Currently I have a system that measures the GPS coordinates of an object. The object is first detected and then using trigonometry, the GPS coordinates are determined, as we know of the GPS coordinates of the camera itself.
However, the camera is on a moving object and therefore the data for the GPS coordinates can be quite noisy. In order to tackle this, I have decided to use an EKF in accordance with the system at hand.
In order to integrate this system into the program that I am currently using, I have decided to use this library here: https://github.com/simondlevy/TinyEKF. However, here it says that
TinyEKF requires you to write only a single model function, filling in the values of the state-transition function f(x), its Jacobian matrix F(x), the sensor function h(x), and its Jacobian H(x). The prediction and update then handled automatically by passing the observation vector z to the step function.
The output of my GPS code, is always
[lat, lon]. What is
x and what is
z here? I don't understand how to create a state transition matrix. however, I am aware that a jacobian can be calculated from the state transition matrix, if I know what it is. Also, what is the