METHODICAL APPROACH TO DETERMINING THE STATISTICAL CHARACTERISTICS OF CODE PHASE MANIPULATED SIGNAL IN INFORMATION SYSTEMS

A methodological approach to determining the statistical characteristics of the phase-shifted signal observed against a white noise background is considered. Parametric and non-parametric consistency criteria have been widely used to test the hypothesis of the form of the law of probability distribution of random variables. The parametric criteria include Pearson's c2 and its modification of Nikulin's c2. Nonparametric criteria – Kolmogorov – Smirnov, w2 Mises, Anderson – Darling, Rainy and others. In the foreign scientific literature, the term W2 Mises is used for the Anderson – Darling criterion. When testing simple hypotheses, the following order of criteria (by their power) is given preference: c2 Pearson; Anderson – Darling; Kolmogorov – Smirnov; w2 Mises. When testing complex hypotheses, the order changes: w2 Mises; Kolmogorov – Smirnov; Anderson – Darling; c2 Nikulin; c2 Pearson. With the known sample volume, according to the selected rule, the number of intervals of the histogram is calculated and it is constructed according to the set of realizations of the received signal. After that, a comparison is made with the reference law of distribution. The steps of comparison are well known and do not need a separate explanation. Mathematical modeling and processing of its results with the help of Mathcad software package 14 is carried out. We will test the hypothesis about the normal law of distribution of the input mixture of signal and noise by the criterion c2 Pearson. The results of simulation modeling and computational experiment with the above approach show that the statistical characteristics of the additive mixture of phase-manipulated signal and white noise in the energy-hidden mode of operation of electronic means are subject to laws that are qualitatively close and generally approximated by normal laws.

Wavelet Co-movement Significance Testing with Respect to Gaussian White Noise Background

The paper deals with significance testing of time series co-movement measured via wavelet analysis, namely via the wavelet cross-spectra. This technique is very popular for its better time resolution compare to other techniques. Such approach put in evidence the existence of both long-run and short-run co-movement. In order to have better predictive power it is suitable to support and validate obtained results via some testing approach. We investigate the test of wavelet power cross-spectrum with respect to the Gaussian white noise background with the use of the Bessel function. Our experiment is performed on real data, i.e. seasonally adjusted quarterly data of gross domestic product of the United Kingdom, Korea and G7 countries. To validate the test results we perform Monte Carlo simulation. We describe the advantages and disadvantages of both approaches and formulate recommendations for its using.

Research on SNR Estimating Arithmetic in Frequency Domain to Linear-FM Pulse-Compression Radar

Linear-LM pulse-compression signal has been widely used in modern radar for its characteristics of far detection range and strong ability of anti-jamming. SNR of Linear-FM pulse-compression radar is needed to be measured real-time in the course of estimating its anti-jamming ability quantitatively. Aiming at the performance of this radar, SNR estimating arithmetic in frequency domain is put forward in this paper and the model is validated by simulating in Gaussian white noise background. Result shows that the high speed operation arithmetic is easy to implement and can be applied to estimating the anti-jamming effect of Linear-FM pulse-compression radar.