DISPERSION DISTORTION TRACKING COMPENSATOR BASED ON THE SIGMA-POINT KALMAN

Abstract

The article proposes an algorithm for nonlinear optimal filtering of the dispersion characteristic slope of the ionospheric channel (DC slope) based on the Kalman sigma-point filter when sounding the channel with broadband signals. The relevance of the applied algorithm can be explained by the fact that at present in the modern world the relevance of the transmission of broadband information signals in the decameter range has increased. The reception of such signals is complicated by the effects caused by the influence of the frequency dispersion of the ionospheric channel on the signal. The above influence leads to significant distortions of the useful signal and a decrease in the quality of information reception in general. The above influence leads to significant distortions of the useful signal and a decrease in the quality of information reception in general. Therefore, the effect of frequency dispersion cannot be neglected and must be taken into account when developing algorithms for optimal reception. Thus, estimation of the parameters of the frequency dispersion of the ionospheric channel becomes an urgent task. The article provides recurrent formulas for calculating the optimal filtration of the DC slope. Moreover, an algorithm for joint estimation of dispersion distortions and their compensation is presented, which allows improving the quality of estimation. The results of simulation modeling of the algorithms are given: the obtained estimate and the true value of the frequency dispersion parameters for two cases – a Markov damped process and a process with random increments. The absolute error in estimating the DC slope is also considered. The computational efficiency of the algorithm for estimating the DC slope using the Kalman sigma-point filter with and without compensation is calculated. In addition, functional diagrams of optimal filtering of the DC slope with compensation and without compensation are given. The ways of implementing the proposed algorithm on an ARM processor are considered.