• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.26 no.3
• /
• pp.263-273
• /
• 2008

The precision of sensors' position is particularly important in the application of road extraction or digital map generation. In general, the various ranging solution systems such as GPS, Total Station, and Laser Ranger have been employed for the position of the sensor. Basically, the ranging solution system has problems that the signal may be blocked or degraded by various environmental circumstances and has low temporal resolution. To overcome those limitations a IMU/range integrated system could be introduced. In this paper, after pointing out the limitation of extended Kalman filter which has been used for workhorse in navigation and geodetic community, the two sampling based nonlinear filters which are sigma point Kalman filter using nonlinear transformation and carefully chosen sigma points and particle filter using the non-gaussian assumption are implemented and compared with extended Kalman filter in a simulation test. For the ranging solution system, the GPS and Total station was selected and the three levels of IMUs(IMU400C, HG1700, LN100) are chosen for the simulation. For all ranging solution system and IMUs the sampling based nonlinear filter yield improved position result and it is more noticeable that the superiority of nonlinear filter in low temporal resolution such as 5 sec. Therefore, it is recommended to apply non-linear filter to determine the sensor's position with low degree position sensors.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.15 no.12
• /
• pp.1031-1043
• /
• 1990

A sequential recursive Kalman filtering algorithm, using causal image model, which is designed to operate in real time in the scanning mode is developed to enhance quality of 64Kbps videocodec images via function of suppression of various noises and optimum restoration. In order to improve its performance, adapted an averaging of pixel values between processing lines and adaptive filtering strategy based on the local spatial variance. Effecttiveness of the Kalman filtering algorithm proposed has been proved in the processed test kalman filtering algorithm proposed has been proved in the processed test images and the NMSE, LOGMSE measured, therefore, it may proposes possibility of the usage in videocodec for pre- and post- processing.

• The Journal of the Acoustical Society of Korea
• /
• v.18 no.8
• /
• pp.42-47
• /
• 1999

In this paper, a mixture interacting multiple model (MIMM) algorithm is proposed to enhance speech contaminated by additive nonstationary noise. In this approach, a mixture hidden filter model (HFM) is used to model the clean speech and the noise process is modeled by a single hidden filter. The MIMM algorithm, however. needs large computation time because it is a recursive method based on multiple Kalman filters with mixture HFM. Thereby, a computationally efficient implementation of the algorithm is developed by exploiting the structure of the Kalman filtering equation. The simulation results show that the proposed method offers performance gain compared to the previous results in [4,5] with slightly increased complexity.

• Journal of the Korea Convergence Society
• /
• v.11 no.6
• /
• pp.7-14
• /
• 2020

A Battery Monitoring System capable of State-of-Charge(SOC) estimation using the Extended Kalman Filter(EKF) is described in this paper. In order to accurately estimate the SOC of the battery, the battery cells were modeled as the Thevenin equivalent circuit model. The Thevenin model's parameters were measured in experiments. For the Battery Monitoring System, we designed a battery monitoring device that can calculate the SOC estimation using the EKF and a monitoring server that controls multiple battery monitoring devices. We also develop a web-based dashboard for controlling and monitoring batteries. Especially the computation of the monitoring server could be reduced by calculating the battery SOC estimation at each Battery Monitoring Device.

• Journal of Broadcast Engineering
• /
• v.20 no.4
• /
• pp.568-579
• /
• 2015

Recently, mobile devices with touch display panel are widely used. Accuracy and reaction speed of touch signal are very important in touch devices. Therefore, we need to develop an effective algorithm to reduce touch noise quickly and accurately. This paper proposes a touch noise reduction algorithm using Kalman filtering in consideration of signal motion. First, a specific pre-emphasis processing is applied to an input signal so as to maximize the effect of Kalman filtering. In other words, a pure signal in the touch signal increases but noise in the touch signal decreases. Next, motion of the signal is detected. Motion estimation is performed only if motion is detected. If we detect motion by using the only neighborhood of the signal, we can reduce about 75% of the computation in comparison with examining the entire area. Finally, Kalman filtering using the previous state of current signal is performed. Experimental results show that the proposed algorithm suppresses touch noise sufficiently without degradation of the pure signal

• Journal of the Institute of Convergence Signal Processing
• /
• v.14 no.4
• /
• pp.243-248
• /
• 2013

In wireless sensor networks, consensus algorithms for dynamic systems may flexibly usable for their data fusion of a sensor network. In this paper, a distributed data fusion filter is implemented using an average consensus based on distributed sensor data, which is composed of some sensor nodes and a sink node to track the mean values of n sensors' data. The consensus filter resolve the problem of data fusion by a distribution Kalman filtering scheme. We showed that the consensus filter has an optimal convergence to decrease of noise propagation and fast tracking ability for input signals. In order to verify for the results of consensus filtering, we showed the output signals of sensor nodes and their filtering results, and then showed the result of the combined signal and the consensus filtering using zeegbee communication.

• Journal of the Korea Society for Simulation
• /
• v.19 no.4
• /
• pp.199-208
• /
• 2010

Positions of tactical objects are represented as Time, Space and Position Information(TSPI) in modeling and simulations(M&S). The format and required information record for TSPI is investigated by referring the TSPI object model of the Test and Training Enabling Architecture(TENA), which has been developed by the United States Department of Defense. The most sophisticated tactical data link, Link-16 has a Precise Participant Location and Information (PPLI) message. We study the data format for exchanging TSPI data based on the PPLI message. To evaluate and track positions of tactical objects, we consider the Kalman filter for linear systems, and the extended Kalman filter and the unscented Kalman filter for nonlinear systems. Based on motion equations of a ballistic missile, the tracking performance for the trajectory of the ballistic missile is simulated by the unscented Kalman filter.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2003.05b
• /
• pp.695-698
• /
• 2003

No Abstract, See Full Text

• Proceedings of the KIEE Conference
• /
• 2004.07d
• /
• pp.2335-2337
• /
• 2004

Kalman filter를 필터링에 적용할 때에 센서의 아날로그 신호에 들어오는 측정값의 잡음은 Gaussian 확률분포를 갖는다고 가정한다. 그러나 Kalman filter를 digital 컴퓨터에 적용할 경우에는 analog-to-digital converter에서 측정값의 잡음이외에도 quantization 잡음이 존재하며 본 논문에서는 이러한 경우에 quantization 영향이 Kalman filter 알고리듬에 미치는 영향을 수치적으로 분석하여 quantization을 Kalman filter 구현에 고려해야 될 사항으로 분류하고자 한다.

• Journal of the Society of Naval Architects of Korea
• /
• v.57 no.3
• /
• pp.175-181
• /
• 2020

For the success of a structural integrity management, it is essential to acquire structural response data at some critical locations with limited number of sensors. In this study, the structural response of numerical model was estimated by data fusion approach based on the Kalman filter known as stochastic recursive filter. Firstly, transient direct analysis was conducted to calculate the acceleration and strain of the numerical standing beam model, then the noise signals were mixed to generate the numerical measurement signals. The acceleration measurement signal was provided to the Kalman filter as an information on the external load, and the displacement measurement, which was transformed from the strain measurement by using strain-displacement conversion relationship, was provided into the Kalman filter as an observation information. Finally, the Kalman filter estimated the displacement by combining both displacements calculated from each numerically measured signal, then the estimated results were compared with the results of the transient direct analysis.