Document Type : Research Articles

Authors

1 Khorasan Istitute of Higher Education

2 Khorasan Institute of Higher Education, Department of Electrical Engineering, Mashhad, Iran.

Abstract

Abstract: A hybrid unknown input estimation based on a new two-sample backward model and data fusion for high maneuvering target tracking is proposed. This new approach is based on the consideration of more than one state and input components from the current single observation. These extracted state and input components would be augmented in a single vector, and the final estimation for unknown target acceleration will be determined. Using a combination of the new backward modeling and traditional modified input estimation (MIE) technique, more information will be extracted. This new hybrid scheme which using more input information can better estimate the target maneuvering structure. Despite the traditional methods, the proposed algorithm introduces two different strategies to state the input estimation including online and delayed estimation scenarios. Also, this paper suggests several different data fusion methods through these strategies. The results are compared with a typical MIE method to evaluate the performance of the proposed hybrid scheme especially for problems in high maneuvering target tracking. The results show that the backward algorithm makes advantages such as reduction of the transient state error and more stability for the estimation by an appropriate combination of the MIE estimator.

Keywords

Main Subjects

[1] Pachter, M.: ‘Kalman filtering when the large bandwidth
control is not known’, IEEE Trans. Aerosp. Electron. Syst.,
2012, 48, (1), pp. 542-551.
[2] Lee H., Tahk, M.J.: ‘Generalized input-estimation technique
for tracking maneuvering targets’, IEEE Trans. Aerosp.
Electron. Syst., 1999, 35, 4, pp.1388-1402.
[3] Chan, Y.T., Hu, A.G.C., Plant, J.B.: ‘A Kalman filter based
tracking scheme with input estimation’, IEEE Trans. Aerosp.
Electron. Syst., 1979, 15, (2), pp. 237–244.
[4] Khaloozadeh, H., Karsaz, A.: ‘Modified input estimation
technique for tracking manoeuvring Targets’, IET Radar Sonar
Navig., 2009, 3, (1), pp. 30–41.
[5] Sheng, H., Zhao, W., Wang, j.: ‘Interacting multiple model
tracking algorithm fusing input estimation and best linear
unbiased estimation filter’, IET Radar Sonar Navig., 2017, 11,
(1), pp. 70-77.
[6] Wang, T.C., Varshney, P.K.: ‘A tracking algorithm for
maneuvering targets’, IEEE Trans. Aerosp. Electron. Syst.,
1993, 29, (3), pp. 910–924.
[7] Whang, H.I., Lee, J.G., Sung, T.K.: ‘Modified input estimation
technique using pseudo residuals’, IEEE Trans. Aerosp.
Electron. Syst., 1994, 30, (1), pp. 220–228.
[8] Cloutier, J.R., Lin, C.F., Yang, C.: ‘Enhanced variable
dimension filter for maneuvering target tracking’, IEEE Trans.
Aerosp. Electron. Syst., 1993, 29, (3), pp. 786–797.
[9] Liang, Y., Zhou, D., Zhang, L., Pan, Q.: ‘Adaptive filtering for
stochastic systems with generalized disturbance inputs’, IEEE
Signal Process., Letters, 2008 15, pp. 645-648.
[10] Karsaz, A., Khaloozadeh, H.: ’An optimal two-stage algorithm
for highly maneuvering targets tracking’, Signal Process.,
2009, 89, (4), pp. 532–547.
[11] Bahari, M.H., Karsaz, A., Pariz, N.: ‘High maneuvering target
tracking using a novel hybrid Kalman filter-fuzzy logic
architecture’, Int. J. Innovative Computing, Information and
Cont., 2011, 7. (5), pp. 501-511.
[12] Malekian, H., Khaloozadeh, H.: ‘Extended input estimation
method for tracking non-linear manoeuvring targets with
multiplicative noises’ , IET Radar Sonar Navig., 2016, 10, (9),
pp. 1683-1690.
[13] Hu, X., Hu, Y.H., Xu, B.: ‘Generalised Kalman filter tracking
with multiplicative measurement noise in a wireless sensor
network’, IET Signal Process., 2014, 8, (5), pp. 467–474.
