Visual Kalman Filter is a math tool that enables the user to simulate Kalman filter for linear or nonlinear systems. With an easy three-step process, users can obtain the curve and estimated results. Available at an affordable price of $49.95.
If you're looking for a mathematical tool to simulate Kalman filter for linear or nonlinear systems, 'Visual Kalman Filter' might just be what you need. This software is optimized to provide users with a visual method to estimate the state of a process, without requiring Matlab installations.
Using this software is a matter of simple steps. Users have two choices for their initial matrices. Either open an existent txt file edited beforehand, for example, from the folder 'Demos', included in the installed path. Or fill the matrices out manually and save them in a txt file at last.
At only $49.95, this software ensures accuracy and precision in the estimated results, providing both the curve and the estimated values. The Kalman filter function enables users to support estimations of past, present, and even future states, even when the modeled system's nature is unknown.
The filter minimizes the squared error's mean, thus providing an efficient computational means to estimate computational systems' states. Finally, if you click the state you want to observe, the figure and the values of the estimated result will appear immediately. Give 'Visual Kalman Filter' a try and witness the efficiency and ease it provides!
Using this software is a matter of simple steps. Users have two choices for their initial matrices. Either open an existent txt file edited beforehand, for example, from the folder 'Demos', included in the installed path. Or fill the matrices out manually and save them in a txt file at last.
At only $49.95, this software ensures accuracy and precision in the estimated results, providing both the curve and the estimated values. The Kalman filter function enables users to support estimations of past, present, and even future states, even when the modeled system's nature is unknown.
The filter minimizes the squared error's mean, thus providing an efficient computational means to estimate computational systems' states. Finally, if you click the state you want to observe, the figure and the values of the estimated result will appear immediately. Give 'Visual Kalman Filter' a try and witness the efficiency and ease it provides!
What's New
Version 4.0: update to 4.0
Version 3.0: update to 3.0