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Nazari, mousa, AlyanNezhadi, Mohammad. M, Hashemi, Seyed Mohammad Reza, Mirrezaei, Seyed Masoud. (1402). A New Nearest Neighbors Data Association Approach Based on Fuzzy Density Clustering. نشریه مهندسی عمران امیرکبیر, 55(Issue 3 (Special Issue)), 393-404. doi: 10.22060/eej.2023.22328.5539
mousa Nazari; Mohammad. M AlyanNezhadi; Seyed Mohammad Reza Hashemi; Seyed Masoud Mirrezaei. "A New Nearest Neighbors Data Association Approach Based on Fuzzy Density Clustering". نشریه مهندسی عمران امیرکبیر, 55, Issue 3 (Special Issue), 1402, 393-404. doi: 10.22060/eej.2023.22328.5539
Nazari, mousa, AlyanNezhadi, Mohammad. M, Hashemi, Seyed Mohammad Reza, Mirrezaei, Seyed Masoud. (1402). 'A New Nearest Neighbors Data Association Approach Based on Fuzzy Density Clustering', نشریه مهندسی عمران امیرکبیر, 55(Issue 3 (Special Issue)), pp. 393-404. doi: 10.22060/eej.2023.22328.5539
Nazari, mousa, AlyanNezhadi, Mohammad. M, Hashemi, Seyed Mohammad Reza, Mirrezaei, Seyed Masoud. A New Nearest Neighbors Data Association Approach Based on Fuzzy Density Clustering. نشریه مهندسی عمران امیرکبیر, 1402; 55(Issue 3 (Special Issue)): 393-404. doi: 10.22060/eej.2023.22328.5539

A New Nearest Neighbors Data Association Approach Based on Fuzzy Density Clustering

AUT Journal of Electrical Engineering
مقاله 7، دوره 55، Issue 3 (Special Issue)، 2023، صفحه 393-404    اصل مقاله (1.3 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22060/eej.2023.22328.5539
نویسندگان
mousa Nazari1؛ Mohammad. M AlyanNezhadi* 1؛ Seyed Mohammad Reza Hashemi2؛ Seyed Masoud Mirrezaei3
1Department of Computer Science, University of Science and Technology of Mazandaran, Behshahr, Iran
2Faculty of Computer Engineering, Shahrood University of Technology, Shahrood, Iran
3Faculty of Electrical Engineering, Shahrood University of Technology, Shahrood, Iran
چکیده
The problem of valid measurement’s associations with true targets called “data association” is an essential challenge in multi-target tracking. Previous works often use the nearest neighbor or all neighbor approaches for updating the position of the targets, which are unsuccessful in complex environments and real-time applications, respectively. This paper provides a novel and effective solution to the data association problem in multi-target tracking, offering promising advancements in heavily cluttered environments. The proposed method uses important measurements that are determined based on fuzzy membership degrees. We selected and used valid measurements with a high fuzzy membership degree for updating the position of the targets. In this paper, we used two approaches for the selection of important measurements. The first strategy selects the k measurements with the highest degree of membership among the valid measurements. A second strategy is to give up measurements with very low membership degrees. The ability to solve the data association problem for both approaches under different levels of selecting measurements is evaluated. The proposed method is examined under two scenarios: linear crossing and maneuvering targets. The results show that the proposed technique performs better than FNN, JPDAF, MEF-JPDAF, and Fuzzy-GA methods based on the RMSE criterion .
کلیدواژه‌ها
Data Association؛ Fuzzy Clustering؛ Valid Measurement
مراجع
  1. S. Satapathi and P. Srihari, “STAP-Based Approach for Target Tracking Using Waveform Agile Sensing in the Presence of ECM,” Arab. J. Sci. Eng., Aug. 2017.
  2. Nazari, S. Pashazadeh, and L. Mohammad-Khanli, “An Adaptive Density-Based Fuzzy Clustering Track Association for Distributed Tracking System,” IEEE Access, vol. 7, pp. 135972–135981, 2019.
  3. He, H. S. Shin, and A. Tsourdos, “Distributed joint probabilistic data association filter with hybrid fusion strategy,” IEEE Trans. Instrum. Meas., vol. 69, no. 1, pp. 286–300, 2020.
  4. Nazari and S. Pashazadeh, “Real-time adaptive fuzzy density clustering for multi-target data association,” Intelligent Data Analysis, vol. 25, no. 1, pp. 5–19, 2021.
  5. Rakai, H. Song, S. Sun, W. Zhang, and Y. Yang, “Data association in multiple object tracking: A survey of recent techniques,” Expert Systems with Applications, vol. 192, 2022.
  6. Li, J. Liu, K. Chen, X. Gao, C. Tang, C. Xie, and X. Lu, “Heterogeneous Sensing for Target Tracking: Architecture, Techniques, Applications, and Challenges,” Measurement Science and Technology, 2023
  7. Xie, Y. Huang, and T. L. Song, “Iterative joint integrated probabilistic data association filter for multiple-detection multiple-target tracking,” Digit. Signal Process., vol. 72, pp. 232–243, 2018.
  8. Nazari and S. Pashazadeh, “Density Clustering Based Data Association Approach for Tracking Multiple Target in Cluttered Environment,” in Internatinal Conference on Contemporany Issue in Data Science, 2019.
  9. A. Memon, T. L. Song, K. H. Memon, I. Ullah, and U. Khan, “Modified smoothing data association for target tracking in clutter,” Expert Syst. Appl., vol. 141, p. 112969, 2020.
