[논문]다중주기 칼만 필터를 이용한 비동기 센서 융합

손영섭; 김원희; 이승희; 정정주

doi:10.5370/kiee.2014.63.11.1551

[국내논문] 다중주기 칼만 필터를 이용한 비동기 센서 융합
Asynchronous Sensor Fusion using Multi-rate Kalman Filter 원문보기

전기학회논문지 = The Transactions of the Korean Institute of Electrical Engineers, v.63 no.11, 2014년, pp.1551 - 1558

손영섭 (Dept. of Electrical Engineering, Hanyang Universty, Korea and Global R&D Center, MANDO Corp.) , 김원희 (Dept. of Electrical Engineering, Dong-A University) , 이승희 (Div. of Electrical and Biomedical Engineering, Hanyang University) , 정정주 (Div. of Electrical and Biomedical Engineering, Hanyang University)

Abstract ▼ AI-Helper

We propose a multi-rate sensor fusion of vision and radar using Kalman filter to solve problems of asynchronized and multi-rate sampling periods in object vehicle tracking. A model based prediction of object vehicles is performed with a decentralized multi-rate Kalman filter for each sensor (vision and radar sensors.) To obtain the improvement in the performance of position prediction, different weighting is applied to each sensor's predicted object position from the multi-rate Kalman filter. The proposed method can provide estimated position of the object vehicles at every sampling time of ECU. The Mahalanobis distance is used to make correspondence among the measured and predicted objects. Through the experimental results, we validate that the post-processed fusion data give us improved tracking performance. The proposed method obtained two times improvement in the object tracking performance compared to single sensor method (camera or radar sensor) in the view point of roots mean square error.

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문제 정의

This paper investigates multi-object vehicle tracking using a fusion of asynchronous sensors such as radar and vision sensors. We propose a multi-rate sensor fusion using a Kalman filter for object vehicle tracking.

제안 방법

The proposed method allows us to implement prediction and correction steps at the ECU sampling rate [9] [10]. It is experimentally validated that the proposed method provides the improved estimation of object positions at every sampling time of the ECU. For object tracking, it is necessary for each sensor to link between measured objects and predicted ones at every time when sensor information is updated.
The vehicle detection and tracking methods using only vision sensor were developed [6], [7]. The methods were designed based on an edge-based constraint filter. However, the vision system cannot guarantee satisfactory performance under various environmental conditions such as light change in the day and night times, poor weather conditions, and lack of light source.
) To obtain an improvement in the estimated object position, different weighting is applied to each sensor’s predicted object position from the multi-rate Kalman filter: large weighting on the lateral position from vision and longitudinal position from radar. The proposed method allows us to implement prediction and correction steps at the ECU sampling rate [9] [10]. It is experimentally validated that the proposed method provides the improved estimation of object positions at every sampling time of the ECU.

참고문헌 (16)

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상세보기
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상세보기
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상세보기
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상세보기
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상세보기
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[국내논문] 다중주기 칼만 필터를 이용한 비동기 센서 융합
Asynchronous Sensor Fusion using Multi-rate Kalman Filter 원문보기

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[국내논문] 다중주기 칼만 필터를 이용한 비동기 센서 융합 Asynchronous Sensor Fusion using Multi-rate Kalman Filter 원문보기

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[국내논문] 다중주기 칼만 필터를 이용한 비동기 센서 융합
Asynchronous Sensor Fusion using Multi-rate Kalman Filter 원문보기

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