[14] Chung, Y.N., Juang, D.J., Hu, K.C., Li, M.L., Chuang, K.C.:
‘The dual-Kalman filtering and neural solutions to
maneuvering estimation problems’, J. Information Sci. and
Eng., 2010, 26, pp. 1479-1490.
[15] Lee, B.J., Park, J.B., Joo, Y.H., Jin, S.H.: ‘Intelligent Kalman
filter for tracking a manoeuvring target’, IEE Proc.-Radar
Sonar Navig., 2004, 151, (6), pp. 344-350.
[16] Lan, H., Liang, Y., Yang, F., Wang, Z., Pan, Q.: ‘Joint
estimation and identification for stochastic systems with
unknown inputs’, IET Control Theory Appl., 2013, 7, (10), pp.
1377–1386.
[17] Zhou, J., Liang, Y., Zhou, L., Quan, P.: ‘Joint estimation of
state and bias based on generalized systematic model’. Proc.
Int. Conf. on Control. China, July 2015, pp. 28-30.
[18] Mohammed, D., Mokhtar, K., Abdelaziz, O., Abdelkrim, M.:
‘A new IMM algorithm using fixed coefficients filters (fast
IMM)’, Int. J. Electron. Commun., 2010, 64, pp. 1123–1127.
[19] Jin, B., Jiu, B., Su, T., Liu, H., Liu, G.: ‘Switched Kalman
filter-interacting multiple model algorithm based on optimalautoregressive model for manoeuvring target tracking’, IET
Radar Sonar Navig., 2015, 9, (2), pp. 199-209.
[20] Zhu, L., Cheng, X.: ‘High manoeuvre target tracking in
coordinated turns,, IET Radar Sonar Navig., 2015, 9, (8), pp.
1078–1087.
[21] Lee, B.J., Park, J.B., Lee, H.J., Joo, Y.H.: ‘Fuzzy logic based
IMM algorithm for tracking a manoeuvring target’, IEE Proc.-
Radar Sonar Navig., 2005, 152, (1), pp. 16-22.
[22] Luo, X., Jiu, B., Chen, S.: ‘ML estimation of transition
probabilities for an unknown maneuvering emitter tracking’,
Signal Proc., 2015, 109, pp. 248–260, 2015.
[23] Foo, P.H., Ng, G.W.: ‘Combining the interacting multiple
model method with particle filters for manoeuvring target
tracking’, IET Radar Sonar Navig., 2011, 5, (3), pp. 234–255.
[24] Kim, T.H., Song, T.L., Kim, H.J.: ‘Information filters with
reduced data storage for out-of-sequence measurements
update’, IET Radar Sonar Navig., 2016, 10, (6), pp. 1038–
1045.
[25] Abdullah, R.S.A.R., Salah, A.A., Ismail, A., et al.:
‘Experimental investigation on target detection and tracking in
passive radar using long-term evolution signal’, IET Radar
Sonar Navig., 2016, 10, (3), pp. 577–585.
[26] Mušicki, D., Song, T.L., Lee, H.H., et al.: ‘Track-to-track
fusion with target existence’, IET Radar Sonar Navig., 2015, 9,
(3), pp. 241–248.
[27] Karszaz, A., Ahari, A.A.: ‘Backward input estimation
algorithm for tracking maneuvering target’. Proc. Conf.
Electrical Engineering, Shiraz, Iran, May 2016, pp. 745-750.
[28] Garcia, C.E., Prett, D.M. Morari, M.: ‘Model predictive
control: theory and practice – a survery”. Auotomatic, 1989, 25,
(3), pp. 335-348.
[29] Zitnik, M., Zupan, B.: ‘Data fusion by matrix factorization’,
IEEE Trans. On Pattern Analysis and Machine Intelligence,
2005, 37, (1), pp. 41-55.
[30] Zhou, L., Liang, Y., Zhou, j., Yang, F., Pan, Q.: ‘Linear
minimum mean squared estimation of measurement bias driven
by structured unknown inputs’, IET Radar Sonar Navig., 2014,
8, (8), pp. 977-986.
[31] George, D.E., Unnikrishnan, A.: ‘On the divergence of
information filter for multi sensors fusion’, J. Information
Fusion, 2016, 27, pp. 76-84.
[32] Ljung, L.: ‘System identification: theory for the user”,
(Prentice Hall, Englewood Cliffs, Nj, 1999).
[33] Anderson, S.R., Dean, P., Kadirkamanathan, V., Kaneko,C.R.S., Porrill, J.: ‘System identification from multiple short-
time-duration signals’, IEEE Trans. Biom. Eng., 2007, 54, (12),pp. 2205-2213.
[34] Song, E., Zhu, Y., Zhou, J., Zhisheng, Y.: ‘Optimal Kalman
filtering fusion with cross-correlated sensor noises’,
Automatica, 2007, 43, pp. 1450–1456.
[35] Ding, J., Xiao, J., Zhang, Y.: ‘Distributed input and state
estimation for non-linear discrete-time systems with direct
feedthrough’, IET Control Theory Appl., 2014, 8, (15), pp.
1543–15