dsp@myallit.com wrote:> On Jul 21, 5:35 pm, "Bruno Luong" <b.lu...@fogale.fr> wrote: >> d...@myallit.com wrote in message >> >> You might consider Extended Kalman filtering (EKF). Be aware >> about the eventual non-stability of the scheme. >> > > What do you mean by the eventual non-stability? I did look at the EKF, > there is some simple sample MATLAB code here: > http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=18189 > > But in the first few lines of this script it says: > > % for nonlinear dynamic system: > % x_k+1 = f(x_k) + w_k > % z_k = h(x_k) + v_k > % where w ~ N(0,Q) meaning w is gaussian noise with covariance Q > % v ~ N(0,R) meaning v is gaussian noise with covariance R > > so the EKF looks appropriate for non-linear process models and > measurement models that can be represented by any arbitrary functions > f(x) and h(x), but the noise is still assumed to be additive.He means that it's a nonlinear system, and therefore you can't assume that just because it's locally stable around some state that it's globally stable for any state. In particular, there may be input vectors that will drive it into a limit cycle or off toward infinity, either permanently or temporarily. Contrary to the beliefs of some, the MathWorks isn't on high, and MatLab isn't an extension of the Bible. I would trust them for examples, but don't take their documentation for anything but a means of selling you copies of MatLab. -- Tim Wescott Wescott Design Services http://www.wescottdesign.com Do you need to implement control loops in software? "Applied Control Theory for Embedded Systems" gives you just what it says. See details at http://www.wescottdesign.com/actfes/actfes.html
Re: Kalman filtering with multiplicative noise
Started by ●July 21, 2008
