Error-space estimate method for generalized synergic target tracking

Ming CEN, Chengyu FU, Ke CHEN, Xingfa LIU

PDF(128 KB)
PDF(128 KB)
Front. Electr. Electron. Eng. ›› 2009, Vol. 4 ›› Issue (1) : 88-92. DOI: 10.1007/s11460-008-0082-7
Research Article
Research Article

Error-space estimate method for generalized synergic target tracking

Author information +
History +

Abstract

To improve the tracking accuracy and stability of an optic-electronic target tracking system, the concept of generalized synergic target and an algorithm named error-space estimate method is presented. In this algorithm, the motion of target is described by guide data and guide errors, and then the maneuver of the target is separated into guide data and guide errors to reduce the maneuver level. Then state estimate is implemented in target state-space and error-space respectively, and the prediction data of target position are acquired by synthesizing the filtering data from target state-space according to kinematic model and the prediction data from error-space according to guide error model. Differing from typical multi-model method, the kinematic and guide error models work concurrently rather than switch between models. Experiment results show that the performance of the algorithm is better than Kalman filter and strong tracking filter at the same maneuver level.

Keywords

target tracking / generalized synergic target / position prediction / error-space estimate

Cite this article

Download citation ▾
Ming CEN, Chengyu FU, Ke CHEN, Xingfa LIU. Error-space estimate method for generalized synergic target tracking. Front Elect Electr Eng Chin, 2009, 4(1): 88‒92 https://doi.org/10.1007/s11460-008-0082-7

References

[1]
WangZ Y, XuZ Y. Study on stable tracking target with single photoelectric theodolite. Opto-electronic Engineering, 2003, 30(2): 11–14(in Chinese)
[2]
ZhouH R, JingZ L, WangP D. Tracking of Maneuvering Targets. Beijing: National Defence Industry Press, 1991(in Chinese)
[3]
GaoX, WangJ G. Moving targets detection using a single SAR sensor. Experiment Science and Technology, 2005, 3(4): 33–34(in Chinese)
[4]
JiC X, XuJ H, ChenK. Development of multiple-model algorithm for tracking maneuvering target. Systems Engineering and Electronics, 2003, 25(7): 882–885(in Chinese)
[5]
MazorE, AverbuchA, Bar-ShalomY. Interacting multiple model methods in target tracking: a survey. IEEE Transactions on Aerospace and Electronics Systems, 1998, 34(1): 103–123
CrossRef Google scholar
[6]
GanQ, HarrisC J. Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion. IEEE Transactions on Aerospace and Electronic Systems, 2001, 37(1): 273–279
CrossRef Google scholar
[7]
HeY, WangG H, LuD J, . Multisensor Information Fusion with Applications. Beijing: Publishing House of Electronics Industry, 2000(in Chinese)
[8]
GuoS L. Random Control. Beijing: Tsinghua University Press, 1999(in Chinese)
[9]
ZhouD H, YeY Z. Modern Fault Diagnosis and Fault Tolerant Control. Beijing: Tsinghua University Press, 2000(in Chinese)

Acknowledgements

This work was supported by the Hi-Tech Research and Development Program of China.

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg
PDF(128 KB)

Accesses

Citations

Detail

Sections
Recommended

/