Iterated extended Kalman filter is a tool in the theory of optimal estimation used for nonlinear problems. It minimizes variance of the estimation error in terms of probabilistic approach. Despite the special terminology, the Kalman filter algorithm minimizes the objective function, representing the squared difference between the measured vector and the calculated one for the parameters of selected model. It works like the least squares method – a conventional method for airborne electromagnetic data inversion. In this paper I describe the essence of the Kalman approach to solving inverse problems. I show, how one-dimensional inversion with vertical and lateral constraints can be performed in terms Kalman filter. The described algorithm takes into account the measurement noise, which is specified as the dispersion of signals in the corresponding measurement channels at high altitude. Special covariance matrix representation allows using corresponding Kalman filter calculation methods. They provide numerical stability of the algorithm. The Kalman approach makes it possible to combine modern techniques used in airborne survey data processing. I give an example of the Kalman filter use in the frequency-domain airborne data processing.