This article is devoted to methods of processing random processes. This task becomes particularly relevant in cases where the random process is broadband and non-stationary; then, the measurement of a random process can be associated with an assessment of its probabilistic characteristics. Very often, a non-stationary broadband random process is represented by a single implementation with a priori uncertainty about the type of distribution function. Such random processes occur in information and measuring communication systems in which information is transmitted at a real-time pace (for example, radio telemetry systems in spacecraft). The use of methods of traditional mathematical statistics, for example, maximum likelihood methods, to determine probability characteristics in this case is not possible. In addition, the on-board computing systems of spacecraft operate under conditions of restrictions on mass-dimensional characteristics and energy consumption. Therefore, there is a need to apply accelerated methods of processing measured random processes. This article discusses a method of processing non-stationary broadband random processes based on the use of non-parametric methods of decision theory. An algorithm for dividing the observation interval into stationary intervals using non-parametric Kendall’s statistics is considered, as are methods for estimating probabilistic characteristics on the stationary interval using ordinal statistics. This article presents the results of statistical modeling using the Mathcad program.

In the radio telemetry systems of spacecraft, various data compression methods are used for data processing. When using any compression methods, the data obtained as a result of compression is formed randomly, and transmission over radio communication channels should be carried out evenly over time. This leads to the need to use special buffer storage devices. In addition, existing spacecraft radio telemetry systems require grouping of compressed data streams by certain characteristics. This leads to the need to sort compressed data by streams. Therefore, it is advisable to use associative buffer storage devices in such systems. This article is devoted to the analysis of the processes of formation of output streams of compressed data generated at the output of an associative storage device (ASD). Since the output stream of compressed data is random, queue theory and probability theory are used for analysis. At the same time, associative memory is represented as a queue system. Writing and reading in an ASD can be interpreted as servicing orders in a queue system. The purpose of the analysis is to determine the characteristics of an associative storage device (ASD). Such characteristics are the queue length M{N} in the ASD, the deviation of the queue length D{N} in the ASD and the probability $p_n$ of a given volume $n$ of compressed data in the ASD (including the probability of emptying and the probability of memory overflow). The results obtained are of great practical importance, since they can be used to select the amount of memory of an associative storage device (ASD) when designing compression devices for telemetry systems of spacecraft.

The most measured data in the rocket-and-space technics is a broadband non-stationary random process. Using traditional methods of cyclic digitalization at processing of such data leads to heavy computational costs and heavy amounts of memory of on-board computing devices. As a rule, in processing of discontinuous processes, the recovery of original implementation on the receiving side is not required and processing involves calculation of probabilistic characteristics. In this case processing of data in the rocket-and-space technics has a number of features. First, random processes always represented by the sole implementation. Secondly, priori knowledge of measured random process probabilistic properties is not possible. Thirdly, there is need in on-line processing that predetermines use of rapid processing methods. The article considers application possibilities of nonparametric solution theory methods for evaluation of probabilistic properties of non-stationary broadband random processes in the rocket-and-space technics on-board systems.

One of the modern trends in the development of information technology is that the technical capabilities of information and measurement systems (IMS) are widely used in all areas. Due to the capabilities of modern technologies, there is a need to improve communication networks and improve the quality of their bandwidth, as well as to compress data in the IMS due to the fact that the amount of information is transmitted through communication networks. Various methods of compressing the amount of information in the storage, processing and transmission of large arrays of information in communication channels are considered. Highly informative data compression is effectively performed in telemetry measurement systems. For example, in spacecraft and satellite systems, the bandwidth of the communication channel is minimized by using various compression methods in collecting data and transmitting them to the control station. This is an innovative scientific direction that allows you to collect, measure and transfer various parameters to remote devices. Compression methods focus on separate data processing in different data sources. Open or hidden information connections are increasingly interconnected in ways that can increase data compression capabilities. Strengthens the implementation of compression methods for data obtained from complex measurement parameters at the design stage of IMS. Thus, the consideration of methods for measuring, processing and compressing data is an urgent task. The article reflects the results of research on data compression methods.

The article is devoted to the issues of signal detection in a radio communication system. As a rule, such tasks are solved in two ways – adaptive and nonparametric. The adaptive method is to adjust the structure and parameters of the detection system in accordance with the parameters of signals and noise. Nonparametric methods are used when it is necessary to ensure that the system is insensitive to changes in the properties of signals and noise, in particular in the absence of a priori information about the probabilistic properties of the measured signals and noise. Nonparametric methods of signal detection are based on the use of nonparametric methods of statistical hypothesis testing theory. Nonparametric methods are effectively used in radio electronic systems due to the need to stabilize the frequency of false alarms with unknown noise properties. The invariant properties of nonparametric procedures are based on the independence of the statistical properties of nonparametric functions from the statistical properties of the measured signals. One of the most effective nonparametric methods is the use of ranks, which are formed as a result of ranking the input samples of the measured signal either in ascending or descending order. Ranks have many useful properties for practice and are the most suitable for the purposes of forming various statistical hypotheses. The article is devoted to the criterion of post-detector rank signal processing based on minimizing the RMS error of measuring the situation parameter. The article considers the noise immunity of this criterion in comparison with optimal rank detection methods based on the Neumann -Pearson criterion.

Бұл мақалада заманауи ақпараттық жүйелерде контейнерлік технологияларды қолданудың артықшылықтары қарастырылады. Талдау негізінде контейнерлер дәстүрлі виртуалды машиналардан ресурстарды пайдалану тиімділігі, масштабталу, ақауларға төзімділік және энергия тиімділігі сияқты бірқатар негізгі көрсеткіштер бойынша айтарлықтай асып түсетіні анықталды. Бұл сипаттамалар контейнерлік шешімдерді әсіресе жоғары жүктемелі және динамикалық өзгеретін жағдайлары бар таратылған жүйелер үшін маңызды етеді. Контейнерлеу жүйелердің жұмысын оңтайландырудың, олардың сенімділігін және өнімділігін арттырудың сонымен қатар энергияны үнемдеу арқылы экологиялық әсерді азайтудың перспективалық құралы ретінде ұсынылады. Бұл зерттеудің мақсаты масштабталу, ақауларға төзімділік және ресурстарды пайдалану тиімділігі факторларын ескере отырып, толық бөлінген жүйелердегі контейнерлік ортаны жоспарлау әдістемесін талдау және әзірлеу болып табылады. Ең жақсы өнімділік көрсеткіштеріне қол жеткізуге мүмкіндік беретін эвристикалық әдістерді, жасанды интеллектке негізделген тәсілдерді және гибридті модельдерді қоса алғанда, заманауи жоспарлау алгоритмдеріне назар аударылады.