- Volume 18 Issue 5
DOI QR Code
Research on improvement of target tracking performance of LM-IPDAF through improvement of clutter density estimation method
클러터밀도 추정 방법 개선을 통한 LM-IPDAF의 표적 추적 성능 향상 연구
- Yoo, In-Je (Defense Agency for Technology and Quality) ;
- Park, Sung-Jae (Defense Agency for Technology and Quality)
- Received : 2017.02.09
- Accepted : 2017.05.12
- Published : 2017.05.31
Improving tracking performance by estimating the status of multiple targets using radar is important. In a clutter environment, a joint event occurs between the track and measurement in multiple target tracking using a tracking filter. As the number increases, the joint event increases exponentially. The problem to be considered when multiple target tracking filter design in such environments is that first, the tracking filter minimizes the rate of false track alarmsby eliminating the false track and quickly confirming the target track. The purpose is to increase the FTD performance. The second consideration is to improve the track maintenance performance by allocating each measurement to a track efficiently when an event occurs. Through two considerations, a single target tracking data association technique is extended to a multiple target tracking filter, and representative algorithms are JIPDAF and LM-IPDAF. In this study, a probabilistic evaluation of many hypotheses in the assignment of measurements was not performed, so that the computation amount does not increase nonlinearly according to the number of measurements and tracks, and the track existence probability based on the track density The LM-IPDAF algorithm was introduced. This paper also proposes a method to reduce the computational complexity by improving the clutter density estimation method for calculating the track existence probability of LM-IPDAF. The performance was verified by a comparison with the existing algorithm through simulation. As a result, it was possible to reduce the simulation processing time by approximately 20% while achieving equivalent performance on the position RMSE and Confirmed True Track.
Clutter Density;Data Association;LM-IPDAF;Multi-Target Tracking;Tracking Algorithm
- Y. Bar-Shalom and T. E. Fortmann, Tracking and Data Association, Academic Press, New York, 1988.
- Y.Bar-Shalom and X. R. Li, Estimation and Tracking, Principles, Techniques, and Software, ArtechHouse, 1993.
- Y. Bar-Shalom and X. R. Li, Multitarget - multisensor Tracking, Principles and Techniques, Storrs, CT : YBS Publishing, 1995.
- Y.Bar-Shalom, X. R. Li, and Kirubarajan, Estimation with Applications to Tracking and Navigation, Wiley, New York, 2001.
- D. Musicki, R. Evans and S.S tankovic, "Integrated Probabilistic Data Association (IPDA)", Proceedings of the 31st Conference on Decision and Control, Tucson, Artzone, Dec. 1992.
- D. Musicki, R. Evans, "Integrated Probabilistic Data Association in Clutter with Finite Resolution Sensor," Proceedings of the 32nd Conference on Decision and Control, San Astonlo, Texas, Dec. 1993. DOI: https://doi.org/10.1109/cdc.1993.325012 https://doi.org/10.1109/cdc.1993.325012
- D. Musicki, R. Evans, and S. Stankovic, "Integrated Probabilistic Data Association," IEEE Transactions on Automatic Control, vol. 39, no. 6, pp. 1237-1241, Jun. 1994. DOI: https://doi.org/10.1109/9.293185 https://doi.org/10.1109/9.293185
- D. Musicki, R. Evans, "Joint integrated probabilistic data association : JIPDA", IEEE Transactions on Aerospace and Electronic Systems, vol. 40, no. 3, pp. 1093-1099, Jul. 2004. DOI: https://doi.org/10.1109/TAES.2004.1337482 https://doi.org/10.1109/TAES.2004.1337482
- D. Musicki, B. Scala, "Multi-Target Tracking in Clutter without Measurement Assignment", IEEE Transactions on Aerospace and Electronic Systems, vol. 44, no. 3, pp. 877-896, Jul. 2008. DOI: https://doi.org/10.1109/TAES.2008.4655350 https://doi.org/10.1109/TAES.2008.4655350
- X. R. Li and Y. Bar-Shalom, "Tracking in Clutter with nearest neighbor filters :Analysis and Performance," IEEE Trans. AES, vol. 32, no. 3, pp. 995-1010, Jul. 1996. DOI: https://doi.org/10.1109/7.532259 https://doi.org/10.1109/7.532259
- X. Rong Li, "The PDF of Nearest Neighbor Measurement and A Probabilistic Nearest Neighbor Filter for Tracking in Clutter," The Proceedings of the 32nd Conference on Decision and Control, San Antonio, Texas, pp.918-923, Dec. 1993.
- Song, T. L., Lee, D. G. and Ryu, J. h, "A probabilistic nearest neighbor filter algorithm for tracking in clutter Environment," Signal Processing, vol. 85, Issue10, pp. 2044-2053, Oct. 2005. DOI: https://doi.org/10.1016/j.sigpro.2005.01.016 https://doi.org/10.1016/j.sigpro.2005.01.016
- Song, T. L. and Shin, S. J., "A Probabilistic Nearest Neighbor Filter for m validated measurements," Proceedings of the 6th International Conference on Information Fusion, Carins, Australia, 7. 2003.
- Song, T. L. and Lee, D. G.,"A Probabilistic Nearest Neighbor Filter Algorithm for m Validated Measurements," IEEE Transaction on Signal Processing, vol. 54, no. 7, pp. 2797-2802, July. 2006. DOI: https://doi.org/10.1109/TSP.2006.874803 https://doi.org/10.1109/TSP.2006.874803
- X. Rong Li, Probability, Random Signals, and Statistics, CRC, 1999.
- Athanasios Papoulis, S.Unnikrishna Pillai, Probability, Random Variables and Stochastic Processes Fourth edition, McGraw Hill, 2002.
- D. Musicki and R. J. Evans, "Clutter map information for data association and track initialization," IEEE Trans. of Aerospace Electronic Systems, vol. 40, no. 2, pp. 387-398, Apr. 2004. DOI: https://doi.org/10.1109/TAES.2004.1309992 https://doi.org/10.1109/TAES.2004.1309992