• Journal of Electrical Engineering and Technology
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• v.6 no.4
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• pp.563-572
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• 2011

Reliability is an essential factor in a navigation system. Therefore, an integrity monitoring system is considered one of the most important parts in an avionic navigation system. A fault due to systematic malfunctioning definitely requires integrity reinforcement through systematic analysis. In this paper, we propose a method to detect faults of the GPS signal by using a distributed nonlinear filter based probability test. In order to detect faults, consistency is examined through a likelihood ratio between the main and auxiliary particle filters (PFs). Specifically, the main PF which includes all the measurements and the auxiliary PFs which only do partial measurements are used in the process of consistency testing. Through GPS measurement and the application of the autonomous integrity monitoring system, the current study illustrates the performance of the proposed fault detection algorithm.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.1
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• pp.1-14
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• 2023

Underwater images carry information that is useful in the fields of aquaculture, underwater military security, navigation, transportation, and so on. In this research, we transmitted an underwater image through various underwater mediums in the presence of underwater turbulence and beam attenuation effects using a high-speed visible optical carrier signal. The optical beam undergoes scintillation because of the turbulence and attenuation effects; therefore, distorted images were observed at the receiver end. To understand the behavior of the communication media, we obtained the bit error rate (BER) performance of the system with respect to the average signal-to-noise ratio (SNR). Also, the structural similarity index (SSI) and peak SNR (PSNR) metrics of the received image were evaluated. Based on the received images, we employed suitable nonlinear filters to recover the distorted images and enhance them further. The BER, SSI, and PSNR metrics of the specific nonlinear filters were also evaluated and compared with the unfiltered metrics. These metrics were evaluated using the on-off keying and binary phase-shift keying modulation techniques for the 50-m and 100-m links for beam attenuation resulting from pure seawater, clear ocean water, and coastal ocean water mediums.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.3
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• pp.64-72
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• 1999

The fictitious power theory in time domain is very easy to understand, but power analyzing speed of active power is increased, because power is analyzed using signal tedmiques based on the corre1ation between voltage and current wavefonns. Also, conventional methods in time/frequency domain to evaluate the correlation performance of active power filters are not provided easy solutions. So, the authors have previously proposed 3-D current coordinates which is composed into active component, fundarrental reactive component and distorted component of nonlinear loads current. This trethod has excellent perfonnance, but can not evaluate the characteristics of nonlinear load current whether It is inductive or capacitive. Therefore, To overcorre problems trentioned previously, this paper deals with the simple average power theory and the modified 3-D current coordinates for evaluating the compensation perfonnance of active power filters. To confirm the validity, active power filter simulator is developed using C-language. From the simulation, results are discussed their utility.tility.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.188-188
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• 2000

The most important problem in target tracking can be said to be modeling the tracking system correctly. Although the simple linear dynamic equation for this model has used until now, the satisfactory performance could not be obtained owing to uncertainties of the real systems in the case of designing the filters baged on the dynamic equations. In this paper, we propose the extended robust Kalman filter (ERKF) which can be applied to the real target tracking system with the parameter uncertainties. A nonlinear dynamic equation with parameter uncertainties is used to express the uncertain system model mathematically, and a measurement equation is represented by a nonlinear equation to show data from the radar in a Cartesian coordinate frame. To solve the robust nonlinear filtering problem, we derive the extended robust Kalman filter equation using the Krein space approach and sum quadratic constraint. We show the proposed filter has better performance than the existing extended Kalman filter (EKF) via 3-dimensional target tracking example.

• International Journal of Ocean Engineering and Technology Speciallssue:Selected Papers
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• v.5 no.1
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• pp.46-52
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• 2002

A robust nonlinear observer, utilizing the sliding mode concept, is developed for the dynamic positioning of ships. The observer provides the estimates of linear velocities of the ship and bias from slowly varying environmental loads. It also filters out wave frequency motion to avoid wear of actuators and excessive fuel consumption. The main advantage of the proposed observer is in its robustness. Especially, the observer structure with a saturation function makes the proposed observer robust against neglected nonlinearties, disturbances and uncertainties. Since the mathematical model of DP ships is difficult to obtain and includes uncertainties and disturbances, it is very important for the observer to be robust. A nonlinear output feedback controller is derives based on the developed observer using the observer backstepping technique, and the global stability of the observer and control law is shown by Lyapunov stability theory.. A set of simulation was carried out to investigate the performance of the proposed observer for dynamic positioning of ships.

