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1
Content available remote Web application service in bus arrival time prediction
EN
Bus arrival time prediction represents very important part of the service that informs passengers of intelligent transport systems in public bus transportation. Different methods are used for the prediction. In this paper, two methods for predicting arrival time of bus are analysed. Proposed method is the freely available Google’s web service “DistanceMatrixAPI” . Comparative view of obtained results using the Kalman filter and Web service is presented. For the experimental research we proposed model of Distribution Modular Information and Communication System. Research results shows that the implementation of Kalman filter method is much more accurate that the use of “DistanceMatrix API” method.
PL
W artykule zaprezentowano inteligentny system informujący pasażerów transportu publicznego o przewidywanym pczasie przyjazdu pojazdu. Analizowano dwie metody. NBajlepsze parametry miał system wykorzystujący filtr Kalmana.
2
Content available Estimation of UFMC Fading Channels Using H∞ Filter
EN
Universal filtered multi-carrier (UFMC) modulation is a very powerful candidate to be employed for future 5G mobile systems. It overcomes the limitations and restrictions in current modulation techniques employed in 4G mobile systems and supports future applications, such as machineto-machine (M2M), device-to-device (D2D), and vehicle-tovehicle (V2V) communications. In this paper, we address the estimation of UFMC fading channels based on the comb-type pilot arrangement in the frequency domain. The basic solution is to estimate the fading channel based on the mean square error (MSE) or least square (LS) criteria with adaptive implementation using least mean square (LMS) or recursive least square (RLS) algorithms. However, these adaptive filters seem not to be effective, as they cannot fully exploit fading channel statistics, particularly at high Doppler rates. To take advantage of these statistics, time-variations of the fading channel are modeled by an autoregressive process (AR), and are tracked by an H∞ filter. This, however, requires that AR model parameters be known, which are estimated by solving the Yule-Walker equation (YWE), based on the Bessel autocorrelation function (ACF) of the fading channel with a known Doppler rate. Results of Matlab simulations show that the proposed H∞ filter-based channel estimator is more effective when compared with existing estimators.
EN
Reliable estimation of longitudinal force and sideslip angle is essential for vehicle stability and active safety control. This paper presents a novel longitudinal force and sideslip angle estimation method for four-wheel independent-drive electric vehicles in which the cascaded multi-Kalman filters are applied. Also, a modified tire model is proposed to improve the accuracy and reliability of sideslip angle estimation. In the design of longitudinal force observer, considering that the longitudinal force is the unknown input of the electric driving wheel model, an expanded electric driving wheel model is presented and the longitudinal force is obtained by a strong tracking filter. Based on the longitudinal force observer, taking into consideration uncertain interferences of the vehicle dynamic model, a sideslip angle estimation method is designed using the robust Kalman filter and a novel modified tire model is proposed to correct the original tire model using the estimation results of longitudinal tire forces. Simulations and experiments were carried out, and effectiveness of the proposed estimation method was verified.
EN
The accuracy and reliability of Kalman filter are easily affected by the gross errors in observations. Although robust Kalman filter based on equivalent weight function models can reduce the impact of gross errors on filtering results, the conventional equivalent weight function models are more suitable for the observations with the same noise level. For Precise Point Positioning (PPP) with multiple types of observations that have different measuring accuracy and noise levels, the filtering results obtained with conventional robust equivalent weight function models are not the best ones. For this problem, a classification robust equivalent weight function model based on the t-inspection statistics is proposed, which has better performance than the conventional equivalent weight function models in the case of no more than one gross error in a certain type of observations. However, in the case of multiple gross errors in a certain type of observations, the performance of the conventional robust Kalman filter based on the two kinds of equivalent weight function models are barely satisfactory due to the interaction between gross errors. To address this problem, an improved classification robust Kalman filtering method is further proposed in this paper. To verify and evaluate the performance of the proposed method, simulation tests were carried out based on the GPS/BDS data and their results were compared with those obtained with the conventional robust Kalman filtering method. The results show that the improved classification robust Kalman filtering method can effectively reduce the impact of multiple gross errors on the positioning results and significantly improve the positioning accuracy and reliability of PPP.
