• Proceedings of the Korea Multimedia Society Conference
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• 2000.11a
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• pp.392-395
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• 2000

본 논문은 도로상에 고정된 카메라의 영상에 들어오는 여러 대의 차량을 추적(tracking)하기 위한 시스템에 대하여 연구하고자 한다. 제안된 차량 추적 시스템은 주변 환경 변화에 따른 배경 이미지 처리를 위하여 적응적 배경 모델(Adaptive Background Model)을 이용한 배경 영상과 연속되어 들어오는 입력 영상과의 차 영상을 이용한 차량 추출 부분과 칼만 필터를 이용하여 효과적으로 위치를 추적하기 위한 차량 추적 단계로 나누어 진다.

• 전자공학회논문지 IE
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• v.45 no.1
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• pp.24-30
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• 2008

In this paper, Digital MultiTone(DMT) is an emerging multi-carrier modulation scheme, which has been adopted for VDSL(Very high speed Digital Subscribe Line). A problem of DMT is its sensitivity to frequency offset between the transmitted and received carrier frequencies. This frequency offset introduces inter-carrier interference(ICI) in the DMT symbol. This paper is proposed an ICI cancelation scheme using Kalman Filtering. The performance of the proposed method is compared with conventional methods in terms of bit error rate performance, bandwidth efficiency, and computational complexity. Through simulations, it is shown that for high values of the frequency offset and for higher order modulation schemes, the EKF(Enhanced Kalman Filtering) method perform better than the others.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.652-654
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• 2012

본 논문에서는 다중 심도 카메라 기반의 실시간 피플 카운팅 시스템을 제안 한다. 카메라 영상으로부터 사람을 감지하고 추적하는 시스템 및 그 방법에 관한 것으로, 피플 카운팅 시스템은 쇼핑몰이나 대형건물의 출입구 등과 같은 다양한 환경에 적용될 수 있다. 기존 피플 카운팅 시스템에서의 급격한 조명의 변화나 겹침 현상, 가림 현상에 대한 해결 방법으로, 다중 심도 카메라 환경에서 동일 객체 추적을 위해 RLM(Range Laser Method)를 적용하고, 조명 등 환경 변화에 강인한 배경 제거 및 물체 검출 기법으로 가우시안 혼합 모델(Gaussian Mixture Model)을 적용해 객체인식에 대한 정확도를 높인다. 또한, 객체를 블랍(Blob)으로 지정해 확장 칼만 필터(Extended Kalman Filter, EKF) 방법으로 객체를 추적한다. 본 제안은 피플 카운팅 시스템에의 객체 검출 및 인식에 대한 정확도를 향상시킬 수 있으리라 기대된다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.06a
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• pp.160-162
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• 2017

본 논문은 칼만 필터를 이용한 다중 객체 추적 알고리즘에 대하여 다루고 있다. 기존의 객체 추적 알고리즘만을 이용하여 객체 추적을 하였을 경우, 잘못 검출되는 물체의 비율이 높았는데, 이를 해결하기 위하여, 본 실험에서는 움직이는 물체에 집중하여, 객체 추적을 하는 방법에 대하여 연구하였다. 효과적인 객체 추적을 위하여, 우리는 우선 배경 분리 알고리즘의 결과 이미지에서 객체의 후보들을 찾았다. 실험적인 결과를 통해 비디오에서 오직 움직이는 물체에만 집중함으로써 우리는 효과적이고 효율적으로 객체를 추적할 수 있다는 것을 알 수 있었다.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1765-1766
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• 2008

본 논문에서는, 전동기를 이용하는 위치제어 시스템을 이용하여, 이너루프 제어기 유무에 따라 시간 응답 특성 개선 효과에 대하여 실험을 통해 검증하고자 한다. 실험 플랜트로서, 전동기의 속도에 따라 진자의 위치를 제어하는 다중 안정도를 가지고 비선형 시스템인 아라고 진자 시스템을 이용한다. 이 시스템은 속도 정보를 알 수 있는 측정 장치가 없는데, 칼만필터로 전동기의 속도를 추정하고 속도 제어기를 설계하도록 한다. 또한, 선형 시스템을 고려하기 위하여 안정영역 동작점인 진자의 각도가 지면으로 부터 45$^{\circ}$인 선형화 모델을 이용한다.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.29-39
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• 2019

As mobile robot applications increase in real life, the need of low cost autonomous driving are gradually increasing. We propose a novel vision-based real-time lane detection and tracking system that supports autonomous driving of mobile robots in constrained tracks which are designed considering indoor driving conditions of mobile robots. Considering the processing of lanes with various shapes and the pre-adjustment of operation parameters, the system structure with multi-operation modes are designed. In parameter tuning mode, thresholds of the color filter is dynamically adjusted based on the geometric property of the lane thickness. And in the unstable input mode of curved tracks and the stable input mode of straight tracks, lane feature pixels are adaptively extracted based on the geometric and temporal characteristics of the lanes and the lane model is fitted using the least-squared method. The track centerline is calculated using lane models and the motion model is simplified and tracked by a linear Kalman filter. In the driving experiments, it was confirmed that even in low-performance robot configurations, real-time processing produces the accurate autonomous driving in the constrained track.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.5
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• pp.93-100
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• 2006

This paper presents a multi-sensor data simulator and a data fusion algorithm for tracking high dynamic flight target from Radar and Telemetry System. The designed simulator generates time-asynchronous multiple sensor data with different data rates and communication delays. Measurement noises are incorporated by using realistic sensor models. The proposed fusion algorithm is designed by a 21st order distributed Kalman Filter which is based on the PVA model with sensor bias states. A fault detection and correction logics are included in the algorithm for bad data and sensor faults. The designed algorithm is verified by using both simulation data and actual real data.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.11
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• pp.95-101
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• 1999

In this paper, we propose an adaptive method of tracking multiple moving objects using contour and features in surrounding conditions. We use an adaptive background model for robust processing in surrounding conditions. Object segmentation model detects pixels thresholded from local difference image between background and current image and extracts connected regions. Data association problem is solved by using feature extraction and object recognition model in searching window. We use Kalman filters for real-time tracking. The results of simulation show that the proposed method is good for tracking multiple moving objects in highway image sequences.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.175-189
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• 2023

Fault detection and diagnosis (FDI) algorithms are actively being researched for ensuring the integrity and reliability of environment perception sensors in autonomous vehicles. In this paper, a fault detection algorithm based on a multi-sensor perception system composed of radar, camera, and lidar is proposed to guarantee the safety of an autonomous vehicle's perception system. The algorithm utilizes reference generation filters and residual generation filters based on finite impulse response (FIR) filter estimates. By analyzing the residuals generated from the filtered sensor observations and the estimated state errors of individual objects, the algorithm detects faults in the environment perception sensors. The proposed algorithm was evaluated by comparing its performance with a Kalman filter-based algorithm through numerical simulations in a virtual environment. This research could help to ensure the safety and reliability of autonomous vehicles and to enhance the integrity of their environment perception sensors.