorithm," IEEE Trans on AES, vol. 23, no. 3, pp. 405-411, May 1987.
[26] J. L. Noga, Bayesian State - Space Modelling of Spatio – Temporal Non-Gaussian Radar Returns, Ph.D Thesis, University of Cambridge, 1998.