Optimal algorithm for distinguishing radio navigation signals by a set of “spaced” correlators

Abstract

The classical algorithm for signal distinction, signal detecting and estimating signal parameters consists in analyzing discrete parameter values using a correlator. The value of the parameter with the maximum absolute value of the correlator is taken as an estimate. Obviously, this is accompanied by losses in sensitivity and noise immunity, since the specified discrete parameter values do not accurately correspond to the true parameter values of the real signal. In this case, the accuracy of the parameter estimation, even at large signal-to-noise ratios, is limited by the value of the correlators placement interval. Therefore, it is of interest to optimally use the entire set of correlators for parameter estimation and signal detection. The article presents the derivation of algorithm for distinguishing signals by a given parameter by a set of "spaced" correlators. Unlike the classical algorithm, it uses decisive statistics not by one, but by a pair of neighboring correlators, detuned by the correlation interval. In this case, at first, the number of the interval between correlators is estimated according to the maximum of the decisive statistics, and then the value of the parameter is refined within this interval. Additionally, the algorithm allows you to estimate the signal amplitude. The proposed algorithm is compared with the classical one. By means of simulation, the dependences on the energy potential of the average probability of signal distinction for both algorithms are plotted. It is shown that the proposed algorithm has a higher probability of correct distinction than the classical algorithm. It is also shown that the maximum and average energy losses of the distinction algorithm based on a set of "spaced" correlators are less than the losses of the classical algorithm. Thus, the proposed algorithm for distinction signals by a set of "spaced" correlators has greater noise immunity and accuracy of estimating the desired parameter than the classical distinction algorithm.