PL
W artykule opisano system nawigacji personalnej wykorzystujący moduły ultraszerokopasmowe, umożliwiające określanie położenia użytkownika wewnątrz budynków. Wielkościami wykorzystywanymi do pozycjonowania są odległości pomiędzy mobilnym modułem użytkownika, a czterema stacjami bazowymi wchodzącymi w skład systemu. Przedstawiono wybrane aspekty realizacji poszczególnych komponentów systemu, użyte podzespoły, algorytmy pozycjonujące oraz wyniki badań eksperymentalnych.
EN
The paper describes personal navigation system, using Ultra-Wideband (UWB) modules, enabling determination of user position inside buildings. Quantities used for positioning are ranges between the mobile user’s module and four beacons which are used in the system. Selected aspects of implementation of individual system components, used parts, positioning algorithms and results of experiments are presented.
EN
The paper proposes a robust faults detection and forecasting approach for a centrifugal gas compressor system, the mechanism of this approach used the Kalman filter to estimate and filtering the unmeasured states of the studied system based on signals data of the inputs and the outputs that have been collected experimentally on site. The intelligent faults detection expert system is designed based on the interval type-2 fuzzy logic. The present work is achieved by an important task which is the prediction of the remaining time of the system under study to reach the danger and/or the failure stage based on the Auto-regressive Integrated Moving Average (ARIMA) model, where the objective within the industrial application is to set the maintenance schedules in precisely time. The obtained results prove the performance of the proposed faults diagnosis and detection approach which can be used in several heavy industrial systems.
PL
W ramach pracy przedstawiono metody numeryczne oraz filtry cyfrowe umożliwiające korekcję sygnału pomiarowego uzyskanego podczas badań doświadczalnych parametrów ruchu pojazdu. Wykorzystany moduł pomiarowy nawigacji inercyjnej składa się z trójosiowych akcelerometrów, żyroskopów wykonanych w technologii MEMS oraz magnetometru. Zebrane dane pomiarowe umożliwiły odniesienie ich do punktu w przestrzeni trójwymiarowej, w celu wyznaczenia trajektorii ruchu pojazdu. W artykule przedstawiono metody numeryczne obrotu lokalnego układu współrzędnych do układu globalnego oraz wybrane filtry cyfrowe umożliwiające wygładzanie sygnału pomiarowego w czasie rzeczywistym.
EN
The work presents numerical methods and digital filters that enable correction of the measurement signal obtained during experimental tests of vehicle motion parameters. The inertial navigation measuring module used consists of three-axis accelerometers, gyroscopes made in MEMS technology and a magnetometer. The collected measurement data made it possible to refer them to a point in three-dimensional space in order to determine the trajectory of vehicle movement. The article presents numerical methods of rotation of the local coordinate system to the global system and selected digital filters enabling smoothening the measurement signal in real time.
8
Content available Projektowanie sterowania monocyklem elektrycznym
PL
Wraz z rozwojem technologii zaobserwować można globalny wzrost zainteresowania branżą pojazdów elektrycznych. Czynnikami, które pozytywnie wpływają na to zjawisko jest możliwość redukcji spalin oraz hałasu, które emitują standardowe pojazdy. Przy mniejszych, mobilnych konstrukcjach elektrycznych, dodatkową zaletą jest zmniejszenie problemu związanego z zatłoczeniem ulic. Artykuł zawiera opis projektu monocyklu elektrycznego, dla którego zaprojektowano dwa układy sterowania z filtrami Kalmana. W badaniach testowych przedstawiono wyniki sterowania.
EN
The development of electric vehicles has been observed in recent times. The factors that positively influence this phenomenon are the reduction of exhaust emissions and noise emitted by standard vehicles. With smaller, mobile electrical structures, an additional advantage is to reduce the congestion problem. The article contains a description of the design of an electrical monocyte for which two control systems with Kalman filters are designed. Control results are presented in the test studies.
