• Smart Structures and Systems
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• v.11 no.1
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• pp.103-122
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• 2013

It is essential to update the model with reflecting observation or inspection data for reliability estimation of existing structures. Authors proposed updated reliability analysis by using Particle Filter. We discuss how to apply the proposed method through numerical examples on reinforced concrete structures after verification of the method with hypothetical linear Gaussian problem. Reinforced concrete structures in a marine environment deteriorate with time due to chloride-induced corrosion of reinforcing bars. In the case of existing structures, it is essential to monitor the current condition such as chloride-induced corrosion and to reflect it to rational maintenance with consideration of the uncertainty. In this context, updated reliability estimation of a structure provides useful information for the rational decision. Accuracy estimation is also one of the important issues when Monte Carlo approach such as Particle Filter is adopted. Especially Particle Filter approach has a problem known as degeneracy. Effective sample size is introduced to predict the covariance of variance of limit state exceeding probabilities calculated by Particle Filter. Its validity is shown by the numerical experiments.

• Journal of IKEEE
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• v.25 no.4
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• pp.762-765
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• 2021

In this paper, an automotive ECU (electronic control unit) with Kalman filter accelerator is designed and implemented. RISC-V is exploited as a processor core. Accelerator for Kalman filter matrix operation, CAN (controller area network) controller for in-vehicle network, and LIN (local interconnect network) controller are designed and embedded. Kalman filter operation consists of time update process and measurement update process. Current state variable and its error covariance are estimated in time update process. Final values are corrected from input measurement data and Kalman gain in measurement update process. Usually floating-point multiplication is exploited in software implementation, but fixed-point multiplier considering accuracy analysis is exploited in this paper to reduce hardware area. In 28nm silicon fabrication, its operating frequency, area, and gate counts are 100MHz, 0.37mm², and 760k gates, respectively.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.5
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• pp.583-597
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• 2001

Tracking the targets of interest has been one of the major research areas in radar surveillance system. We formulate the tracking problem as an incomplete data problem and apply the EM algorithm to obtain the MAP estimate. The resulting filter has a recursive structure analogous to the Kalman filter. The difference is that the measurement-update deals with multiple measurements and the parameter-update can estimate the system parameters. Through extensive experiments, it turns out that the proposed system is better than PDAF and NNF in tracking the targets. Also, the performance degrades gracefully as the disturbances become stronger.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.12
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• pp.59-66
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• 1993

In this paper we propose an adaptive update rate tracking algorithm for a phased array radar, based on the interacting multiple model(IMM) algorithm. The purpose of the IMM algorithm hers is twofold: 1) to estimate and predict the target states, and 2) to estimate the level of the process noise. Using the estimate of the process noise level adapted to target dynamics, the update interval is determined to maintain a desired prediction accuracy so that the radar system load is minimized. The adaptive update rate tracking algorithm is implemented for a phased array radar and evaluated with Monte Carlo simulations on various trajectories. The evaluation results of the proposed algorithm and a standard Kalman filter without the adaptive update rate control are presented to compare.

• 전기의세계
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• v.31 no.2
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• pp.119-125
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• 1982

A new approach to the maneuvering target tracking problem is proposed. Its basic concept is to take the maneuver variable from the measurements. Tracking scheme based on the Kalman filter estimates the maneuver varieble from the residual and uses the estimates to update the Kalman filter. The estimation process is independent of target types and a model of the maneuver characteristics. All the filtering algorithms are processed in polor coordinate. Simulation results are presented and compared to that of the extended Kalman filter.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.50 no.3
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• pp.130-137
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• 2001

In this paper, we propose a new Active Noise Filter Control System which operate as a control performance when a adaptive filter fault. In this system, half-fixed filter which is new filter, connected to parallel with adaptive filter. An adaptive filler use to continuous parameter estimating, but adaptive filter is fault, half-fixed filter update newly data which is continuous estimating date each during sampling period. We simulate and apply the proposed active noise filter system to in the cylinder type duct. Experimental results show that proposed Active Noise Filter Control System has better control performance than existing filter which Eriksson's or Parallel Filter System in term of noise reduction.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.321-324
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• 1997

