In the paper an example of application of the Kalman filtering in the navigation process of automatically guided vehicles was presented. The basis for determining the position of automatically guided vehicles is odometry – the navigation calculation. This method of determining the position of a vehicle is affected by many errors. In order to eliminate these errors, in modern vehicles additional systems to increase accuracy in determining the position of a vehicle are used. In the latest navigation systems during route and position adjustments the probabilistic methods are used. The most frequently applied are Kalman filters.
Speech enhancement is fundamental for various real time speech applications and it is a challenging task in the case of a single channel because practically only one data channel is available. We have proposed a supervised single channel speech enhancement algorithm in this paper based on a deep neural network (DNN) and less aggressive Wiener filtering as additional DNN layer. During the training stage the network learns and predicts the magnitude spectrums of the clean and noise signals from input noisy speech acoustic features. Relative spectral transform-perceptual linear prediction (RASTA-PLP) is used in the proposed method to extract the acoustic features at the frame level. Autoregressive moving average (ARMA) filter is applied to smooth the temporal curves of extracted features. The trained network predicts the coefficients to construct a ratio mask based on mean square error (MSE) objective cost function. The less aggressive Wiener filter is placed as an additional layer on the top of a DNN to produce an enhanced magnitude spectrum. Finally, the noisy speech phase is used to reconstruct the enhanced speech. The experimental results demonstrate that the proposed DNN framework with less aggressive Wiener filtering outperforms the competing speech enhancement methods in terms of the speech quality and intelligibility.
The form, waviness and roughness components of a measured profile are separated by means of digital filters. The aim of analysis was to develop an algorithm for one-dimensional filtering of profiles using approximation by means of B-splines. The theory of B-spline functions introduced by Schoenberg and extended by Unser et al. was used. Unlike the spline filter proposed by Krystek, which is described in ISO standards, the algorithm does not take into account the bending energy of a filtered profile in the functional whose minimization is the principle of the filter. Appropriate smoothness of a filtered profile is achieved by selecting an appropriate distance between nodes of the spline function. In this paper, we determine the Fourier transforms of the filter impulse response at different impulse positions, with respect to the nodes. We show that the filter cutoff length is equal to half of the node-to-node distance. The inclination of the filter frequency characteristic in the transition band can be adjusted by selecting an appropriate degree of the B-spline function. The paper includes examples of separation of 2D roughness, as well as separation of form and waviness of roundness profiles.
Extraction of the foetal electrocardiogram from single-channel maternal abdominal signals without disturbing its morphology is difficult. We propose to solve the problem by application of projective filtering of time-aligned ECG beats. The method performs synchronization of the beats and then employs the rules of principal component analysis to the desired ECG reconstruction. In the first stage, the method is applied to the composite abdominal signals, containing maternal ECG, foetal ECG, and various types of noise. The operation leads to maternal ECG enhancement and to suppression of the other components. In the next stage, the enhanced maternal ECG is subtracted from the composite signal, and this way the foetal ECG is extracted. Finally, the extracted signal is also enhanced by application of projective filtering. The influence of the developed method parameters on its operation is presented.
Two-dimensional (2D) positive systems are 2D state-space models whose state, input and output variables take only nonnegative values. In the paper we explore how linear matrix inequalities (LMIs) can be used to address the stability problem for 2D positive systems. Necessary and sufficient conditions for the stability of positive systems have been provided. The results have been obtained for most popular models of 2D positive systems, that is: Roesser model, both Fornasini-Marchesini models (FF-MM and SF-MM) and for the general model.
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 quaternionbased 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.
In the paper a frequency method of filtering airborne laser data is presented. A number of algorithms developed to remove objects above a terrain (buildings, vegetation etc.) in order to obtain the terrain surface were presented in literature. Those all methods published are based on geometrical criteria, i.e. on a specific threshold of elevation differences between two neighbouring points or groups of points. In other words, topographical surface is described in a spatial domain. The proposed algorithm operates on topographical surface described in a frequency domain. Two major tools, i.e. Fast Fourier Transform (FFT) and digital filters are used. The principal assumption is based on the idea that low frequencies are responsible for a terrain surface, while high frequencies are connected to objects above the terrain. The general guidelines of this method were for the first time presented at (Marmol and Jachimski, 2004). Due to the fact that the preliminary results showed some limitations, two-stage filtering algorithm has been introduced. The frequency filter was modified in such a manner that different filter parameters are used to detect buildings than those to recognize vegetation. In the first stage of data processing the filtering concerning elimination of points connected with urban areas was applied. The low-pass filter with parameters determined for urban area was used for the whole tested terrain in that stage. The purpose of the second stage was to eliminate vegetation by using the filter for forest areas. The presented method was tested by using data sets obtained in the ISPRS test on extracting DTM from point clouds. The results of using the two-stage algorithm were com- pared with both reference data and with filtering results of eight method reported to ISPRS test. A numerical comparison of the filter output with a reference data set shows that the filter generates DTM of a satisfactory quality. The accuracy of DTM produced by the frequency algorithm fits the average accuracy of eight methods reported in the ISPRS test.
