• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.323-324
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• 2008

In this paper, we present performance comparison of two methods to localize multiple robots. One is extended Kalman filter and the other is constraint propagation technique. Extended Kalman filter is conventional probabilistic method which gives the sub-optimal estimation rather than guarantee any boundary for true position of robot. In case of constraint propagation, it can give a boundary containing true robot position value. Especially, we deal with cooperative localization problem in outdoor environment for multiple robots equipped with GPS, gyro meter, wheel encoder. In simulation results, we present strength and weakness for localization methods based on extend Kalman filter and constraint propagation technique.

• Journal of the Korean Geophysical Society
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• v.9 no.3
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• pp.231-240
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• 2006

The crustal structure of the Korean Peninsula was investigated by analyzing phase velocity dispersion data of Rayleigh waves. Earthquakes recorded by three component broad-band velocity seismographs during 1999-2004 in South Korea were used in this study. The fundamental mode Rayleigh waves were extracted from vertical components of seismograms by multiple filter technique and phase match filter method. Phase velocity dispersion curves of the fundamental mode signal pairs for 14 surface wave propagation paths on the great circle in the range 10 to 80 sec were computed by two-station method. Treating the shear velocity of each layer as an independent parameter, phase velocity data of Rayleigh wave were inverted. All the result models can be explained by a rather homogeneous crust of shear-wave velocity increasing from 2.8 to 3.25 km/sec from top to about 33 km depth without any distinctive crustal discontinuities and an uppermost mantle of shear-wave velocity between 4.55 and 4.67 km/sec. Our results turn out to agree well with recent study of Cho et al. (2006 b) based on the analysis of seismic background noises to recover short-period (0.5-20 sec) Rayleigh- and Love-wave group velocity dispersion characteristics.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1278-1282
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• 2003

A new interacting multiple model (IMM) method using intelligent input estimation (IIE) is proposed to track a maneuvering target. In the proposed method, the acceleration level for each sub-model is determined by IIE-the estimation of the unknown acceleration input by a fuzzy system using the relation between maneuvering filter residual and non-maneuvering one. The genetic algorithm (GA) is utilized to optimize a fuzzy system for a sub-model within a fixed range of acceleration input. Then, multiple models are composed of these fuzzy systems, which are optimized for different ranges of acceleration input. In computer simulation for an incoming ballistic missile, the tracking performance of the proposed method is compared with those of the input estimation (IE) technique and the adaptive interacting multiple model (AIMM) method.

• 한국지구물리탐사학회:학술대회논문집
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• 2005.05a
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• pp.29-36
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• 2005

The crustal structure of Korean Peninsula is investigated by analyzing phase velocity dispersion data of Rayleigh wave. Earthquakes recorded by three component seismographs during 1999 - 2004 in South Korea are used in this study. The fundamental mode signals of Rayleigh waves are obtained from vertical components of seismograms by multiple filter technique method and phase match filter method. Velocity dispersion curves of surface waves for 14 propagation paths on the great circle are computed from the fundamental mode signals on the great circle path by two-station method. Treating the shear velocity of each layer as an independent parameter, phase velocities of Rayleigh wave are inverted. The result models are regarded as average structure for surface wave propagation paths respectively. All the results can be explained by an earth model of the Korean Peninsula comprising crust of shear-wave velocity increasing from 2.8 to 3.25 km/sec from top to 33 km depth and uppermost mantle of shear-wave velocity between 4.55 and 4.67 km/sec.

