基于Unscented卡尔曼滤波的室内定位

Unscented Kalman filter-based indoor positioning

基于RSSI值的测距技术中,通过对天线全向性问题的分析,提出基于Unscented卡尔曼滤波(UKF)的定位算法。利用基于RSSI值的测距模型进行距离测量,并使用Unscented卡尔曼滤波算法估计节点坐标。由于RSSI值的测量和测距模型参数受到环境的影响,采用高斯滤波对RSSI值进行优化,对环境参数使用线性回归算法进行优化并采用自适应机制更新。通过与最大似然估计法(ML)的比较实验表明,该算法能有效地减小定位误差,提高定位精度。

By analyzing antenna omni-directional problem in RSSI values-based ranging technique, it proposes location algorithm based on the Unscented Kalman Filter（UKF）. It uses the RSSI values-based ranging model for distance mea-surement, and uses the Unscented Kalman filter algorithm to estimate node coordinates. Since the RSSI value measurement and ranging model parameters affected by the environment, it uses Gaussian filter to optimize the RSSI value and uses linear regression algorithm to optimize the environmental parameters. Compared with the Maximum Likelihood estimation method （ML）, experimental results show that this method can effectively reduce the positioning error and improve accuracy.