Separated covariance filtering

Author

Portmann, G.J. ; Moore, J.R. ; Bath, W.G.

Author_Institution

Appl. Phys. Lab., Johns Hopkins Univ., Laurel, MD, USA

fYear

1990

fDate

7-10 May 1990

Firstpage

456

Lastpage

460

Abstract

A separated covariance filter (SCF) for estimating position and velocity given noisy measurements is discussed. The SCF calculates the state errors due to measurement errors and those due to target acceleration separately. The SCF overcomes two difficulties with the standard Kalman filter technique: (1) selection of the process noise covariance; and (2) interpreting the Kalman state covariance. The SCF is adaptable to a stream of unsequenced measurements

Keywords

filtering and prediction theory; measurement errors; radar theory; measurement errors; noisy measurements; position estimation; separated covariance filter; separated covariance filtering; state errors; target acceleration; unsequenced measurements; velocity estimation; Acceleration; Accelerometers; Adaptive filters; Covariance matrix; Measurement errors; Position measurement; Predictive models; State estimation; Steady-state; Trajectory;

fLanguage

English

Publisher

ieee

Conference_Titel

Radar Conference, 1990., Record of the IEEE 1990 International

Conference_Location

Arlington, VA

Type

conf

DOI

10.1109/RADAR.1990.201208

Filename

201208

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3048851