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Kalman filter observation error model applied to vehicle tracking dynamic obstacle correction

Author

Listed:
  • S. M. Siao

    (Automotive Research & Testing Center, Changhua, Taiwan)

  • Y. R. Chen

    (Automotive Research & Testing Center, Changhua, Taiwan)

  • L. Y. Shu

    (Automotive Research & Testing Center, Changhua, Taiwan)

Abstract

In order to increase accuracy for the detection of a dynamic obstacle, this paper presents a Kalman filter observation error model based on 77 GHz Middle Range Radar (MRR). Recently, the vehicle has been equipped with an Advanced Driver Assistance System (ADAS), which has become more popular. Among these, the accuracy of the obstacle information, such as distance, relative speed and position are the most important purpose. However, radar is one of the main detection sensors, but its data transmission delay, which includes millimeter-wave reflection, an analogy to digital signal conversion, and data process, would influence radar tracking correction. Therefore, the algorithm of the proposed model adopts detection time and a dynamic estimated model of the obstacle to compensate for detection delay, such that the dynamic correction mean error can be reduced. Next, the testing condition sets RTK-GPS as the real-world reference frame; the experiment would be realized with a dynamic scene. According to the above results, when the target distance is closer than 100 meters, the dynamic correction mean error of our model was improved by 59%, 74%, and 78% in 20kph, 40kph, and 60kph relative speed, respectively.

Suggested Citation

  • S. M. Siao & Y. R. Chen & L. Y. Shu, 2019. "Kalman filter observation error model applied to vehicle tracking dynamic obstacle correction," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 5(3), pages 116-124.
    • Handle: RePEc:apb:jaterr:2019:p:116-124
    DOI: 10.20474/jater-5.3.2
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    References listed on IDEAS

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    More about this item

    Keywords

    ADAS; Kalman filter; Radar;
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