Set value-based dynamic model development for non-linear manoeuvring target tracking problem in the presence of unknown but bounded disturbances

Mohammad Ali Alirezapouri; Hamid Khaloozadeh; Ahmadreza Vali; Mohammad Reza Arvan

Set value-based dynamic model development for non-linear manoeuvring target tracking problem in the presence of unknown but bounded disturbances

View Fulltext

Author(s): Mohammad Ali Alirezapouri¹ ; Hamid Khaloozadeh² ; Ahmadreza Vali¹ ; Mohammad Reza Arvan¹
- Affiliations: 1: Department of Control, Faculty of Electrical Engineering , Malek-Ashtar University of Technology , Tehran , Iran ;
  2: Department of Systems and Control , Faculty of Electrical Engineering, Industrial Control Center of Excellence, K.N. Toosi University of Technology , Tehran , Iran
Source: Volume 12, Issue 2, February 2018, p. 186 – 194
DOI: 10.1049/iet-rsn.2017.0293 , Print ISSN 1751-8784, Online ISSN 1751-8792

Received 07/07/2017, Accepted 09/10/2017, Revised 08/10/2017, Published 11/10/2017

The stochastic models developed for the manoeuvring target tracking (MTT) problem do not exhibit good performance owing to the lack of sufficient information about uncertainties. Moreover, each of the MTT models can be used for limited target manoeuvres. An ‘unknown but bounded’ (UBB)-based non-linear dynamic model for modelling time-dependent acceleration and model uncertainties is proposed for addressing the aforementioned problems. In the UBB approach, uncertainties and target manoeuvre are modelled using information about their upper bounds only. The extended set membership filter can be used to estimate the target states and manoeuvre with this new dynamic model. In order to examine the stability of the proposed method, the input to state stability of the estimation error is analysed. Very good performance is achieved by the proposed dynamic model without precise information about the model uncertainties. The proposed MTT algorithm can be used to track both non-manoeuvring and manoeuvring targets. The theoretical development is verified using simulations and the results are compared with those of other MTT methods. These comparisons demonstrate that the performance of the proposed method is better.

