Multi-model adaptive Kalman filter design for manoeuvring target tracking

Autor:	T. Y. Lin, H. P. Wang, M. C. Kung
Rok vydání:	1994
Předmět:	Engineering business.industry Covariance intersection Kalman filter Computer Science Applications Theoretical Computer Science Adaptive filter Extended Kalman filter Noise Control and Systems Engineering Control theory Median filter Kernel adaptive filter Ensemble Kalman filter business
Zdroj:	International Journal of Systems Science. 25:2039-2046
ISSN:	1464-5319 0020-7721
DOI:	10.1080/00207729408949333
Popis:	A new method of increasing manoeuvring target tracking performance is presented. The target manoeuvring is modelled as process white gaussian noise so that the system equations to which optimal filtering theory applies can be obtained. A multi-model adaptive Kalman filter is developed to estimate the target positions, based on process noise covariance matching and multi-model measurement noise. In the conventional adaptive filter, only one of process noise and measurement noise covariance matching can be done in a program. In this algorithm, the tracking filter estimates the covariance of process noise and measurement noise simultaneously to increase the accuracy of the conventional real-time tracking filter. Finally, an illustrative numerical example is included.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::20e8b9a73c3870e6a7928dd830294d9f https://doi.org/10.1080/00207729408949333 Zobrazit plný text záznamu