摘要
针对基于传感器能量估计的目标跟踪问题,本文通过扩展卡尔曼滤波器(EKF)来最小化估计误差,在目标跟踪优化问题中引入具有稀疏特性惩罚函数,得到稀疏卡尔曼增益矩阵,利用引入项惩罚与活动传感器数量相对应的卡尔曼增益矩阵中的非零列向量,由此只需少数传感器将测量数据传送到数据融合中心,从而节省多数传感器能量。分析结果表明,相对于要求所有传感器传输信号到数据融合中心的标准扩展卡尔曼滤波器,采用具有稀疏卡尔曼增益矩阵的扩展卡尔曼滤波器可以实现与前者非常相近的跟踪性能。
It is studied the problem of target tracking based on energy estimatings of sensors.The estimation error is minimized by using an Extended Kalman Filter(EKF).As the solution to an optimization problem in which a sparse penalty function is added to the objective,the sparse Kalman gain matrix is acquired.The added term penalizes the number of nonzero columns of the Kalman gain matrix,which corresponds to the number of active sensors.By using the Kalman gain matrix,only a few sensors send their measurements to the data fusion center,consequently saving energy of most sensor.Analysis results show that an EKF with a sparse Kalman gain matrix can achieve tracking performance that is very close to that of the classical EKF,where all sensors transmit to the fusion center.
作者
沈颖
黎泽伦
SHEN Ying;LI Zelun(Department ofMechatronics Engineering,Chongqing University of Science and Technology,Chongqing 400042,China)
出处
《智能物联技术》
2022年第6期1-3,22,共4页
Technology of Io T& AI
基金
重庆市自然科学基金资助项目(cstc2018jcyjAX0291)。