• Earthquakes and Structures
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• v.5 no.3
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• pp.359-378
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• 2013

The problem of identification of multi-component and (or) spatially varying earthquake support motions based on measured responses in instrumented structures is considered. The governing equations of motion are cast in the state space form and a time domain solution to the input identification problem is developed based on the Kalman and particle filtering methods. The method allows for noise in measured responses, imperfections in mathematical model for the structure, and possible nonlinear behavior of the structure. The unknown support motions are treated as hypothetical additional system states and a prior model for these motions are taken to be given in terms of white noise processes. For linear systems, the solution is developed within the Kalman filtering framework while, for nonlinear systems, the Monte Carlo simulation based particle filtering tools are employed. In the latter case, the question of controlling sampling variance based on the idea of Rao-Blackwellization is also explored. Illustrative examples include identification of multi-component and spatially varying support motions in linear/nonlinear structures.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.9B
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• pp.1563-1569
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• 2000

Teager-Kaiser algorithm that can track the energy of tone signals with simple nonlinear operations, has been applied to detection of tone signals closely spaced in frequency. The performance of the previous detector depends upon the performance of internal filters. An improved scheme is proposed in this paper by employing the nonlinear energy trackers in cascade. The proposed detector is analytically designed and verified by computer simulation. It shows that the new detection scheme improves the detection performance while reducing the implementation complexity.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.37.2-37
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• 2001

This paper addresses the robust adaptive output feedback tracking for nonlinear systems under disturbances whose bounds are unknown. A new algorithm is proposed for estimation of unknown bounds and adaptive control of the uncertain nonlinear systems. The State estimation is solved using K-filters, together with the construction of a bound of an error in the state estimation due to the perturbation of the disturbance. Tuning functions are used to estimate unknown system parameters without overparametrization. The proposed control algorithm ensures that the out put tracking error converges to a residual set which can be arbitrarily small, while maintaining the boundedness of all other variables. A simulation shows the effectiveness of the proposed approach

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.255-260
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• 2006

Safety-critical navigation systems have to provide 'reliable' position solutions, i.e., they must detect and exclude measurement or system faults and estimate the uncertainty of the solution. To obtain more accurate and reliable navigation systems, various filtering methods have been employed to reduce measurement noise level, or integrate sensors, such as global navigation satellite system/inertial navigation system (GNSS/INS) integration. Recently, particle filters have attracted attention, because they can deal with nonlinear/non-Gaussian systems. In most GNSS applications, the GNSS measurement noise is assumed to follow a Gaussian distribution, but this is not true. Therefore, we have proposed a fault detection and exclusion method using particle filters assuming non-Gaussian measurement noise. The performance of our method was contrasted with that of conventional Kalman filter methods with an assumed Gaussian noise. Since the Kalman filters presume that measurement noise follows a Gaussian distribution, they used an overbounded standard deviation to represent the measurement noise distribution, and since the overbound standard deviations were too conservative compared to the actual distributions, this degraded the integrity-monitoring performance of the filters. A simulation was performed to show the improvement in performance of our proposed particle filter method by not using the sigma overbounding. The results show that our method could detect smaller measurement biases and reduced the protection level by 30% versus the Kalman filter method based on an overbound sigma, which motivates us to use an actual noise model instead of the overbounding or improve the overbounding methods.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.955-959
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• 2003

A technique for realizing linear phase IIR filters has been proposed by Powell-Chau which gives a real-time implementation of H(z-1).H(z), where H(z) is a causal nonlinear phase IIR filter. Powell-Chau system is linear but not timeinvariant system. Therefore, that system has group delay response that exhibits a minor sinusoidal variation superimposed on a constant value. In the signal processing, this oscillation seriously degrade the signal quality. Unfortunately, that system has a large sample delay of 4L and also more computational complexity. Proposed system is present a reduced computational complexity technique by moved the numerator polynomial of H(1/z) out to cascade with causal filter H(z) and remain only all-pole of H(1/z), then applied truncated infinite impulse response to finite with truncated IIR filtel $H_L$(z) and L sample delay to subtract the output sequence from the top and bottom filter. Proposed system is linear time invariance and group delay response and total harmonic distortion are also improved.