فهرست:
عنوان صفحه
چکیده. 1
مقدمه......................................................................................................................................................................................... ...... 2
فصل اول: کلیات.. 3
1-1 بیان مسأله.. 4
1-2 اهداف تحقیق.. 8
1-3 فرضیه ها 8
1-4 پیشینه‌ی تحقیق.. 8
1-5 روش تحقیق.. 9
فصل دوم: سابقه تحقیق.. 10
مقدمه................................................................................................................................................................................................ 11
2-1 مدل ردیابی چند هدفه به وسیله فیلتر بیزین.. 11
2-2 فیلتر گوسی.. 13
2-2-1 مدل ردیابی چند هدفه به وسیله فیلتر PHD... 14
2-3 فیلتر مونت‌کارلو. 22
2-3-1 مونت کارلو ترتیبی.. 23
2-4 فیلتر SMC-PHD با ثبت خطا 30
2-4-1 بررسی مشکل ثبت خطا 34
2-4-2 شبیه سازی SMC-PHD با ثبت خطا 36
فصل سوم: GM-PHD با کمک تخمین بایاس... 44
مقدمه 45
3-1 فیلتر GM-PHD با کمک تخمین بایاس برای اهداف خطی.. 50
3-1-1 مرحله اول: پیش بینی.. 50
3-1-2 مرحله دوم: به روز رسانی.. 51
3-1-3 مرحله سوم: هرس و ادغام اعضای گوسی.. 56
3-1-4 مرحله چهارم: تخمین موقعیت هدف و تخمین بایاس سنسور 60
3-2 فیلتر GM-PHD با کمک تخمین بایاس برای ردیابی اهداف غیر خطی(مانوری) 61
3-2-1 مرحله اول: تقریب BFG... 61
3-2-2 مرحله دوم: پیش بینی.. 65
3-3 معیار ارزیابی انواع فیلتر. 66
3-4 همگرایی خطا PHD... 68
3-5 اجرا فیلتر GM-PHD با کمک تخمین بایاس.... 73
3-5-1 الگوریتم اجرا GM-PHD با کمک تخمین بایاس برای اهداف خطی.. 73
3-5-2 الگوریتم اجرا GM-PHD با کمک تخمین بایاس برای اهداف غیر خطی.. 74
فصل چهارم: شبیه سازی. 75
مقدمه 76
4-1 شبیه سازی 1. 76
4-2 شبیه سازی 2. 85
فصل پنجم: نتیجه گیری و پیشنهادها 94
5-1 نتیجه گیری.. 95
5-2 پیشنهادها 98
منابع و مآخذ. 99
چکیده‌ی انگلیسی.. 1
منبع:
- Alspach, D. (1970). A Bayesian approximation technique for estimation and control of discrete time systems. Univ. Calif.
- Anderson , B., & Moore, J. (1979). Optimal Filtering. Englewood Cliffs.
- Bdddey, A., & van Lieshout, M. (1992). ICM for object recognition. Springer.
- Clark, D., & Bell, J. (Jul.2006). Convergence results for the particle PHDfilter. IEEE Trans. Signal Process, 54(7), 2652–2661.
- Clark, D., & Bell, J. (Oct. 2005). Bayesian multiple target tracking in forward scan sonar images using the PHD filter. Proc. Inst. Elect. Eng.—Radar, Sonar, Navigation, 152(5), 327–334.
- Clark, D., & Vo, B. (2007). Convergence analysis of the Gaussian mixture PHD filter. IEEE Transactions on Signal Processing, 55(4), 1204–1211.
- Clark, D., Panta, K., & Vo, B. (Jul 2006). The GM-PHD filter multiple target tracker. Information Fusion. Florence, Italy.
- Clark, D., Ruiz, I., Petillot, Y., & Bell, J. (2007). Particle PHD filter multiple target tracking in sonar image. IEEE Trans. Aerosp. Electron. Syst, 43(1), 409–416.
- Clark, D., Vo, B., & Bell, J. (Apr 2006). GM-PHD filter multi-target tracking in sonar images. presented at the SPIE Defense Security Symp, 17–21.
