Wyniki wyszukiwania - Biblioteka Nauki

1

Tracking swarms of unmanned aerial systems

100%

Easthope P. F. , Collinson A.

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tom T. 43

47--63

EN

The proliferation of cheap Unmanned Aerial Systems (UAS) poses some significant challenges for the Ground Based Air Defence (GBAD) environment. Individual UAS are inherently difficult to detect, track, classify and identify, but the challenges are exacerbated if UAS are deployed in swarms. It can be appreciated that small physical size, combined with the extensive use of non-metallic materials, will render an individual UAS difficult to detect from a radar’s perspective. If such targets are deployed in a group, then the radar response is likely to consist of intermittent, uncorrelated observations on random subsets of the swarm as a whole, with no guarantee that observations made on individual constituent UAS will be consistent from scan to scan. This paper describes a tracking filter model that is able to simultaneously track the swarm centroid and the spatial dispersion of constituent UAS, without requiring any pre-processing of radar returns into clusters. Although this paper is primarily concerned with UAS, the authors are aware that other types of platform may usefully be employed in swarms, for example satellites, and that some large and complex unitary systems may present swarm-like characteristics to sensors. The principles discussed here may also be applicable in these other cases.

PL

W artykule zamieszczono równania określające oczekiwane liczby linii przechodzących przez k punktów, zdefiniowanych jako wąskie prostokątne obszary o zadanych wymiarach, które można znaleźć w szerszym kwadratowym obszarze zawierające punkty rozmieszczone z rozkładem normalnym. Zamieszczono wyniki symulacyjne, stanowiące podstawę do oceny prawdopodobieństwa tego, że liniowe serie pomiarów w środowisku zakłóconym mają charakter losowy.

2

Comparison of auxiliary and Likelihood Particle Filters for state estimation of dynamical systems

72%

Michalski J. , Kozierski P. , Ziętkiewicz J.

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2018

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tom R. 94, nr 12

86--90

EN

In this paper, algorithms of the state estimation of dynamical systems, using different types of particle filters, have been presented. Three Particle Filter methods have been used: Bootstrap Filter, Auxiliary Particle Filter and Likelihood Particle Filter. These methods have been applied to two nonlinear objects, with quadratic measurement functions. The results have been additionally compared with the outcome from Kalman filters. Based on the obtained results (5 different quality indices) the estimation methods have been evaluated.

PL

W niniejszej pracy zostały przedstawione algorytmy estymacji stanu układów dynamicznych za pomocą różnych rodzajów filtrów cząsteczkowych. Zaprezentowano trzy metody filtrów cząsteczkowych: algorytm Bootstrap, pomocniczy filtr cząsteczkowy i wiarygodny filtr cząsteczkowy. Metody te zastosowano dla dwóch obiektów nieliniowych o kwadratowych funkcjach pomiarowych. Z filtrami cząsteczkowymi zostały dodatkowo zestawione metody filtru Kalmana. Na podstawie uzyskanych wyników (5 różnych wskaźników jakości) metody estymacji zostały ocenione.

3

Error Reduction for GPS Accurate Timing in Power Systems using Kalman Filters and Neural Networks

58%

Reza Mosavi M.

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tom R. 87, nr 12a

161-168

EN

The Global Positioning System (GPS) based time reference provides inexpensive but highly-accurate timing and synchronization capability and meets requirements in power system fault location, monitoring, and control. Precision satellite clocks and time measurements are the keys to the accuracy of GPS. A stand-alone civilian user enjoys an accuracy of 25 meters and 200 nanoseconds. Five methods, including two methods using Kalman Filter (KF), Recurrent Neural Network (RNN), Pi-Sigma Neural Network (PSNN) and Sigma-Pi Neural Network (SPNN), are proposed for error reduction of GPS receivers timing data. We use actual data to evaluate the performance of the proposed methods. An experimental test setup is designed and implemented for this purpose. Results using the five methods are discussed. The experimental results obtained from a Coarse Acquisition (C/A)-code single-frequency GPS receiver strongly support the potential of the method using PSNN to give high accurate timing. The GPS timing RMS error reduces to less than 38 nanoseconds.

PL

Opisano zastosowanie systemu pozycjonowania GPS lokalizacji uszkodzeń i monitorowania sieci przesyłowej. System cywilny GPS oferuje dokładność rzędu 25 m i 200 nanosekund. Opracowano szereg metod poprawy dokładności, wykorzystujących filtry Kalmana i sieci neuronowe. Zredukowano błąd taktowania do około czterdziestu nanosekund.

4

Fault detection and diagnosis of the valve actuators in the discharge air temperature (DAT) systems, using frequency analysis

58%

Tudoroiu N. , Zaheer-Udin M.

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tom Vol. 31, no 3

129-137

EN

One of the main objectives of our research is to build several ARMA linear models and nonlinear neuro models for DAT systems such as developed in [2], [3]. Based on these models we have been interested in building some feedback control strategies for these systems [2], [4], [5]. Due to the high complexity of the HVAC systems the requirements for reliability, availability, and security grow significantly and consequently investigations in this field become necessary. The objective of our paper is to develop some strategies for the fault detection and diagnosis (FDD) of the HVAC systems based on frequency and spectral analysis of the system response. The main interest is focused on dealing with the unanticipated valve actuator failures in the most general formulation based on the frequency analysis of the system response. In our study, we consider that the increasing of the backlash opening in the valve actuators could create serious problems by degrading drastically the HVAC system performance. Based on the spectrum analysis we are capable to gather the most significant patterns that characterize well each fault mode detected, and finally a systematic procedure and technique for proper fault accommodation under the unanticipated failures will be developed. As tools we use MATLAB with SIMULINK toolboxes from control systems, system identification and signal processing. Compared to the other FDD strategies developed in HVAC literature, the one presented in this paper is more simple and much easier to apply with the possibility of extension to other possible applications from different fields.