一种面向异构传感器的无人艇海面目标识别跟踪系统

Sea surface target recognition and tracking system for unmanned surface vehicle with heterogeneous sensors

  • 摘要:
      目的  为了应对无人艇在海洋环境下执行任务时,通过导航雷达与一体化光电设备对远距离海面目标识别与跟踪的需求,开发一种同时具备目标航迹预测、光电摄像头姿态补偿与修正等功能的无人艇自主感知系统。
      方法  通过对导航雷达输出的目标位置信息使用基于卡尔曼滤波的目标航迹预测算法,提高目标定位精度,同时为光电摄像头提供实时性更好的目指信息;采用基于船舶姿态的光电摄像头姿态补偿算法,完成光电摄像头对目标的图像采集、识别与跟踪。
      结果  搭载本感知系统的无人艇在3级海况下完成了动态目标识别跟踪测试,并将目标跟踪误差降低了6%,目标识别成功率提高到96.25%,验证了本感知系统的环境适应能力。
      结论  通过试验验证,本识别跟踪系统可以解决海面目标图像采集难、识别效果差的问题,有效提高目标识别成功率。

     

    Abstract:
      Objectives  In order to meet the needs of long-distance sea target recognition and tracking through the navigation radar and integrated optoelectronic equipment of an unmanned ship operating in a marine environment, a novel unmanned ship autonomous sensing system is developed with heterogeneous sensor association, target track prediction and photoelectric camera attitude compensation.
      Methods  By using the target track prediction algorithm based on a Kalman filter for the target position information output by the navigation radar, target positioning accuracy can be improved and real-time target information can be provided to the photoelectric camera. The posture compensation algorithm based on the ship's posture in the photoelectric camera is used to complete the tasks of target image collection, recognition and tracking. An unmanned surface vehicle equipped with our proposed sensing system has completed dynamic target recognition and tracking tasks under Sea State 3 conditions.
      Results  The target tracking error is reduced by 6% and the target recognition success rate is increased to 96.25%, which verifies the environmental adaptability of this sensing system.
      Conclusions  Through testing experiments, the proposed recognition and tracking system can effectively solve the problems of difficult image acquisition and poor recognition effects of sea surface targets, effectively improving the success rate of target recognition.

     

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