The paper presents a method of obtaining short-termpositioning accuracy based on micro electro-mechanical system (MEMS) sensors and analysis of the results. A high-accuracy and fast-positioning algorithm must be included due to the high risk of accidents in cities in the future, especially when autonomous objects are taken into account. High-level positioning systems should consider a number of sub-systems such as global positioning system (GPS), CCTV – video analysis, a system based on analysis of signal strength of access points (AP), etc. Short-term positioning means that there are other locating systems with a sufficiently high degree of accuracy based on, e.g. a video camera, but the located object can disappear when it is hidden by other objects, e.g. people, things, shelves etc. In such a case, MEMS sensors can be employed as a positioning system. The paper examines typical movement profiles of a radio-controlled (RC) model and fundamental filtering methods in respect of position accuracy. The authors evaluate the complexity and delay of the filter and the accuracy of the positioning in respect of the current speed and phase of movement (positive acceleration, constant) of the object. It is necessary to know whether and how the length of the filter changes the position accuracy. It has been shown that the use of fundamental filters, which provide solutions in a short time, enables to locate objects with a small error in a limited time.
The paper deals with the application of the extended Kalman filters in the control structure of a two-mass drive system. In the first step only linear extended Kalman filter was used for the estimation of mechanical state variables of the drive including load torque value. The estimation algorithm showed good robustness to mechanical parameters variations. For the system with some parameters changing in the wide range, simultaneous estimation of the state variables and chosen system parameters is required. For this reason the non-linear extended Kalman filter, which estimates simultaneously state variables and mechanical parameters of the two-mass drive system, was developed. Parameters of covariance matrices of used Kalman filters were set using the genetic algorithm. Both proposed estimators were investigated in simulation and experimental tests, in the open-loop operation and in the state-feedback control system of the two-mass system.
This paper presents a geomagnetic detection method for pipeline defects using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet energy product (WEP) – Teager energy operator (TEO), which improves detection accuracy and defect identification ability as encountering strong inference noise. The measured signal is first subtly decomposed via CEEMDAN into a series of intrinsic mode functions (IMFs), which are then distinguished by the Hurst exponent to reconstruct the filtered signal. Subsequently, the scale signals are obtained by using gradient calculation and discrete wavelet transform and are then fused by using WEP. Finally, TEO is implemented to enhance defect signal amplitude, completing geomagnetic detection of pipeline defects. The simulation results created by magnetic dipole in a noisy environment, indoor experiment results and field testing results certify that the proposed method outperforms ensemble empirical mode decomposition (EEMD)-gradient, EEMD-WEP-TEO, CEEMDAN-gradient in terms of detection deviation, peak side-lobe ratio (PSLR) and integrated side-lobe ratio (ISLR).
Bogusław Wolniewicz, inspired by his formal ontology of situations, has put forward a question on semilattices with a unit (A question about joinsemilattices, Bulletin of the Section of Logic 19/3, 1990). The present paper is entirely devoted to this problem in the formulation given by Wolniewicz. First, the meaning of the question is analyzed and its lattice-theoretical and Boolean algebraic contents are exhibited. Second, set-theoretical and topological counterparts of the question are formulated and commented upon.
Various components of surface texture are identified, namely form, waviness and roughness. Separation of these components is done by digital filtering. Several problems exist during analysis of two-process surfaces. Therefore the Gaussian robust profile filtering technique was established and has been studied here. The computer generated 2D profiles and 3D surface topographies having triangular scratches as well as measured stratified surfaces were subjected to filtration. However even robust filter applications cause distortion of profiles having valleys wider than 100 μm. In order to minimize the distortion associated with wide and deep valleys, the robust filter should be modified. A special procedure was elaborated for minimizing distortion of roughness profiles caused by filtration. Application of this method to analyses of several profiles was presented. The difference between 1-D and 2-D filtering of surface topography using the same kind of filter was discussed. As a result we found that modification of a 2-D surface topography filter was not necessary.
The secretiveness of sonar operation can be achieved by using continuous frequency-modulated sounding signals with reduced power and significantly prolonged repeat time. The application of matched filtration in the sonar receiver provides optimal conditions for detection against the background of white noise and reverberation, and a very good resolution of distance measurements of motionless targets. The article shows that target movement causes large range measurement errors when linear and hyperbolic frequency modulations are used. The formulas for the calculation of these errors are given. It is shown that for signals with linear frequency modulation the range resolution and detection conditions deteriorate. The use of hyperbolic frequency modulation largely eliminates these adverse effects.
An electronic system and an algorithm for estimating pedestrian geographic location in urban terrain is reported in the paper. Different sources of kinematic and positioning data are acquired (i.e.: accelerometer, gyroscope, GPS receiver, raster maps of terrain) and jointly processed by a Monte-Carlo simulation algorithm based on the particle filtering scheme. These data are processed and fused to estimate the most probable geographical location of the user. A prototype system was designed, built and tested with a view to aiding blind pedestrians. It was shown in the conducted field trials that the method yields superior results to sole GPS readouts. Moreover, the estimated location of the user can be effectively sustained when GPS fixes are not available (e.g. tunnels).
The equipment mounted on the carbody chassis of the railway vehicles is a critical component of the vehicle in terms of ride comfort. The reason for that is their large mass, able to visibly influence the vibrations mode of the carbody. The paper examines the influence of the equipment upon the mode of vertical vibrations of the carbody in the high-speed vehicles, reached on the basis of the frequency response functions of the acceleration in three carbody reference points – at the centre and above the bogies. These functions are derived from the numerical simulations developed on a rigid-flexible coupled model, with seven degrees of freedom. As a rule, the results herein prove the influence of the equipment mounting mode (rigid or elastic), along with the speed regime, upon the level of vibrations in the carbody reference points, at the resonance frequency of the symmetrical bending mode. Similarly, it is also demonstrated how the equipment mass and the damping degree of the suspension system affect the level of the vibrations in the carbody.