It is well known that Renyi’s entropy of order 2 determines the maximum possible length of the distilled secret keys in sequential secret key distillation protocols so that no information is leaked to the eavesdropper. There have been no attempts to estimate this key quantity based on information available to the legitimate parties to this protocol in the literature. We propose a new machine learning system, which estimates the lower bound of conditional Renyi entropy with high accuracy, based on 13 characteristics locally measured on the side of legitimate participants. The system is based on a prediction intervals deep neural network, trained for a given source of common randomness. We experimentally evaluated this result for two different sources, namely 14 and 6-dimensional EEG signals, of 50 participants, with varying advantage distillation and information reconciliation strategies with and without additional lossless compression block. Across all proposed systems and analyzed sources on average, the best machine learning strategy, called the hybrid strategy, increases the quantity of generated keys 2.77 times compared to the classical strategy. By introducing the Huffman lossless coder before the PA block, the loss of potential source randomness was reduced from 68.48% to a negligible 0.75%, while the leakage rate per one bit remains in the order of magnitude 10⁻⁴.

The Integrated Track Splitting (ITS) is a multi-scan algorithm for target tracking in a cluttered environment. The ITS filter models each track as a set of mutually exclusive components, usually in the form of a Gaussian Mixture. The purpose of this research is to determine the limits of the ‘endurance’ of target tracking of the known ITS algorithm by analyzing the impact of target detection probability. The state estimate and the a-posteriori probability of component existence are computed recursively from the target existence probability, which may be used as a track quality measure for false track discrimination (FTD). The target existence probability is also calculated and used for track maintenance and track output. This article investigates the limits of the effectiveness of ITS multi-target tracking using the method of theoretical determination of the dependence of the measurements likelihood ratio on reliable detection and then practical experimental testing. Numerical simulations of the practical application of the proposed model were performed in various probabilities of target detection and dense clutter environments. Additionally, the effectiveness of the proposed algorithm in combination with filters for various types of maneuvers using Interacting Multiple Model ITS (IMMITS) algorithms was comparatively analyzed. The extensive numerical simulation (which assumes both straight and maneuvering targets) has shown which target tracking limits can be performed within different target detection probabilities and clutter densities. The simulations confirmed the derived theoretical limits of the tracking efficiency of the ITS algorithm up to a detection probability of 0.6, and compared to the IMMITS algorithm up to 0.4 in the case of target maneuvers and dense clutter environments.

The problem of designing a feasible adaptive M-robustified Kalman filter in a case of a thick-tailed Gaussian environment, characterized by impulsive noise-inducing observation and innovation outliers, and/or errors in mathematical model-inducing structural outliers, has been considered. Firstly, the time-varying criterion is used to generate a family of dynamic stochastic approximation algorithms. The M-robust estimate stochastic approximation is derived by minimizing the minimum variance criterion, the estimates of the latter being combined with the one-step minimum mean square error prediction to design M-robust estimate Kalman filter. Finally, the latter is combined with the Huber moving window M-robust parameter estimator of the unknown noises statistics, in parallel with the M-robust state estimation to design an adaptive M-robust estimate Kalman filter. Simulated maneuvering target tracking scenario revealed that the proposed adaptive M-robust estimate-based Kalman filter improves significantly the target estimation and tracking quality, being effective in suppressing multiple outliers with contamination degrees less than thirty percent. Moreover, the achieved improvement comes with additional computational efforts. However, these efforts are usually not significant enough to prevent real-time application.

This paper describes one new approach to frequency locked loop (FLL), which is based on the time non-recursive processing of input periods. System parameters are defined by the ratio of frequencies. The conditions, under which the described system can have the properties of a FLL, are analyzed. All math analyses were made by the use of Z transform approach. It was shown that FLL is extremely fast and that it is suitable for usage in different predicting, tracking and modulation applications, for the measurements of frequency and for other applications. The FLL realization in the technique of standard integrated circuits is described. The oscilloscope picture, made on the realized 8 bit model, is presented. Analysis in frequency domain was made using Matlab tools.

Uvod/cilj: Na osnovu binomne funkcije gustine raspodele verovatnoće, u ovom radu je predstavljen novi verovatni model predviđanja položaja zrakoplova. Metode: Predloženi algoritam sastoji se od tri različita bloka: pridruživanje podataka, praćenje/procena hibridnog stanja i izračunavanje verovatnoće sudara. Informacije o njihovim trenutnim položajima i planovima leta koriste se za dobijanje algoritma za otkrivanje mogućih sukoba između vazduhoplova. Predviđanje situacija u kojima se vazduhoplov može približiti drugom na većoj udaljenosti izračunava se s velikom verovatnoćom. Procena položaja i neodređenost odnose se na pridruživanje podataka ciljeva, kada u „prozor’’ validacije padnu dva traga korišćenjem filtra sa pridruživanjem podataka po verovatnoći. Rezultati: Dizajniran je efikasan algoritam za otkrivanje sudara, koji se testira na mnogim višestrukim praćenjima ciljeva. Zaključak: Rezultati simulacije prevencije sudara aviona u dva scenarija putanje potvrđuju efikasnost predloženog algoritma.