EN
This article presents research results concerning the determination of the position of a Cessna 172 aircraft by means of the DGPS positioning method. The position of the aircraft was recovered on the basis of P1/P2 code observations in the GPS navigation system. The coordinates of the aircraft were designated due to the application of the Kalman forward-filtering method. The numerical calculations were conducted using RTKLIB software in the RTKPOST module. In the scientific experiment, the authors used research materials from the test flight conducted by a Cessna 172 aircraft in the area of Dęblin in the Lublin Voivodeship in south-eastern Poland. The research experiment exploited navigation data and GPS observation data recorded by the geodetic Topcon Hiper Pro receiver mounted in the cockpit of the Cessna 172 and installed on the REF1 reference station. The typical accuracy for recovering the position of the Cessna 172 with the DGPS method exceeds in the region of 2 m. In addition, the authors specify the parameters of availability, integrity and continuity of GNSS satellite positioning in air navigation. The obtained findings of the scientific experiment were compared with the International Civil Aviation Organization’s (ICAO’s) technical standards.
EN
In the paper an overview of state estimators and state observers used in linear systems, will be presented. The state estimators and observers can be used in many applications like the state reconstruction for the control purposes or for the diagnosis and fault detection in technical processes or for the virtual measurements of inaccessible variables of the system as well as for the best filtration of the differential equation solution. As the standard most commonly the Kalman filter and Luenberger type observers are used. Although the Kalman filter guarantees optimal filtering quality of the state, reconstructed from the noisy measurements, both Kalman filter and the Luenberger observer guarantee only asymptotic quality of the real state changes and tracking, basing on the current measurements of the system output and input signals. Unfortunately, the value of the estimation error at any moment of time cannot be calculated. The discussion on differences between continuous and two types of discrete Kalman Filter will be presented. This paper is planned to be the introduction to presentation of another type of the state observers which have the structure given by the integral operators. Based on measurements of the system output and input signals on some predefined finite time interval, they can reconstruct, after this interval, the observed state exactly.
EN
Kalman filter is used widely in harmonics detection in power system, where, the quality of the Kalman filter depends on having accurate predicting values based on a mathematical model of the harmonics in power system. It required an exact knowledge of the harmonics’ orders, and this is difficult especially that in some of power system apparatus the order of harmonics may change during their operation. For that reason an adaptive Kalman filter combined with Fast Fourier Transform FFT is proposed to determine the orders of harmonics that should be modelled in Kalman filter, in order to reduce the error in the estimated signal.
PL
Filtr Kalmana może być wykorzystywany do określania harmonicznych w systemie energetycznym. Dokładność określania harmonicznych zależy od dokładności predykcji. W celu poprawy dokładności adaptacyjny filtr Kalmana jest wspomagany szybką transformatą Fouriera.
PL
W artykule przedstawiono metodę estymacji położenia na podstawie pomiaru globalnej orientacji członów ramienia manipulatora z wykorzystaniem czujników pola grawitacyjnego. Niniejszy artykuł przybliża problem estymacji położenia na podstawie pomiarów obarczonych dużym szumem i wykorzystania rozszerzonego filtra Kalmana do ograniczenia w dużym stopniu wpływu szumów na pomiar.
EN
Paper presents kinematic structure of measurement arm along with its construction restrains originating from using only accelerometers for determining relative positions of links. This article introduces the problem of position estimation based on measurements with high noise and the use of the extended Kalman filter to limit the impact of noise on the measurement to a large extent. Repeatability tests were performed using custom made test stand.
EN
Conventionally, the filtering technique for attitude estimation is performed using gyros or attitude dynamics models. In order to extend the application range of an attitude filter, this paper proposes a quaternion-based filtering framework for gyroless attitude estimation without an attitude dynamics model. The attitude estimation system is established based on a quaternion kinematic equation and vector observation models. The angular velocity in the system is determined through observation vectors from attitude sensors and the statistical properties of the angular velocity error are analysed. A Kalman filter is applied to estimate the attitude error such that the effect from the angular velocity error is compensated with its statistical properties at each sampling moment. A numerical simulation example is presented to illustrate the performance of the proposed algorithm.