In this paper, Active Noise Control(ANC) algorithm is proposed based on the estimated frequency estimator of the reference signal. The conventional feedforward ANC algorithms should measure the reference and use it to calculate the gradient of the squared error and filter coefficients. For ANC systems applied to aircrafts and passenger ships, engines from which reference signal is usually measured is so far from seats where main part of controller is placed that the scheme might be difficult to implement or very costly. Feedback ANC algorithm which doesn't need to measure the reference uses the error signal to update the filter and is sensitive to unexpected transient noise like a sneeze, clapping of hands and so on The proposed algorithm estimates frequencies of the desired signal in real time using adaptive notch filter. New frequency estimation algorithm is proposed with the improved convergence rate, threshold SNR and computational simplicity. Reference is not measured but created with the estimated frequencies. It has strong similarity to the conventional feedback control because reference is made from error signal. Enhanced error signal is used to update the controller for better performance under the measurement noise and impact noise. The proposed ANC algorithm is compared with the conventional feedback control.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.1
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• pp.25-32
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• 2017

Most of weapon systems use aided navigation system which integrates inertial navigation and aiding sensors to compensate the INS errors increasing with the passage of time. Various aid sensors can be applied such as Global Navigation Satellite System (GNSS), radar, barometer, etc., but there might exist time delay caused by signal processing or transferring aid information. This time delay leads out-of-sequence measurements (OOSM) systems. Previously, optimal and suboptimal measurment update method for OOSM systems, where the time delay length are known, are proposed. However, previous algorithm does not guarantee the positive definite property of covariance matrix. In order to improve numerical stability for aided navigation using delayed-measurement, this paper proposes a new measurement covariance update algorithm be similar to Joseph-form in Kalman filter. Futhermore, we propose how to implement it in indirect feedback Kalman filter structure, which is commonly used in aided navigation systems, for time-delayed measurement systems. Simulation and vehicle test results show effectiveness of a proposed algorithm.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.2
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• pp.118-123
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• 2011

Video based object tracking normally deals with non-stationary image streams that change over time. Robust and real time moving object tracking is considered to be a problematic issue in computer vision. Multiple object tracking has many practical applications in scene analysis for automated surveillance. In this paper, we introduce a specified object tracking based particle filter used in an environment of multiple moving objects. A differential image region based tracking method for the detection of multiple moving objects is used. In order to ensure accurate object detection in an unconstrained environment, a background image update method is used. In addition, there exist problems in tracking a particular object through a video sequence, which cannot rely only on image processing techniques. For this, a probabilistic framework is used. Our proposed particle filter has been proved to be robust in dealing with nonlinear and non-Gaussian problems. The particle filter provides a robust object tracking framework under ambiguity conditions and greatly improves the estimation accuracy for complicated tracking problems.

• Journal of Electrical Engineering and information Science
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• v.3 no.1
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• pp.126-130
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• 1998

In this paper, a new interference rejection filter using the data-recycling technique is presented. The reference signal of the interference rejection filter is formed by using chip decisions, which is correlated with the narrowband interference components of the received signal. The decision feedback techniques reduce the distortion of the desired signal, which is introduced by the interference rejection filter through he use of feedback chip decisions. In order to update the filter coefficients a simple and efficient data-recycling technique that has improved performance over the conventional LMS algorithm is used. The performance of the proposed interference rejection filter is compared to filter with conventional LMS linear filter. The results show that the convergence speed of the proposed interference rejection filter using decision feedback and data-recycling technique is significantly faster than that of the conventional filters. Also, BER performance of the interference rejection filter demonstrates the superiority of the proposed filter algorithm.