A SVD Based SRUKF Algorithm of Single Observer Passive Location

Author

Song Hailiang ; Liu Xue ; Fu Yongqing

Author_Institution

Coll. of Inf. & Commun. Eng., Harbin Eng. Univ., Harbin, China

fYear

2011

Firstpage

1

Lastpage

4

Abstract

A square root unscented kalman filter algorithm based on singular valued decomposition is presented to enhance the robustness in the single observer passive location. The Cholesky decomposition or update is replaced by singular value decomposition, so the new method solves the unstable problem of SRUKF (Square Root unscented kalman filter) which is caused by covariance matrix morbidity in strong nonlinear cases. The simulation results show that the filtering algorithm of SVD-SRUKF proposed in this paper has higher stability and accuracy than any other similar algorithm.

Keywords

Kalman filters; covariance matrices; filtering theory; singular value decomposition; Cholesky decomposition; SVD based SRUKF algorithm; covariance matrix morbidity; single observer passive location; singular valued decomposition; square root unscented Kalman filter algorithm; Accuracy; Convergence; Filtering algorithms; Observatories; Observers; Singular value decomposition; Stability analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Wireless Communications, Networking and Mobile Computing (WiCOM), 2011 7th International Conference on

Conference_Location

Wuhan

ISSN

2161-9646

Print_ISBN

978-1-4244-6250-6

Type

conf

DOI

10.1109/wicom.2011.6040435

Filename

6040435