2016/1

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    Point Spread Functions in Identification of Astronomical Objects from Poisson Noised Image
    (Společnost pro radioelektronické inženýrství, 2016-04) Mojzis, Frantisek; Kukal, Jaromir; Svihlik, Jan
    This article deals with modeling of astronomical objects, which is one of the most fundamental topics in astronomical science. Introduction part is focused on problem description and used methods. Point Spread Function Modeling part deals with description of basic models used in astronomical photometry and further on introduction of more sophisticated models such as combinations of interference, turbulence, focusing, etc. This paper also contains a~way of objective function definition based on the knowledge of Poisson distributed noise, which is included in astronomical data. The proposed methods are further applied to real astronomical data.
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    Box-Particle Implementation and Comparison of Cardinalized Probability Hypothesis Density Filter
    (Společnost pro radioelektronické inženýrství, 2016-04) Song, Li-ping; Liang, Meng; Ji, Hong-bing
    This paper develops a box-particle implementation of cardinalized probability hypothesis density filter to track multiple targets and estimate the unknown number of targets. A box particle is a random sample that occupies a small and controllable rectangular region of nonzero volume in the target state space. In box-particle filter the huge number of traditional point observations is instead by a remarkably reduced number of interval measurements. It decreases the number of particles significantly and reduces the runtime considerably. The proposed algorithm based on box-particle is able to reach a similar accuracy to a Sequential Monte Carlo cardinalized probability hypothesis density (SMC-CPHD) filter with much less computational costs. Not only does it propagates the PHD, but also propagates the cardinality distribution of target number. Therefore, it generates more accurate and stable instantaneous estimates of target number as well as target state than the box-particle probability hypothesis density (BP-PHD) filter does especially in dense clutter environment. Comparison and analysis based on the simulations in different probability of detection and different clutter rate have been done. The effectiveness and reliability of the proposed algorithm are verified by the simulation results.
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    Robust Tensor Analysis with Non-Greedy L1-Norm Maximization
    (Společnost pro radioelektronické inženýrství, 2016-04) Zhao, Limei; Jia, Weimin; Wang, Rong; Yu, Qiang
    The L1-norm based tensor analysis (TPCA-L1) is recently proposed for dimensionality reduction and feature extraction. However, a greedy strategy was utilized for solving the L1-norm maximization problem, which makes it prone to being stuck in local solutions. In this paper, we propose a robust TPCA with non-greedy L1-norm maximization (TPCA-L1 non-greedy), in which all projection directions are optimized simultaneously. Experiments on several face databases demonstrate the effectiveness of the proposed method.
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    A Novel Data Association Method for Frequency Based MIMO Systems
    (Společnost pro radioelektronické inženýrství, 2016-04) Kalkan, Yilmaz
    Whenever more than one target exist, the most important problem is associating the received signals to the correct targets. This problem appears for all multiple target applications such as multiple target tracking and it is known as "Data Association". For frequency-based systems, Multiple-Input Multiple-Output (MIMO) configuration together with the frequency diversity of the system enable us to determine the number of moving targets by using the Doppler frequencies. These frequencies include all relevant information about the location, velocity and direction of the targets and hence, they can be used efficiently to estimate the other unknown target parameters.
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    The Distributed Convergence Classifier Using the Finite Difference
    (Společnost pro radioelektronické inženýrství, 2016-04) Kenyeres, Martin; Kenyeres, Jozef; Skorpil, Vladislav
    The paper presents a novel distributed classifier of the convergence, which allows to detect the convergence/the divergence of a distributed converging algorithm. Since this classifier is supposed to be primarily applied in wireless sensor networks, its proposal makes provision for the character of these networks. The classifier is based on the mechanism of comparison of the forward finite differences from two consequent iterations. The convergence/the divergence is classifiable only in terms of the changes of the inner states of a particular node and therefore, no message redundancy is required for its proper functionality.