• The Journal of the Acoustical Society of Korea
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• v.21 no.4
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• pp.387-393
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• 2002

The enhancement technique of noise signal using mixture HFM (Midden Filter Model) are proposed. Given the parameters of the clean signal and noise, noisy signal is modeled by a linear state-space model with Markov switching parameters. Estimation of state vector is required for estimating original signal. The estimation procedure is based on mixture interacting multiple model (MIMM) and the estimator of speech is given by the weighted sum of parallel Kalman filters operating interactively. Simulation results showed that the proposed method offers performance gains relative to the previous results with slightly increased complexity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2483-2503
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• 2016

Traffic congestion is a severe problem in many modern cities around the world. Real-time and accurate traffic congestion identification can provide the advanced traffic management systems with a reliable basis to take measurements. The most used data sources for traffic congestion are loop detector, GPS data, and video surveillance. Video based traffic monitoring systems have gained much attention due to their enormous advantages, such as low cost, flexibility to redesign the system and providing a rich information source for human understanding. In general, most existing video based systems for monitoring road traffic rely on stationary cameras and multiple vehicle tracking method. However, most commonly used multiple vehicle tracking methods are lack of effective track initiation schemes. Based on the motion of the vehicle usually obeys constant velocity model, a novel vehicle recognition method is proposed. The state of recognized vehicle is sent to the GM-PHD filter as birth target. In this way, we relieve the insensitive of GM-PHD filter for new entering vehicle. Combining with the advanced vehicle detection and data association techniques, this multiple vehicle tracking method is used to identify traffic congestion. It can be implemented in real-time with high accuracy and robustness. The advantages of our proposed method are validated on four real traffic data.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.2
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• pp.284-290
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• 2012

This paper addresses a new passive target tracking problem using the range differences measured by cooperative UAVs. In order to solve the range difference based passive target tracking problem within the framework of linear robust state estimation, the uncertain linear measurement model which contains the stochastic parameter uncertainty is derived by using the noisy range difference measurements. To cope with the performance degradation due to the stochastic parameter uncertainty, the recently developed non-conservative robust Kalman filtering technique [1] is applied. For the cruciform formation flying UAVs, the relationship between the target tracking performance and the measurement errors is quantitatively analyzed. The proposed filter has practical advantages over the classical nonlinear filters because, for its recursive linear structure, it can provide satisfactory convergence properties and is suitable for real-time multiple UAVs applications. Through the simulations, the usefulness of the proposed method is demonstrated.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.5
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• pp.190-196
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• 2015

In this paper, we propose multiple location error compensation algorithm for GPS/INS fusion using kalman filter and introduce the way to reduce location error in 9-axis navigation devices for implementing inertial navigation technique. When evaluating location, there is an increase of location error. So navigation systems need robust algorithms to compensate location error in GPS/INS fusion. In order to improve robustness of 9-axis inertial sensor(mpu-9150) over its disturbance, we used tilt compensation method using compensation algorithm of acceleration sensor and Yaw angle compensation to have exact azimuth information of the object. And it shows improved location result using these methods combined with kalman filter.

• International Journal of Aeronautical and Space Sciences
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• v.7 no.1
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• pp.73-83
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• 2006

In this paper, a redundancy management system for aircraft is studied, and fault detection and isolation algorithms of inertial sensor system are proposed. Contrary to the conventional aircraft systems, UAV system cannot allow triple or quadruple hardware redundancy due to the limitations on space and weight. In the UAV system with dual sensors, it is very difficult to identify the faulty sensor. Also, conventional fault detection and isolation (FDI) method cannot isolate multiple faults in a triple redundancy system. In this paper, two FDI techniques are proposed. First, hardware based FDI technique is proposed, which combines a parity equation approach with a wavelet based technique. Second, analytic FDI technique based on the Kalman filter is proposed, which is a model-based FDI method utilizing the threshold value and the confirmation time. To provide the reference value for detecting the fault, residuals are calculated using the extended Kalman filter. To verify the effectiveness of the proposed FDI methods, numerical simulations are performed.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.6
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• pp.764-776
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• 1999

In this paper, a tracking algorithm for a maneuvering single target in the presence of multiple data from multiple sensors is investigated. Allowing individual sensors to function by themselves, the estimates from individual sensors on the same target are fused for the purpose of improving the state estimate. The filtering method adopted in the local sensors is the variable dimensional filter with input estimatio technique, which consists of a constant velocity model and a constant acceleration model. A posteriori probability for the maneuvering hypothesis is newly derived. It is shown that the relation function of the a posteriori probability is a function of only the covariance of the fused estimates. Simulation results are provided.