EN
State estimation of stochastic discrete-time linear systems subject to unknown inputs has been widely studied, but few works take into account disturbances switching between unknown inputs and constant biases. We show that such disturbances affect a networked control system subject to deception attacks on the control signals transmitted by the controller to the plant via unreliable networks. This paper proposes to estimate the switching disturbance from an augmented state version of the intermittent unknown input Kalman filter. The sufficient stochastic stability conditions of the obtained filter are established when the arrival binary sequence of data losses follows a Bernoulli random process.
EN
The article discusses the issues relating to mutual relations between artificial intelligence and inertial navigation, which are noticeable within logistics. The main focus has been on showing how the use of inertial navigation systems used in logistics can affect the use of artificial intelligence. The reflections taken in the article are both theoretical and practical. As part of the theory, we tried to show, based on the literature of the subject, what is the essence of artificial intelligence and inertial navigation and what are the relationships between them. The results of our own research were also presented (practical aspect).
EN
The BeiDou navigation satellite system (BDS) is one of the four global navigation satellite systems. More attention has been paid to the positioning algorithm of the BDS. Based on the study on the Kalman filter (KF) algorithm, this paper proposed a novel algorithm for the BDS, named as the minimum dispersion coefficient criteria Kalman filter (MDCCKF) positioning algorithm. The MDCCKF algorithm adopts minimum dispersion coefficient criteria (MDCC) to remove the influence of noise with an alpha-stable distribution (ASD) model which can describe non-Gaussian noise effectively, especially for the pulse noise in positioning. By minimizing the dispersion coefficient of the positioning error, the MDCCKF assures positioning accuracy under both Gaussian and non-Gaussian environment. Compared with the original KF algorithm, it is shown that the MDCCKF algorithm has higher positioning accuracy and robustness. The MDCCKF algorithm provides insightful results for potential future research.
EN
In advanced control, a control target tracks the set points and tends to achieve optimal operation of a process. Model predictive control (MPC) is used to track the set points. When the set points correspond to an optimum economic trajectory that is sent from an economic layer, the process will be gradually reaching the optimal operation. This study proposes the integration of an economic layer and MPC layer to solve the problem of different time scale and unreachable set points. Both layers require dynamic models that are subject to objective functions. The prediction output of a model is not always asymptotically equal to the measured output of a process. Therefore, Kalman filter is proposed as a state feedback to the two-layer integration. The proposed controller only considers the linear empirical model and the inherent model is identified by system identification, which is assumed to be an ample representation of the process. A depropanizer process case study has been used for demonstration of the proposed technique. The result shows that the proposed controller tends to improve the profit of the process smoothly and continuously, until the process reaches an asymptotically maximum profit point.
18
Content available Fuzja sensoryczna IMU metodą filtra Kalmana
PL
W pracy przedstawiono metody pomiaru orientacji obiektu w przestrzeni. Na potrzeby określenia orientacji obiektu (attitude) estymowano lotnicze kąty Eulera RPY - Roll, Pitch, Yaw. Do pomiaru poszukiwanych wielkości wykorzystano mikroprocesorowy inercyjny układ pomiarowy (IMU). Dzięki fuzji sensorycznej zintegrowano dane pochodzące z fizycznie odseparowanych, niezależnych czujników IMU. W celu sprawdzenia poprawności fuzji sensorycznej zaimplementowano filtrację Kalmana w środowisku inżynierskim Matlab oraz na mikrokontrolerze a uzyskane wyniki pomiarów przedstawiono na przebiegach czasowych. W wyniku modelowania procesu pomiarowego oraz jego implementacji i filtracji w sterowniku cyfrowym uzyskano odfiltrowane przebiegi wielkości określających orientację obiektu w przestrzeni.
