基于改进卡尔曼滤波算法的煤矿井下跟踪方法

    Underground Coal Mines Tracking Way Based on Improved Kalman Filter Algorithm

    • 摘要: 煤矿井下环境特殊,受限的多径衰落无线信道对跟踪技术提出了极大的挑战。提出了指纹匹配和卡尔曼滤波相结合的井下跟踪算法。首先利用卡尔曼-均值滤波器对采样RSSI值进行去噪等处理,然后执行指纹匹配算法得到观测点处的位置坐标作为跟踪中的观测值,并与真实值估算出井下观测噪声方差,最后采用改进的卡尔曼滤波算法跟踪目标运动轨迹。试验结果表明,改进的算法能够满足矿井下目标的跟踪精度及误差的要求,增强了跟踪系统的可靠性。

       

      Abstract: The environment of underground coal mine is very special, the confined wireless channel which is affected by the multipath fading is a great challenge to tracking technology. A novel underground tracking algorithm is proposed, which combines the fingerprint matching algorithm with Kalman filter. Firstly, by Kalman-mean filter, the sampled RSSI value is disposed of noise, and then the position coordinates of the observation points are drawn from implementation of fingerprint matching algorithm as the observation values of the tracking, at the same time, the observation values and actual values estimate jointly underground measurement noise covariance, finally, we use improved Kalman filter algorithm to track the trajectory of the target. The experimental results show that the improved algorithm can meet the requirements of tracking accuracy and the error of underground target, enhances the reliability of tracking system.

       

    /

    返回文章
    返回