Adaptive multichannel detection-resolution of stochastic signals in conditions of parametric prior uncertainty

The purpose of the study is to improve the quality indicators of adaptive multichannel detection-resolution-measurement parameters of stochastic signals under parametric a prior uncertainty.

Materials and methods. The methodology for the synthesis of signal detectors of various structures, taking into account various factors, is used to achieve the goal. This makes it possible to exclude from the expression for estimating the signal power the terms due to “colored” internal noises and an uncorrelated background. The tools of correlation analysis, adaptive Bayes approach, criterion of generalized likelihood ratio, methods of calculating its logarithm are also used. 

Results. In this paper, the problems of synthesis of adaptive multichannel detection-resolution algorithms for stochastic signals of various structures under the influence of intense noise interference are considered. An integral element of the detection task is a joint assessment of the intensity of the useful signal and the correlation matrix of interference. This problem is effectively solved for highintensity signals, and the nonstationarity of the internal noise of the receiving uncorrelated background of the interfering signals is not taken into account. A multi-channel receiving system consisting of a number of independent spatially separated elements that form a linear antenna array is considered. The width of the spectrum of the received signals should be considered sufficiently narrow, so that the delay of the signals at the antenna aperture can be neglected. This provision can significantly improve the performance of detection and resolution of stochastic signals in the background of noise interference. Based on the analysis of a finite discrete sample of complex amplitudes of received oscillations, a detection problem was solved, which is formulated as a problem of checking statistical hypotheses regarding distribution parameters. The detection algorithm is reduced to a comparison with the likelihood ratio threshold, and the threshold level value is determined by the selected optimality criterion and for the Neumann-Pearson criterion remains dependent on the power of interfering oscillations.

Conclusion. The presented detector possesses higher characteristics of detection and resolution of stochastic signals in comparison with the known ones. It can be shown that an important property of the obtained statistics is the stabilization of the probability of false detection. This is achieved by normalizing the noise power at the output of the adaptation device. In addition, the resulting algorithm is invariant to the form used for its calculation of the correlation matrix of interference. If we take into account that an uncorrelated background will be added to the internal noise power, with a large number of noise jammers, a significant improvement in the detection performance has been achieved.

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