EN
The article describes method of measurement of 6DOF object’s attitude. For the purposes of determining the object orientation (attitude) estimation of airline Euler angles Roll, Pitch, Yaw. There are three types of sensor using for this purpose: accelerometer, gyroscope and magnetometer. Working as a measurement system uses a microprocessor RAZR IMU 9-DOF. Three types of independent signals are connected using Kalman Filter Fusion developed on the basis of designated signal models and their dependencies in space state. The validity of the assumptions made by implementing the Kalman filter engineering environment of Matlab. The results of numerical experiments are presented in the form of time passes selected parameters that describe the orientation of the object. Designed filtration system is implemented in the electronic layout of the IMU and test research. As a result of the study was obtained from the sensor signals are filtered out. Registered time characteristics were presented in work.
EN
Due to safety reasons, the movement of a ship in coastal areas should be monitored, tracked, recorded, and stored. The Automatic Identification System (AIS) is a suitable tool to use in performing these functions. The probability limit for the AIS dynamic data availability can be limited by the lack of a Global Position System (GPS) signal, heading (HDG), and rate of turn (ROT) data in the position report. The unavailability of a data link is an additional limitation. To fill this gap, it is possible to attach the discrete Kalman filter (KF) for the position and course estimation. Coordinate estimation in the absence of a transmission link can improve the quality of the AIS service at Vessel Traffic Service (VTS) stations. This paper has presented the Kalman filtering algorithm to improve the possibilities for ship motion tracking and monitoring in the TSS (Traffic Separation Scheme) and fairways area. More than 570 iterations were calculated and the results have been presented in figures to familiarize the reader with the operating principle of the Kalman filter algorithm.
EN
Due to the safety reason, the ship movement on the littoral area should be monitored, tracked, recorded and stored. Automatic Identification System (AIS) is the perfect tool to ensure this requirement. The limit probability for the AIS dynamic data availability can be limited by the lack of Global Position System (GPS) signal, heading (HDG) and rate of turn (ROT) data in position report. Availability of data link is an additional limitation. For this purpose, it is possible to attach the Discrete Kalman filter (KF) for the position, and course estimation. Coordinate estimation in the absence of a transmission link can improve the quality of AIS service at Vessel Traffic Service (VTS) stations. This article presents Kalman filtering algorithm to improve the possibilities of ship motion tracking and monitoring in the TSS (Traffic Separation Scheme) and fairways area. Only 39 iterations were presented to familiarize how the Kalman filter algorithm works. The archival data from 2006 were used deliberately. During that time, there were problems with the AIS availability service. With the use of measurements series from those years, it is easier to observe the effectiveness of Kalman filter in absence of AIS data.
PL
Dla zapewnienia bezpieczeństwa żeglugi ruch jednostek pływających w rejonie wód wewnętrznych i w strefie przybrzeżnej powinien być monitorowany i rejestrowany, najlepiej w postaci cyfrowej. Doskonałym narzędziem do tego celu jest automatyczny system identyfikacji (AIS). Dostępność danych dynamicznych AIS może zostać jednak zredukowana z powodu braku dostępu do systemów pozycjonowania (GPS) oraz braku danych o kursie (HDG) i prędkości kątowej (ROT) w raportach pozycyjnych. Niedostępność łącza komunikacyjnego w paśmie VHF jest dodatkowym ograniczeniem systemu. W celu estymacji danych dotyczących pozycji i kursu statku w czasie, gdy dane te nie są dostępne, można zastosować dyskretny filtr Kalmana (KF). Estymacja współrzędnych w przypadku braku łącza komunikacyjnego wynikającego z ograniczeń dostępności systemu AIS podnosi jakość serwisu zarządzania ruchem statków (VTS). W artykule zaprezentowano 39 iteracji filtru Kalmana. Celowo zastosowano dane archiwalne z 2006 roku, albowiem w tych rejestracjach występują wyraźne przerwy w strumieniu danych. Rzecz w tym, że efektywność zaproponowanego rozwiązania łatwiej zaobserwować, jeśli zostaną zastosowane serie pomiarowe z okresu, gdy występowały problemy z dostępnością serwisu AIS.
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