  10. Bar-Shalom and T. E. Fortmann, Tracking and Data Association, vol. 179. San Diego, CA, USA: Academic Press Professional, Inc., 1988.
  11. M. Aziz, “A new nearest-neighbor association approach based on fuzzy clustering,” Aerosp. Sci. Technol., vol. 26, no. 1, pp. 87–97, 2013.
  12. S. Satapathi and P. Srihari, “Soft and evolutionary computation based data association approaches for tracking multiple targets in the presence of ECM,” Expert Syst. Appl., vol. 77, pp. 83–104, 2017.
  13. Liu, H. Ji, W. Zhang, and G. Liao, “Multi-Sensor Multi-Target Tracking Using Probability Hypothesis Density Filter,” IEEE Access, vol. 7, pp. 67745–67760, 2019.
  14. Fan, W. Xie, J. Pei, K. Hu, X. Li, and V. Podpečan, “Improved Joint Probabilistic Data Association (JPDA) Filter Using Motion Feature for Multiple Maneuvering Targets in Uncertain Tracking Situations,” Information, vol. 9, no. 12, p. 322, 2018.
  15. Wang, Q. Bao, and Z. Chen, “Refined PHD filter for multi-target tracking under low detection probability,” Sensors (Switzerland), vol. 19, no. 13, 2019.
  16. Chen and others, “Bayesian filtering: From Kalman filters to particle filters, and beyond,” Statistics (Ber)., vol. 182, no. 1, pp. 1–69, 2003.
  17. S. Blackman, “Multiple hypothesis tracking for multiple target tracking,” IEEE Aerosp. Electron. Syst. Mag., vol. 19, no. 1, pp. 5–18, 2004.
  18. Gorji, M. B. Menhaj, and S. Shiry, “Multiple target tracking for mobile robots using the JPDAF algorithm,” in Tools and Applications with Artificial Intelligence, Springer, 2009, pp. 51–68.
  19. Zhou, S. Wang and T. Qin. “Multi-Spacecraft Tracking and Data Association Based on Uncertainty Propagation.” Applied Sciences,vol. 12, no. 15, 2022.
  20. L. Purushottama and P. Srihari, ”Comparative analysis on diverse heuristic-based joint probabilistic data association for multi-target tracking in a cluttered environment,” in Proceedings of First International Conference on Computational Electronics for Wireless Communications: ICCWC 2021, pp. 259-270, Springer Singapore,2022.
  21. Yi, Z. Yao, and W. Meng-yan, “Research On Underwater Multi-clutter Data Association Based On Fuzzy Clustering,” in 2019 Chinese Automation Congress (CAC), 2019, pp. 5115–5119. 
  22. Liangqun, J. Hongbing, and G. Xinbo, “Maximum entropy fuzzy clustering with application to real-time target tracking,” Signal Processing, vol. 86, no. 11, pp. 3432–3447, 2006.
  23. D. Tafti and N. Sadati, “Modified Maximum Entropy Fuzzy Data Association Filter,” J. Dyn. Syst. Meas. Control, vol. 132, no. 2, p. 21013, 2010.
  24. Zhang, H. Ji, and C. Ouyang, “Multitarget bearingsonly tracking using fuzzy clustering technique and Gaussian particle filter,” J. Supercomput., vol. 58, no. 1, pp. 4–19, Oct. 2011.
  25. Yu, B. B. Zhang, B. Rao, and L. Chen, “An improved fuzzy C-means cluster algorithm for radar data association,” Int. J. Adv. Comput. Technol., vol. 4, no. 20, pp. 181–189, 2012.
  26. Liang-qun and X. Wei-xin, “Intuitionistic fuzzy joint probabilistic data association filter and its application to multitarget tracking,” Signal Processing, vol. 96, pp. 433–444, 2014.
  27. Li and W. Xie, “Bearings-only maneuvering target tracking based on fuzzy clustering in a cluttered environment,” AEU - Int. J. Electron. Commun., vol. 68, no. 2, pp. 130–137, 2014.
  28. M. Aziz, “A new adaptive decentralized soft decision combining rule for distributed sensor systems with data fusion,” Inf. Sci. (Ny)., vol. 256, pp. 197–210, 2014.
  29. Y. Zhang, L. Q. Li, & S. Huang,”Multiple target data-association algorithm based on Takagi–Sugeno intuitionistic fuzzy model,”, Neurocomputing, vol. 536, pp.114-124, 2023
  30. Wang, and Z. Qin. “Multi-target tracking method based on FCM clustering,” In 2021 International Conference on Big Data, Artificial Intelligence and Risk Management (ICBAR), pp. 127-130. IEEE, 2021.
  31. Oussalah and J. De Schutter, “Hybrid fuzzy probabilistic data association filter and joint probabilistic data association filter,” Inf. Sci. (Ny)., vol. 142, no. 1–4, 195–226, 2002.
  32. Ristic, S. Arulampalam, and N. Gordon, “Beyond the Kalman filter,” IEEE Aerosp. Electron. Syst. Mag., vol. 19, no. 7, pp. 37–38, 2004.
  33. Guo, X. Yuan, and C. Han, “Sensor selection based on maximum entropy fuzzy clustering for target tracking in large-scale sensor networks,” IET Signal Process., vol. 11, no. 5, pp. 613–621, 2017.
  34. Khaloozadeh and A. Karsaz, “Modified input estimation technique for tracking manoeuvring targets,” IET radar, sonar Navig., vol. 3, no. 1, pp. 30–41, 2009.
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