- Dana, M. (MA 1990). Registration: a prerequisite for multiple sensor tracking.Multitarget multisensor tracking. In advanced applications (pp. 155–185). Norwood: ArtechHouse Publishers,.
- Doucet, A., De Freitas, N., & Gordon, N. (May 2001). Sequential Monte Carlo Methods in Practice. Springer Springer-Verlag.
- El-Fallah, A., & Mahler, R. (May 2011). Bayesian unified registration and tracking. Proceedings of the SPIE Conference on Signal Processing, Sensor Fusion and Target Recognition, 8050, 1–11.
- Friedland, B. (1969). Treatment of bias in recursive filtering. IEEE Transactions on Automatic Control 14 (4), 14(4), 359–367.
- Geyer, C. (1999). Likelihood inference for spatial point processes. Stochastic Geometry likelihood and computation, 79-140.
- Goodman, I., Mahler, R., & Nguyen, H. (1997). Mathematics of Data Fusion. Kluwer Academic Publishers.
- Grimmett, G., & Stirzaker, D. (2011). One Thousand Exercises in Probability. London: Oxford University Press.
- Hernandez, M., Ristic, B., Farina, A., & Sathyan, T. (2008). Performance measure for Markovian switching systems using best bestfitting Gaussian distributions. IEEE Transactions on Aerospace and Electronic Systems, 44 (2), 724–747.
- Herrero, J., Portas, J., & Corredera, J. (2007). On-line multi-sensor registration for data fusion on airport surface. IEEE Trans. Aerosp. Electron. Syst, 43(1), 356–370.
- Ignagni, M. (1981). An alternate derivation and extension of Friedland’stwo-stage Kalman estimator. IEEE Transactions on Automatic Control, 26(3), 746–750.
- Ikoma, N., Uchino, T., & Maeda, T. (Aug 2004). Tracking of feature points in image sequence by SMC implementation of PHD filter. in Soc. Instrument and Control Engineers (SICE) 2004 Annu. Conf, 2, 1696–1701.
- Johansen, A., Singh, S., Doucet, A., & -N, B. (Jun. 2006). Convergence of the SMC implementation of the PHD filter. Method. Comput. Appl. Probab, 8(2), 265–291.
- Julier, S., & Uhlmann, J. (1996). A general method for approximating nonlinear transformations of probability distributions. RRG, Eng. Sci. Dep., Univ. Oxford, Oxford, U.K.,.
- Julier, S., & Uhlmann, J. (1997). A new extension of the Kalman filter to nonlinear systems. in Int. Symp. Aerosp./Defense Sensing, Simultaneous Controls, Orlando, FL.
- Li, X., & Jilkov, V. (April 2000). A survey of maneuvering target tracking: dynamic models. Proc. 2000 SPIE Conf. on Signal and Data Processing of Small Targets, 4048, 212–235.
- Lian, F., Han, C., Liu, W., & Chen, H. (2011). Joint spatial registration an multi-target tracking using an extended probability hypothesis density filter. IET Radar, Sonar and Navigation, 5(4), 441–448.
- Lo, H. (1972). Finite-dimensional sensor orbits and optimal non-linear filtering. IEEE Trans. Inf. Theory, IT-18(5), 583–588.
- Ma, W., Singh, S., & Vo, B. (2004). Tracking multiple speakers with random sets. Proceedings of the International Conference on Acoustics, Speech and Signal Processing, (pp. 357–360). Montreal, Canada.
- Mabler, R. (June 2000). A theoretical foundation for the Stein-Wmter Probability Hypothesis Density (PHD) multi-target tracking approach. Pmc.2002 MSS Nat? Symp. on Sensor and Data Fusion, 1. San Antonio TX.
- Maggio, E., Taj, M., & Cavallaro, A. (2008). Effcient multitarget visual tracking using random finite sets. IEEE Trans. Circuits Syst. Video Technol, 18(8), 1016–1027.
- Mahler, R. (1994). Global integrated data fusion. Proc. 7th Nat. Symp. on Sensor Fusion, 1, 187-199.
- Mahler, R. (2000). Approximate multisensor-multitarget joint detection, tracking and identification using a first order multitarget moment statistic. IEEE lhm. AES.
