The paper presents the results of the development of a method and an algorithm for the synthesis of optimal basic signalcode structures in the form of code binary sequences, with a minimum criterion for the side lobes of the periodic autocorrelation function of the indicated sequences. To develop this method, approaches based on set theory and number theory were used. The method is based on a discrete representation of the periodic autocorrelation function of sequences in the form of a system of equations defined on a set of integers, set-theoretic interpretation of the constituent parts of sequences, their integer transformations, mutual properties and relations. A number of transformations of the constituent parts of the sequences are developed, analytical expressions for the dependence of the sum modulus of the sequence elements on the sum of the side lobe levels of their periodic autocorrelation function are derived, and the necessary conditions for the existence of sequences are defined and formulated. The relationship between the parameters of the code binary sequence and the canonical representation of the Euler function on the dimension of the sequence is determined. Analytical relationships between the levels of the side lobes of the periodic autocorrelation function and the parameters of the transformed sequence structures are obtained. The criterion of the effectiveness of the developed method and the corresponding algorithm is the ratio of the number of all possible variants of code binary sequences of a given dimension to a quantity that is determined by the developed algorithm; an expression was obtained to estimate the indicated amount. This efficiency is confirmed by the results of simulation and experimental research. The developed method can be used for the creation of secretive noise-proof data transmission radio systems, remote control systems, radar, and communications.
Proc. SPIE. 11055, XII Conference on Reconnaissance and Electronic Warfare Systems
KEYWORDS: Signal to noise ratio, Digital signal processing, Defect detection, Wavelets, Artificial neural networks, Signal processing, Signal detection, Stochastic processes, Continuous wavelet transforms
The report examines the issue of increasing the efficiency of detecting complex impulse stochastic signals in the process of their generation against the background of quasi-periodic deterministic interference by using wavelet transformations and neural networks. An example the detection of a triple-wave stochastic signal is considered. One of the most characteristic signs of the shape of such signals is a sharply expressed asymmetry: the amplitude of the negative part of the signal is usually 3-4 times higher than the positive maximum amplitude. The second very important feature is the ratio of the positive parts amplitudes of the signal: the amplitude of the right-hand side is always greater, or in extreme cases, equal to the amplitude of the left-hand side. The proposed technique for processing such impulse signals against a background of quasi-periodic interference by using wavelet-neural technologies for analyzing digital signals. For this purpose, an artificial neural network was constructed, which made it possible to detect such signals at the beginning of their development, starting from a signal-to-noise ratio of 1.5 times, which is twice as good as the threshold for visual analysis. The proposed technique can be used in the analysis of pulsed signals in radar systems, mobile railroad rail diagnostic systems by the Magnitodynamic method, as well as in the experimental work of processing digital stochastic signals of various objects, when it is necessary to observe the dynamics of the signal change.
In this paper, we propose a new method for data fusion in the multichannel imaging system verified based on the data
obtained by Landsat 7 Enhanced Thematic Mapper +. Advantage of the presented method consists in the application of multilevel approach of the spectral bands data fusion. This was confirmed based on the comparison of systems with multilevel or pixel level only data fusions methods.