Abstract
When evaluating the track fusion algorithm, common accuracy indexes may fail to evaluate the fusion accuracy correctly when the state estimation and the real target cannot be one-to-one, and fail to effectively distinguish the performance of the algorithm when the state estimation is similar. Therefore, it is necessary to construct a high-resolution evaluation index, which can evaluate the track fusion algorithm more accurately, reasonably and comprehensively. Firstly, the advantages and disadvantages of the optimal subpattern assignment (OSPA) distance as the accuracy index to evaluate the track fusion algorithm are analyzed. Then, its deficiencies are improved by using the Hellinger distance instead of the original Euclidean distance, and the distance is index transformed. Finally, a new evaluation index for track fusion algorithms is proposed, which is the OSPA distance based on Hellinger distance and index transformation. The simulation results show that the new index can not only correctly evaluate the fusion precision, but also consider the state uncertainty, making that can evaluate the track fusion algorithm more sensitively, and effectively solves the sensitivity of the index to the cut-off parameter c through index transformation.
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References
GUO Z. Research on state estimation method based on multi-source information fusion [D]. Henan: Henan University, 2017 (in Chinese).
WU R C. Key technology research of information fusion in military information system [D]. Chengdu: University of Electronic Science and Technology of China, 2016 (in Chinese).
LI Y W, HU J W, JI B. A survey of the evaluation of multi-sensor target tracking system’s fusion ability [C]// The 6th China Conference on Command and Control. Beijing, China: Chinese Institute of Command and Control, 2018: 942–947 (in Chinese).
LIU H L, ZHOU S H, LIU H W, et al. Track quality evaluation method using amplitude information [J]. Journal of Xidian University, 2017, 44(1): 65–70 (in Chinese).
YANG S R. Research on the fime grained evaluation index of multi sensor information fusion algorithm [D]. Shanghai: East China University of Science and Technology, 2014 (in Chinese).
YU Y Y. Multi-model estimation based on Jaccard distance and conceptual clustering [J]. Computer Engineering, 2012, 38(10): 22–26 (in Chinese).
SHAN G L, ZHANG K, JI B. Metric for performance evaluation of tracking algorithms based on index fusion [J]. Chinese Journal of Scientific Instrument, 2014, 35(10): 2341–2347 (in Chinese).
SCHUHMACHER D, VO B T, VO B N. A consistent metric for performance evaluation of multi-object filters [J]. IEEE Transactions on Signal Processing, 2008, 56(8): 3447–3457.
SHI X B, YANG F, TONG F, et al. A comprehensive performance metric for evaluation of multi-target tracking algorithms [C]// 3rd International Conference on Information Management. Chengdu, China: IEEE, 2017: 373–377.
HE X F, THARMARASA R, KIRUBARAJAN T, et al. A track quality based metric for evaluating performance of multitarget filters [J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(1): 610–616.
RISTIC B, VO B N, CLARK D, et al. A metric for performance evaluation of multi-target tracking algorithms [J]. IEEE Transactions on Signal Processing, 2011, 59(7): 3452–3457.
NAGAPPA S, CLARK D E, MAHLER R. Incorporating track uncertainty into the OSPA metric [C]// 14th International Conference on Information Fusion. Chicago, IL, USA: IEEE, 2011: 1568–1575.
BHATIA R, GAUBERT S, JAIN T. Matrix versions of the Hellinger distance [J]. Letters in Mathematical Physics, 2019, 109(8): 1777–1804.
TANG Z. Uncertainty evaluation of measurement results of digital thermometer indication error [J]. China Science & Technology Overview, 2013(14): 134–135 (in Chinese).
WU J J, ZHOU X F. Minimum Hellinger distance estimation for a semiparametric location-shifted mixture model [J]. Journal of Statistical Computation and Simulation, 2018, 88(13): 2507–2527.
HE G J, CHEN K Y. Pipeline leak detection based on Hellinger distance [J]. China Petroleum and Chemical Standard and Quality, 2018(20): 187–189.
LIU W D, LIU Y, GAO L E. Research of track fusion based on convex combination and Bar-Shalom-Campo [J]. Computer Engineering and Applications, 2014, 50(2): 49–53 (in Chinese).
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Foundation item: the Natural Science Foundation of Hebei Province (No. F2017506006)
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Li, Y., Hu, J., Ji, B. et al. New Accuracy Evaluation Index for Track Fusion Algorithms. J. Shanghai Jiaotong Univ. (Sci.) 25, 97–105 (2020). https://doi.org/10.1007/s12204-019-2130-2
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DOI: https://doi.org/10.1007/s12204-019-2130-2
Key words
- track fusion algorithm
- evaluation index
- optimal subpattern assignment distance
- Hellinger distance
- index transformation