References

1. 1)
  - 24. Alirezapouri, M.A., Khaloozadeh, H., Vali, A.R., et al: ‘Unknown but bounded based input estimation method for maneuvering target tracking problem’. Iranian Conf. Electrical Engineering (ICEE), 2017.
2. 2)
  - 14. Blom, H.A.P., Bar-Shalom, Y.: ‘The interacting multiple model algorithm for systems with Markovian switching coefficients’, IEEE Trans. Autom. Control, 1988, 33, (8), pp. 780–783.
3. 3)
  - 21. Rahmati, H., Khaloozadeh, H., Ayati, M.: ‘Maneuvering target tracking method based on unknown but bounded uncertainties’, IFAC Proc. Vol., 2011, 44, (1), pp. 4290–4295.
4. 4)
  - 27. Anderson, B.D.O., Moore, J.B.: ‘Detectability and stabilizability of time-varying discrete-time linear systems’, SIAM J. Control Optim., 1981, 19, (1), pp. 20–32.
5. 5)
  - 28. Jiang, Z.-P., Wang, Y.: ‘Input-to-state stability for discrete-time nonlinear systems’, Automatica, 2001, 37, (6), pp. 857–869.
6. 6)
  - 10. Li, X.R., Jilkov, V.P.: ‘Survey of maneuvering target tracking. Part I. Dynamic models’, IEEE Trans. Aerosp. Electron. Syst., 2003, 39, (4), pp. 1333–1364.
7. 7)
  - 16. Mehrotra, K., Mahapatra, P.R.: ‘A jerk model for tracking highly maneuvering targets’, IEEE Trans. Aerosp. Electron. Syst., 1997, 33, (4), pp. 1094–1105.
8. 8)
  - 3. Hungu, L., Min-Jea, T.: ‘Generalized input-estimation technique for tracking maneuvering targets’, IEEE Trans. Aerosp. Electron. Syst., 1999, 35, (4), pp. 1388–1402.
9. 9)
  - 23. Liu, Y., Zhao, Y.: ‘Ellipsoidal set filter combined set-membership and statistics uncertainties for bearing-only maneuvering target tracking’. IEEE/ION Position, Location and Navigation Symposium – PLANS 2014, 2014.
10. 10)
  - 18. Dahmani, M., Meche, A., Keche, M., et al: ‘An improved fuzzy alpha-beta filter for tracking a highly maneuvering target’, Aerosp. Sci. Technol., 2016, 58, pp. 298–305.
11. 11)
  - 17. Mahapatra, P.R., Mehrotra, K.: ‘Mixed coordinate tracking of generalized maneuvering targets using acceleration and jerk models’, IEEE Trans. Aerosp. Electron. Syst., 2000, 36, (3), pp. 992–1000.
12. 12)
  - 5. Kirubarajan, T., Bar-Shalom, Y., Pattipati, K.R., et al: ‘Ground target tracking with variable structure IMM estimator’, IEEE Trans. Aerosp. Electron. Syst., 2000, 36, (1), pp. 26–46.
13. 13)
  - 11. Bilik, I., Tabrikian, J.: ‘Maneuvering target tracking in the presence of glint using the nonlinear Gaussian mixture Kalman filter’, IEEE Trans. Aerosp. Electron. Syst., 2010, 46, (1), pp. 246–262.
14. 14)
  - 13. Singer, R., Sea, R., Housewright, K.: ‘Derivation and evaluation of improved tracking filter for use in dense multitarget environments’, IEEE Trans. Inf. Theory, 1974, 20, (4), pp. 423–432.
15. 15)
  - 29. Khaloozadeh, H., Karsaz, A.: ‘Modified input estimation technique for tracking manoeuvring targets’, IET Radar Sonar Navig., 2009, 3, (1), pp. 30–41.
16. 16)
  - 8. Li, X.R., Jilkov, V.P.: ‘Survey of maneuvering target tracking. Part V. Multiple-model methods’, IEEE Trans. Aerosp. Electron. Syst., 2005, 41, (4), pp. 1255–1321.
17. 17)
  - 22. Qiu, Q., Zheng, W., Han, J.: ‘Determining the feasible set of motion states in target tracking using extended set-membership filter’. Proc. 10th World Congress on Intelligent Control and Automation, 2012.
18. 18)
  - 12. Bar-Shalom, Y., Birmiwal, K.: ‘Variable dimension filter for maneuvering target tracking’, IEEE Trans. Aerosp. Electron. Syst., 1982, AES-18, (5), pp. 621–629.
19. 19)
  - 20. Fletcher, F.K., Arulampalam, M.S., Evans, R.J., et al: ‘Ellipsoidal set based tracking with nonlinear measurements’, IEE Proc. Radar Sonar Navig., 2005, 152, (5), pp. 335–344.
20. 20)
  - 4. Ick Ho, W., Jang Gyu, L., Tae Kyung, S.: ‘Modified input estimation technique using pseudoresiduals’, IEEE Trans. Aerosp. Electron. Syst., 1994, 30, (1), pp. 220–228.
21. 21)
  - 19. Schweppe, F.C.: ‘Uncertain dynamic systems’ (Prentice-Hall, Englewood Cliffs, N.J, 1973).
22. 22)
  - 25. Liu, Y., Zhao, Y., Wu, F.: ‘Ellipsoidal state-bounding-based set-membership estimation for linear system with unknown-but-bounded disturbances’, IET Control Theory Applic., 2016, 10, (4), pp. 431–442.
23. 23)
  - 26. Scholte, E., Campbell, M.E.: ‘A nonlinear set-membership filter for on-line applications’, Int. J. Robust Nonlinear Control, 2003, 13, (15), pp. 1337–1358.
24. 24)
  - 2. Karsaz, A., Khaloozadeh, H.: ‘An optimal two-stage algorithm for highly maneuvering targets tracking’, Signal Process., 2009, 89, (4), pp. 532–547.
25. 25)
  - 9. Wu, W., Cao, P., Pan, Z.D.: ‘Maneuver target tracking based on Kalman IMM algorithm’, in Xie, A., Huang, X. (Eds.): ‘Advances in electrical engineering and automation’ (Springer, Berlin Heidelberg, 2012).
26. 26)
  - 6. Li, X.R., Youmin, Z.: ‘Multiple-model estimation with variable structure. V. likely-model set algorithm’, IEEE Trans. Aerosp. Electron. Syst., 2000, 36, (2), pp. 448–466.
27. 27)
  - 7. Lee, B.J., Park, J.B., Joo, Y.H., et al: ‘Intelligent Kalman filter for tracking a manoeuvring target’, IEE Proc. Radar Sonar Navig., 2004, 151, (6), pp. 344–350.
28. 28)
  - 1. McIntyre, G.A., Hintz, K.J.: ‘Comparison of several maneuvering target tracking models’, in Aerospace/Defense Sensing and Controls, (SPIE, 1998).
29. 29)
  - 15. Mazor, E., Averbuch, A., Bar-Shalom, Y., et al: ‘Interacting multiple model methods in target tracking: a survey’, IEEE Trans. Aerosp. Electron. Syst., 1998, 34, (1), pp. 103–123.

Login

Not registered yet?

Share

Tools

Login to add to favourites

Key

Set value-based dynamic model development for non-linear manoeuvring target tracking problem in the presence of unknown but bounded disturbances

References

Related content