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提出一种面向煤矿井下线性拟合和卡尔曼滤波相结合的改进分布式目标跟踪算法。根据移动目标的当前位置建立动态簇,簇头节点集中处理簇成员节点发来的最新观测数据,结合线性拟合算法和卡尔曼滤波算法对移动目标进行预测,将线性拟合的预测值和卡尔曼滤波预测值作为真正的预测值,得到目标的状态估计,通过这样的改进可实时的修正预测值。仿真结果表明,与传统的分布式目标跟踪算法比较,改进算法集中了2种算法的优点,有很好的跟踪性能。
An improved distributed target tracking algorithm based on the combination of linear fitting and Kalman filter in coal mine is proposed. According to the current position of the moving object, dynamic cluster is established. The cluster head node centralizes the latest observation data sent from the cluster member nodes, predicts the moving target by combining the linear fitting algorithm and the Kalman filter algorithm, The predicted value of the Mann filter is taken as the true predicted value to get the state estimation of the target. Through such improvement, the predicted value can be corrected in real time. Simulation results show that, compared with the traditional distributed target tracking algorithm, the improved algorithm focuses on the advantages of the two algorithms and has good tracking performance.