1. Is There a Professional Kalman Filter Design Tool?
The Kalman filtering technique rapidly developed in recent decades. It's widely used in many
areas such as Aerospace, Earthquake monitoring, Economic trending Control and Inertial
navigation. Not like other filters, the Kalman filtering is suitable for multi-input and multi-output
system. It provides the most optimal filtering and estimation based on mathematical model
describing the system.
A classic example of Kalman filtering application is to
predict the position and velocity of the object from a
set of limited observation sequences containing the
noise. It can be found in many engineering applications
such as Radar, Computer vision. Meanwhile, it is an
important topic in control theory and control system
engineering.
For example, people are interested in tracking targets in radar, but the measurements of the
target position, velocity and acceleration contain noise at all times. The Kalman filter removes
the noise and gets a good estimate of the target location by the dynamic target informations.
This estimates maybe the current target position estimates(filtering), as well as the estimates of
the future(projections). It can also be estimated location of the past (interpolation or smoothing).
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People often confused with the complex formulas of Kalman filtering. They are eager to find a
simple way to achieve the operation. Visual Kalman Filter is a nice tool for training and simulation.
It meets the needs of many beginners.
Visual Kalman Filter is developed for science researchers based on visual windows interface. It
helps people to deal with the dynamic data, and draw predictions and graphics. Users need not to
write any code(Such as Matlab, C++, etc.). Users should build the system model first, and get the
matrice of the model. That is, you'd better input the matrice A,B,P0,Z,Q,R,etc. in the base
equations as follow: X(k) = AX(k-1) + BU(k) + W(k) Z(k) = HX(k) + V(k) In the two formulas, X(k)
is the system state, U(k) is the amount of system control. Z(k) is the measured value, and W(k)
and V(k) represent process and measurement noise, which are assumed as Gaussian white noise.
After inputting the system matrix parameters, click 'step 3', users will get the results such as

2. X(k|k-1),P(k|k-1),X(k|k) and P(k|k). When users click the strings in the listbox, the results and
curve will appear. Then just save the results.
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