- Mahler, R. (2003). Multi-target Bayes filtering via first-order multi-target moments. IEEE Trans. Aerosp. Electron. Syst, 39(4), 1152–1178.
- Mahler, R. (2010). Approximate multisensor CPHD and PHD filters. Proceedings of the 13th International Conference on Information Fusion, 1-8.
- Mahler, R. (MA, 2007). Statistical Multisource-Multitarget Information Fusion. Artech House, Norwood,.
- Mahler, R. (March 2000). An Introduction to Multisource-Multitarget Statistics and Applications. Lockheed Martin Technical Monograph.
- Nagappa, S., & Clark, D. (May 2011). On the ordering of the sensors in the iterated-probability hypothesis density (PHD) filter. Proceedings of the SPIE Conference on Signal Processing, Sensor Fusion and Target Recognition, 8050, 1-6.
- Okello, N., & Ristic, B. (2003). Maximum likelihood registration for multiple dissimilar sensors. IEEE Trans. Aerosp. Electron. Syst, 39(3), 1074–1083.
- Pao, L., & Frei, C. (1995). A comparison of parallel and sequential implementation of a multisensor multitarget tracking algorithm. American Control Conf, (pp. 1683–1687). Seattle, Washington.
- Pham, N., Huang, W., & Ong, S. (2007). Multiple sensor multiple object tracking with GMPHD filter. Proceedings of the 10th International Conference on Information Fusion, 1–7.
- Ruan, Y., & Willett, P. (2004). The turbo PMHT. IEEE Trans. Aerosp. Electron.Syst, 40(4), 1388–1398.
- Rynne, B., & Youngson, M. (2000). Linear Functional Analysis. New York: Springer-Verlag.
- Salmond, D. (n.d.). Tracking in uncertain environments. 1989: Univ. Sussex, Sussex, U.K.
- Schuhmacher, D., Vo, B., & Vo, B. (2008). A consistent metric for performance evaluation of multi-object filters. IEEE Trans. Sign Process, 86(8), 3447–3457.
- Shalom, Y., & Li, X. (1995). Multitarget-Multisensor Tracking. Principles and Techniques Storrs.
- Sorenson, H., & Alspach, D. (1971). Recursive Bayesian estimation using Gaussian sum. Automatica, 7, 465–479.
- Sudano, J. (1993). A least square algorithm with covariance weighting for computing the translational and rotational errors between two radar sites". Proceedings of the IEEE Aerospace and Electronics Conference, 383–387.
- Tobias, M., & Lanterman, A. (2005). Probability hypothesis density-based multi-target tracking with bistatic range and Doppler observations. IEE Proc. Radar Sonar Navig, 152(3), 195–205.
- Vo, B., & Ma, W. (2006). “The Gaussian mixture probability hypothesis density filter. ,” IEEE Transactions on Signal Processing, 54(11), 4091–4104.
- Vo, B., & Ma, W. (Jul 2005). A closed-form solution to the probability hypothesis density filter. Inf. Fusion, 2, pp. 25–28. Philadelphia.
- Vo, B., Singh, S., & Doucet, A. (2003). Sequential Monte Carlo implementation of the PHD filter for multi-target tracking. Proc. FUSION 2003, 792–799.
- Vo, B., Singh, S., & Doucet, A. (2005). Sequential Monte Carlo methods for multi-target filtering with random finite sets. IEEE Trans. Aerosp. Electron. Syst, 41(4), 1224–1245.
- Vo, B., Vo, B., & Cantoni, A. (2009). The cardinality balanced multi-target multi-Bernoulli filter and its implementations. IEEE Transactions on Signal Processing 57 (2), 57(2), 409–423.
- Williams, J. (2003). Gaussian Mixture reduction for tracking multiple maneuvering targets in clutter. Master’s thesis, Grad. School of Eng. and Management, Air Force Inst. Technol., Wright-Patterson Air Force Base, OH.
- Zhou, Y., Leung, H., & Martin, B. (1999). Sensor alignment with earthcentered earth-fixed coordinate system. IEEE Trans. Aerosp.Electron. Syst, 35(2), 410–416