Abstract
The article discusses the issues of building intelligent receivers of pulsed signals with an unknown arrival time without restrictions on the value of the signal-to-noise ratio under the influence of a complex of interfering factors. Using recurrent methods for detecting disturbances of random processes and an algorithm of cumulative sums, the problem of synthesizing algorithms for detecting Markov signals with random moments of appearance against the background of various combinations of destabilizing factors acting in control systems for the states of rail lines in normal and shunt operating modes is solved. To assess the efficiency of detecting a random signal by the method of cumulative sums, statistical modeling of the specified algorithm was carried out.
Highlights
When building intelligent receivers of pulse signals with an unknown arrival time without restrictions on the value of the signal-to-noise ratio under the influence of a complex of interfering factors, good results are obtained using the mathematical apparatus for detecting the disorder of random processes [1,2,3]
Let us consider the problem of synthesizing algorithms for detecting Markov signals with random moments of appearance against the background of various combinations of destabilizing factors acting in control systems for the states of rail lines [11]. a
18000 realizations were considered in shunt mode
Summary
When building intelligent receivers of pulse signals with an unknown arrival time without restrictions on the value of the signal-to-noise ratio under the influence of a complex of interfering factors, good results are obtained using the mathematical apparatus for detecting the disorder of random processes [1,2,3]. The issue of signal detection is solved based on the analysis of the realizations of the input action that are successively received at the input of the receiver Since this increases the amount of memory required to memorize all observations h received at an arbitrary moment, the question arises of finding recurrent algorithms. Such algorithms are based on sufficient statistics that allow recalculating the previous values of observations taking into account the newly received ones [4, 5]
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