Mirel Vasiliu

 

Top Tags Applied by Mirel	1 - 1 of 1
basic kalman filter

Files Tagged by Mirel					1 - 1 of 1
Updated		File	Tags	Downloads (last 30 days)	Comments	Rating
12 Oct 2005		Learning the Kalman Filter Basic Kalman filter, heavily commented, for beginners to Kalman filtering. Author: Michael Kleder	basic kalman filter, kalman filter, discrete filter, linear, imtransform, filter analysis	666	90	4.44048 4.4 | 85 ratings

Comments and Ratings Matching Mirel's Watch List			1-5 of 123
Date	File	Comment by	Comments	Rating
13 Mar 2012	Learning the Kalman Filter Basic Kalman filter, heavily commented, for beginners to Kalman filtering.	Carolina Brum Medeiros	helpful and clear comments	5
25 Feb 2012	Learning the Kalman Filter Basic Kalman filter, heavily commented, for beginners to Kalman filtering.	Roja e	Thanks..but an error occurred while running this demo. Input argument "s" is undefined help me please	3
18 Feb 2012	Learning the Kalman Filter Basic Kalman filter, heavily commented, for beginners to Kalman filtering.	chao		5
11 Feb 2012	Learning the Kalman Filter Basic Kalman filter, heavily commented, for beginners to Kalman filtering.	Ruban Sugumar	Nice Explanation.. Really Helpful..	5
05 Aug 2011	Learning the Kalman Filter Basic Kalman filter, heavily commented, for beginners to Kalman filtering.	greg	Nicely documented. As a practicing engineer I would never use the implementation shown. This is the standard covariance form of the Kalman filter. In operation the statement s.P = s.P - K*s.H*s.P; causes significant issues. s.P needs to always be positive definite but with rounding this will tend to violate this assmption making the Kalman filter 'blow up' over time or with poorly conditioned data. The alternative is to process in the square root domain where the P matrix is expressed as a P=Psr'*Psr. Therefore the resuting matrix must always be positive definite and does not have this issue. An added advantage is the precision is doubled processing in this domain and can be similar to the input data resolution rather than 2x the number of bits to provide comparable precision. For more information see 'Linear estimation' Kailath, Sayed, Hassibi or 'adaptive filter thoery' Haykin IMHO one should always to filtering in the SR domain. There are also advantages of processing with separated real/imag data rather then complex if the underling data is complex.	Comment only