Browse > Article
http://dx.doi.org/10.9766/KIMST.2014.17.1.096

Study on Tactical Target Tracking Performance Using Unscented Transform-based Filtering  

Byun, Jaeuk (School of Information and Communications, Gwangju Institute of Science and Technology)
Jung, Hyoyoung (School of Information and Communications, Gwangju Institute of Science and Technology)
Lee, Saewoom (School of Information and Communications, Gwangju Institute of Science and Technology)
Kim, Gi-Sung (Naval combat systems PEO, Agency for Defense Development)
Kim, Kiseon (School of Information and Communications, Gwangju Institute of Science and Technology)
Publication Information
Journal of the Korea Institute of Military Science and Technology / v.17, no.1, 2014 , pp. 96-107 More about this Journal
Abstract
Tracking the tactical object is a fundamental affair in network-equipped modern warfare. Geodetic coordinate system based on longitude, latitude, and height is suitable to represent the location of tactical objects considering multi platform data fusion. The motion of tactical object described as a dynamic model requires an appropriate filtering to overcome the system and measurement noise in acquiring information from multiple sensors. This paper introduces the filter suitable for multi-sensor data fusion and tactical object tracking, particularly the unscented transform(UT) and its detail. The UT in Unscented Kalman Filter(UKF) uses a few samples to estimate nonlinear-propagated statistic parameters, and UT has better performance and complexity than the conventional linearization method. We show the effects of UT-based filtering via simulation considering practical tactical object tracking scenario.
Keywords
Communication; Network Centric Operational Environment; Tracking; Geodetic Coordinate System; Kalman Filter; Unscented Transform;
Citations & Related Records
연도 인용수 순위
  • Reference
1 S. J. Julier and J. K. Uhlmann, "The scaled Unscented Transformation," in Proc. Amer. Control Conf., pp. 4555-4559, 2002.
2 S. J. Julier and J. K. Uhlmann, "Unscented Filtering and Nonlinear Estimation," Proceedings of the IEEE, Vol. 92, No. 3, pp. 401-422, March 2004.
3 R. van der Merwe, "Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models," In Workshop on Advances in Machine Learning, Montreal, June 2003.
4 R. van der Merwe, "Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models," PhD thesis, OGI School of Science & Engineering at Oregon Health & Science University, Portland, OR, April 2004.
5 R. Turner and C. E. Rasmussen, "Model Based Learning of Sigma Points in Unscented Kalman Filtering," Neurocomputing, Vol. 80, pp. 47-53, 2012.   DOI
6 K. C. Bailey, "Iraq's Asymmetric Threat to the United States and US Allies," Comparative Strategy 21.3, pp. 161-177, 2002.   DOI
7 J. Kueter, K. Howard, "The Cruise Missile Challenge : Designing a Defense Against Asymmetric Threats," George C. Marshall Institute, 2007.
8 C. Hu, et al., "Adaptive Kalman Filtering for Vehicle Navigation," Journal of Global Positioning Systems 2.1, pp. 42-47, 2003.   DOI
9 R. O. Zetik, et al., "Kalman Filter Based Tracking of Moving Persons Using UWB Sensors," Wireless Sensing, Local Positioning, and RFID, 2009. IMWS 2009. IEEE MTT-S International Microwave Workshop on. IEEE, 2009.
10 M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, "A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking," IEEE Trans. Signal Processing, Vol. 50, No. 2, pp. 174-188, Feb. 2002.   DOI   ScienceOn
11 G. Welch and G. Bishop, "An Introduction to the Kalman Filter," Technical Report TR 95-041, University of North Carolina at Chapel Hill, 1995.
12 S. J. Julier, J. K. Uhlmann and H. F. Durrant- Whyte, "A New Approach for Filtering Nonlinear Systems," In American Control Conference, Seattle, Washington, pp. 1628-1632, 1995.
13 International Hydrographic Organization, "User's Handbook on Datum Transformations Involving WGS 84," International Hydrographic Bureau, 2003.
14 E. Wan and R. van der Merwe, "The Unscented Kalman Filter for Nonlinear Estimation," Presented at the Proc. Symp. 2000 Adaptive Systems Signal Processing, Communications, and Control, Lake Louise, AB, Canada, 2000.
15 M. St-Pierre and D. Gingras, "Comparison between the Unscented Kalman Filter and the Extended Kalman Fiter for the Position Estimation Module of an Integrated Navigation Information System," in Proc. IEEE Intell. Veh., Symp., Parma, Italy, pp. 831-835, Jun. 2004.
16 M. Marron, "Comparing a Kalman Filter and a Particle Filter in a Multiple Objects Tracking Application," Intelligent Signal Processing 2007, IEEE International Symposium on. IEEE, 2007.
17 K. C. Lee, "A Comparison between Unscented Kalman Filtering and Particle Filtering for RSSI-based Tracking," Positioning Navigation and Communication(WPNC), 2010 7th Workshop on. IEEE, 2010.
18 Y. Boers, Y. et al., "Interacting Multiple Model Particle Filter," IEE Proceedings-Radar, Sonar and Navigation 150.5, pp. 344-349, 2003.
19 H. A. P. Blom, "An Efficient Filter for Abruptly Changing Systems," Decision and Control, 1984. The 23rd IEEE Conference on. Vol. 23, IEEE, 1984.
20 Y. Bar-Shalom, et al. "Tracking a Maneuvering Target Using Input Estimation Versus the Interacting Multiple Model Algorithm," Aerospace and Electronic Systems, IEEE Transactions on 25.2, pp. 296-300, 1989.   DOI   ScienceOn
21 T. Kirubarajan, Y. Bar-Shalom, "Kalman Filter vs. IMM Estimator : When Do We Need the Latter?," IEEE Transactions on Aerospace and Electronic Systems, Vol. 39, No. 4, pp. 1452-1457, Oct. 2003.   DOI   ScienceOn
22 임재성, "NCW 시대에 요구되는 국방 IT 기술", 제10회 통신핵심기술 워크샵, 아주대학교 국방전술네트워크 연구센터, 2008.
23 X. R. Li and V. P. Jilkov, "Survey of Maneuvering Target Tracking," IEEE Trans. on AES, Vol. 39, pp. 1333-1363, Oct. 2003.
24 R. K. Saha, "Trak-to-track Fusion with Dissimilar Sensors," IEEE Trans. on AES, Vol. 32, pp. 1021-1029, July 1996.
25 D. Fox, J. Hightower, H. Kautz, L. Liao, and D. J. Patterson, "Bayesian Techniques for Location Estimation," in Proc. UBIComp Workshop, pp. 16-18, 2003.
26 R. E. Kalman, "A New Approach to Linear Filtering and Prediction Problems," Transaction of the ASME-Journal of Basic Engineering, pp. 35-45, Mar. 1960.
27 박휘락, 김관호, "미래 NCW 수행을 위한 육군야전 자동화 정보체계 운용방안 연구," 2008 육군정책과제 보고서, 사단법인 21세기군사연구소, pp. 12-18, 2008.