Branko Kovačević

Strong consistency and asymptotic error distribution for a new class of nonlinear recursive parameter estimation algorithms of an approximate Newton–Raphson type are established. The system model is given in the discrete-time domain by a linear difference equation with constant parameters. The parameter estimator design is based on martingale theory and the Cramér–Rao (CR) theorem, providing a maximum likelihood (ML)-type optimal recursive parameter identification algorithm, whose minimum asymptotic estimation error covariance matrix achieves the CR bound under the worst-case pdf within a specified class; this worst-case pdf simultaneously yields the maximum asymptotic error covariance matrix within the class. However, the worst-case pdf does not generally exist, making the min–max optimal design indeterminable. Therefore, an approximate ML (AML)-type optimal on a class design, based on a suboptimal worst-case pdf, minimizing the scalar Fisher information within the specified class, has also been developed. The proposed design minimizes the conditional estimation error covariance under the specified suboptimal worst-case pdf. Such an approach results in Huber’s M-robustified version of the AML-based optimal design on the class of contaminated Gaussian pdfs. The practical performance of the proposed approach is analyzed through statistical validation, based on the relative asymptotic estimation efficiency measure and Monte Carlo simulations.

A new class of modified minimum state error variance (MSEV) state-space based optimal linear quadratic Gaussian (LQG) regulators for closed-loop structures with estimated feedback has been proposed in this article. The negative feedback path is designed as the cascade of the digital LQG regulator and discrete Kalman state observer. The proposed design enables tracking of a time-varying reference input using the predictive control approach. Moreover, the proposed tracking method utilizes a multivariable continuous-time Cauchy state-space model of nonlinear, nonstationary dynamic systems. The resulting control strategy is approximately optimal, as the optimality of the LQG design holds locally for each linearized model around the respective operating point and does not extend to the global nonlinear system. In this sense, starting from the prespecified nominal state trajectory to be tracked, a numerical optimization procedure minimizing the squared tracking error at each step by using the Nelder–Mead direct search simplex algorithm under the required constraints on the input signal has been developed. The LQG regulator and Kalman state observer are designed by utilizing the linear discrete-time state variable models that properly approximate the nonlinear system dynamics across the nominal state trajectory. The performance of the proposed design is validated by simulating a six-degree-of-freedom nonlinear aircraft model across typical flight regimes.

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.

: The radar multi target tracking (MTT) technique requires prior knowledge of a number of parameters about the sensor, the target and backgrounds. The Integrated Track Splitting (ITS) is a fully automatic track-while-scan (TWS) target tracking algorithm capable of extracting and tracking a target in a dense clutter environment using quality false track discrimination (FTD) methodology. The computational complexity in ITS algorithm is limited, compared to other algorithms they use statistical methods to discriminate between false and true tracks, such as multiple hypothesis tracking (MHT), mainly due to the FTD performed. The paper provides an analysis of tracking parameters that allows determining the limit of the possibility of successful target tracking. Extensive experiments have confirmed that the recursive determination of the probability of the existence of a track during tracking can confirm a true track and reject a false track. The clutter density, number of random occurred targets, targets load during the maneuver and the target detection probability were varied. The results of experiments, carried out via Monte Carlo simulations, shown over representative confirmed true tracks (CTT) diagrams, root mean square error position and normalized tracking efficiency parametric diagrams allow the user to select optimal multi-target tracking parameters for different scenarios and clutter densities.

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.

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.

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.