estimation of the position of the magnetic source I have a flying robot with magnetic coil as a sensor. An output from the coil is measured every second in different position. I know the position of the coil and its angles. I need to estimate the unknown position of magnetic source.

As the output is with noise I assume I need to use Kalman filter, but not sure how to start in this case. Do you know if i can implement Kalman to estimate the position of the source? Do not really know how to start. Did anyone know similar example described somewhere?

Thanks for all advice.
To start, you have to write a probabilistic model of your system. Let us denote, your outputs (observations) as $\bf{y}_t$'s and the true positions as $\bf{x}_t$'s. To use Kalman filter, you have to be sure that underlying noise is Gaussian and your observation and transition models (I will explain in the sequel) are linear.

First, write a reasonable observation model such that,
$$ \mathbf{y}_t = H_t \mathbf{x}_t + \mathbf{w}_t $$

In this model, you aim to model how your true positions are corrupted and become observations. Thus you have to determine $H_t$. For example, if you are using a camera and want to know 3D positions, then observation model is a camera matrix which projects the real world points to 2D points. In your case, you have to know sensor characteristics (or model it).

Second, you have to write a transition model, this requires to use physical knowledge of the underlying system. For instance, if we model a ball and try to model its evolution in time, certainly we have to write a motion model. Transition model can be written as, 

$$ \mathbf{x}_t = F_t \mathbf{x}_{t-1} + \mathbf{\eta}_t $$

Notice that, in both models, the noise terms are Gaussian and $H$ and $F$ are linear. To use Kalman filter, you have to know these models, i.e., you should have a probabilistic model which explains the underlying physical structure. Then you can use the Kalman filtering recursions, which are straightforward from this point.