© The Institution of Engineering and Technology
Infinite impulse response (IIR) filter generally used in estimation, positioning, etc. has problems in that, when there is a modeling error even if the system is completely observable, the estimates may converge to incorrect values or the varying values cannot be estimated quickly. The finite impulse response (FIR) filter, which has been investigated as a method to solve this problem, has faster estimation performance than the IIR filter in the presence of modelling error, but there is a limit to the convergence characteristic of the state variables. In this paper, a nonlinear FIR smoothing (NFS) filter is proposed to overcome the limitation of the state variable convergence characteristic of the FIR filter. The proposed NFS filter adds the smoothing concept to the modified receding horizon Kalman FIR filter. In order to verify the performance of the NFS filter, this filter is applied to a delaytolerant integrated navigation system using magnetic compass (MC) of which error is difficult to be modelled accurately. If an unmodeled jump or ramp error occurs in the MC measurement, it shows that the NFS filter can estimate the measurement error more accurately than the FIR filter as well as the IIR filter through a simulation.
References


1)

1. Cho, S.Y., Lee, H. K.: ‘Modified RHKF filter for improved DR/GPS navigation against uncertain model dynamics’, ETRI J., 2012, 34, (3), pp. 379–387.

2)

2. Kwon, W.H., Kim, P.S., Han, S.H.: ‘A receding horizon unbiased FIR filter for discretetime state space models’, Automatica, 2002, 38, (3), pp. 545–551.

3)

3. PomaricoFranquiz, J.J., Shmaliy, Y.S.: ‘Accurate selflocalization in RFID tag information grids using FIR filtering’, IEEE Trans. Ind. Inf., 2014, 10, (2), pp. 1317–1326.

4)

4. Kim, P.S.: ‘An alternative FIR filter for state estimation in discretetime systems’, Digit. Signal Process., 2010, 20, (3), pp. 935–943.

5)

5. Pak, J.M, Ahn, C.K., Lim, M.T., et al: ‘Horizon group shift FIR filter: alternative nonlinear filter using finite recent measurement’, Measurement, 2014, 57, pp. 33–45.

6)

6. Cho, S.Y., Kim, B.D.: ‘Adaptive IIR/FIR fusion filter and its application to the INS/GPS integrated systems’, Automatica, 2008, 44, (8), pp. 2040–2047.

7)

7. Ling, K.V., Lim, K.W.: ‘Receding horizon recursive state estimation’, IEEE Trans. Autom. Control, 1999, 44, (9), pp. 1750–1753.

8)

8. Michalska, M., Mayne, D.Q.: ‘Moving horizon observers and observer based control’, IEEE Trans. Autom. Control, 1995, 40, (6), pp. 995–1006.

9)

9. Trecate, G.F., Mignone, D., Morari, M.: ‘Moving horizon estimation for hybrid systems’, IEEE Trans. Automatic Control, 2002, 47, (10), pp. 1663–1676.

10)

10. Brown, R.G., Hwang, P.Y.C.: ‘Introduction to random signals and applied kalman Filtering’ (John Wiley & Sons, NJ, 2012).

11)

11. Crouse, D.F., Willett, P., BarShalom, Y.: ‘A lowcomplexity slidingwindow kalman FIR smoother for discretetime models’, IEEE Signal Process. Lett., 2010, 17, (2), pp. 177–180.

12)

12. Han, S.H., Kwon, B.K., Kwon, W.H.: ‘Minimax FIR smoothers for deterministic continuoustime state space models’, Automatica, 2009, 45, (6), pp. 1561–1566.

13)

13. Kwon, B.K., Han, S.K., Han, S.H.: ‘A finite impulse response fixedlag smoothing for discretetime nonlinear systems’, J. Inst. Control, Robotics Syst., 2015, 21, (9), pp. 807–810.

14)

14. Yu, J., Lee, J.G., Park, C.G., et al: ‘An offline navigation of a geometry PIG using a modified nonlinear fixedinterval smoothing filter’, Control Eng. Pract., 2005, 13, (11), pp. 1403–1411.

15)

15. Lee, S.Y., Choi, K.H., Joo, I.H., et al: ‘Design and implementation for 4Svan: a mobile mapping system’, ETRI J., 2006, 28, (3), pp. 265–274.

16)

16. Saab, S.S.: ‘A map matching approach for train positioning. Part I: development and analysis’, IEEE Trans. Veh. Technol., 2002, 49, (2), pp. 467–475.

17)

17. DurrantWhyte, H., Bailey, T.: ‘Simultaneous localization and mapping: part I’, IEEE Robot. Autom. Mag., 2006, 13, (2), pp. 99–110.

18)

18. Wang, W., Xie, G.: ‘Online highprecision probabilistic localization of robotic fish using visual and inertial cues’, IEEE Trans. Ind. Electron., 2015, 62, (2), pp. 1113–1124.

19)

19. Sebesta, K.D., Boizot, N.: ‘A realtime adaptive highgain EKF, applied to a quadcopter inertial navigation system’, IEEE Trans. Ind. Electron., 2014, 61, (1), pp. 495–503.

20)

20. Liu, H., Wang, X., Zhong, Y.: ‘Quaternionbased robust attitude control for uncertain robotic quadroters’, IEEE Trans. Ind. Inf., 2015, 11, (2), pp. 406–415.

21)

21. Zhang, Y., Chong, K.T.: ‘A GPS/DR data fusion method based on the GPS characteristics for mobile robot navigation’, Int. J. Control Autom., 2014, 7, (10), pp. 119–132.

22)

22. Zhang, H., Zhao, Y.: ‘The performance comparison and analysis of extended Kalman filters for GPS/DR navigation’, OptikInt. J. Light Electron. Optics, 2011, 122, (9), pp. 777–781.

23)

23. Yang, L., Li, Y., Wu, Y., et al: ‘An enhanced MEMSINS/NSS integrated system with fault detection and exclusion capability for land vehicle navigation in urban areas’, GPS Solutions, 2014, 18, (4), pp. 593–603.

24)

24. Farrell, J.A., Barth, M.: ‘The global positioning system & inertial navigation’ (McGrawHill, New York, 1999).
http://iet.metastore.ingenta.com/content/journals/10.1049/ietrsn.2017.0551
Related content
content/journals/10.1049/ietrsn.2017.0551
pub_keyword,iet_inspecKeyword,pub_concept
6
6