JOURNAL BROWSE
Search
Advanced SearchSearch Tips
An Extended Scalar Adaptive Filter for Mitigating Sudden Abnormal Signals of Guided Missile
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
An Extended Scalar Adaptive Filter for Mitigating Sudden Abnormal Signals of Guided Missile
Lim, Jun-Kyu; Park, Chan-Gook;
  PDF(new window)
 Abstract
An extended scalar adaptive filter for guided missiles using a global positioning system receiver is presented. A conventional scalar adaptive filter is adequate filter for eliminating sudden abnormal jumping measurements. However, if missile or vehicle velocities have variation, the conventional filter cannot eliminate abnormal measurements. The proposed filter utilizes an acceleration term, which is an improvement not used in previous conventional scalar adaptive filters. The proposed filter continuously estimates noise measurement variance, velocity error variance and acceleration error variance. For estimating the three variances, an innovation method was used in combination with the least square method for the three variances. Results from the simulations indicated that the proposed filter exhibited better position accuracy than the conventional scalar adaptive filter.
 Keywords
Scalar filter;Adaptive method;Global positioning system;
 Language
English
 Cited by
 References
1.
Aggarwal, C. C. and Yu, P. S. (2001). Outlier detection for high dimensional data. Proceedings of the ACM SIGMOD International Conference on Management of Data, Santa Barbara, CA. pp. 37-46.

2.
Brown, R. G. and Hwang, P. Y. C. (1997). Introduction to Random Signals and Applied Kalman Filtering: with MATLAB Exercises and Solutions. 3rd ed. New York: Wiley.

3.
Cho, Y., Bang, H., and Tahk, M. (2002). Scalar adaptive Kalman filtering for stellar inertial attitude determination. KSAS International Journal, 3, 88-94.

4.
Hautamaki, V., Karkkainen, I., and Franti, P. (2004). Outlier detection using k-nearest neighbour graph. Proceedings of the 17th International Conference on Pattern Recognition, Cambridge, UK. pp. 430-433.

5.
Lee, T., Kim, K., and Park, H. (2001). A study on the standalone GPS jump error smoothing scheme. Journal of Control, Automation and Systems Engineering, 7, 1015-1023.

6.
Maybeck, P. S. (1979). Stochastic Models, Estimation and Control. New York: Academic Press.

7.
Mehra, R. K. (1972). Approaches to adaptive filtering. IEEE Transactions on Automatic Control, AC-17, 693-698.

8.
Ramaswamy, S., Rastogi, R., and Shim, K. (2000). Efficient algorithms for mining outliers from large data sets. SIGMOD Record, 29, 427-438. crossref(new window)

9.
Salychev, O. (1998). Inertial Systems in Navigation and Geophysics. Bauman: MSTU Press.