In many physical experiments, linear frequency modulated (LFM) signals are widely used to probe objects in different environments, from outer-space to underwater. These signals allow a significant improvement in measurement resolution, even when the observation distance is great. For example, using LFM probe signals in underwater investigations enables discovery of even small objects covered by bottom sediments.

Recognition of LFM (chirp) signals depends on their compression based on matched filtering. This work presents two simple solutions to improve the resolution of the short chirp signals recognition. These methods are effective only if synchronization between the signal and matched filter (MF) is obtained. This work describes both the aforementioned methods and a method of minimizing the effects of the lack of synchronization.

The proposed matched filtering method, with the use of n parallel MFs and other techniques, allows only one sample to be obtained in the main lobe and to accurately locate its position in the appropriate sampling period Ts with accuracy Ts/n. These approaches are appropriate for use in probe signal processing.

Noise-like binary sequences combined with signals with linear frequency modulation might be successfully used to increase the reliability of the recognition of both probe and communication signals in the presence of natural and artificial interference. To identify such formed sequences the usage of the two-step matched filtering was suggested and the probabilistic model of the recognition of noise-like code sequences transferred by LFM signals was developed.

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.

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.

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.

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.

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).

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.

Over the last twenty years, there has been a growing interest in the design of tunable devices at microwave frequencies by us- ing liquid crystals technology. In particular, the use of liquid crystals with high dielectric anisotropy allows manufacturing voltage-controlled devices to operate in a wide frequency range. In this work the frequency response of a liquid crystal band-pass filter with dual-mode microstrip structure has been studied in depth by using a simulation software tool. A reshap- ing of a conventional dual-mode square patch resonator bandpass filter with a square notch, studied in the literature, has been proposed with the goal of improving the filter performance. The main features achieved are a significant increase in the return loss of the filter and a narrowing of a 3-dB bandwidth. Specifically, a reduction in the filter bandwidth from 800 MHz to 600 MHz, which leads to a return loss increase from 6 dB to 12.5 dB, has been achieved. The filter centre frequency can be tuned from 4.54 GHz to 5.19 GHz.

As a result of the development of modern vehicles, even higher accuracy standards are demanded. As known, Inertial Navigation Systems have an intrinsic increasing error which is the main reason of using integrating navigation systems, where some other sources of measurements are utilized, such as barometric altimeter due to its high accuracy in short times of interval. Using a Robust Kalman Filter (RKF), error measurements are absorbed when a Fault Tolerant Altimeter is implemented. During simulations, in order to test the Nonlinear RKF algorithm, two kind of measurement malfunction scenarios have been taken into consideration; continuous bias and measurement noise increment. Under the light of the results, some recommendations are proposed when integrated altimeters are used.

In this paper, we show that signal sampling operation can be considered as a kind of all-pass filtering in the time domain, when the Nyquist frequency is larger or equal to the maximal frequency in the spectrum of a signal sampled. We demonstrate that this seemingly obvious observation has wideranging implications. They are discussed here in detail. Furthermore, we discuss also signal shaping effects that occur in the case of signal under-sampling. That is, when the Nyquist frequency is smaller than the maximal frequency in the spectrum of a signal sampled. Further, we explain the mechanism of a specific signal distortion that arises under these circumstances. We call it the signal shaping, not the signal aliasing, because of many reasons discussed throughout this paper. Mainly however because of the fact that the operation behind it, called also the signal shaping here, is not a filtering in a usual sense. And, it is shown that this kind of shaping depends upon the sampling phase. Furthermore, formulated in other words, this operation can be viewed as a one which shapes the signal and performs the low-pass filtering of it at the same time. Also, an interesting relation connecting the Fourier transform of a signal filtered with the use of an ideal low-pass filter having the cut frequency lying in the region of under-sampling with the Fourier transforms of its two under-sampled versions is derived. This relation is presented in the time domain, too.

Commonly known DC-AC switching converters are commonly used in compensator branches. One example of this is a static synchronous compensator (STATCOM). It consists of a voltage source converter (VSC) and acts as an inverter with a capacitor as a DC power source. These compensators use the PWM switching scheme or space vector modulation (SVM) method. Both methods require the desired signal to be generated. In some cases, as during the synthesis of self-excited systems or active energy-compensators, it is necessary to perform the desired branch immittance, e.g. negative capacitance, inductance, resistance or irrational impedance. In such cases, it is necessary to control the universal branch on the basis of a formula. This article presents the implementation method for the convolutional type impedance operators.