一种基于CKF的改进LANDMARC室内定位算法

针对传统LANDMARC室内定位算法受室内环境的干扰存在定位精度不高,波动大的问题,提出一种基于CKF的改进LANDMARC室内定位算法。该算法首先通过传统LANDMARC算法得到待定位目标的状态预估值;然后将得到的状态预估值作为观测量并用容积卡尔曼滤波(CKF)算法对其进行滤波处理,以提高算法的定位精度并降低定位结果的波动;最后用滤波处理后的结果代替LANDMARC得到的预估值作为待定位目标的状态估计。实验研究表明,所提算法误差在0.5 m以下的标签达到60%,与传统LANDMARC定位算法和经由无迹卡尔曼滤波(UKF)算法滤波的LANDMARC定位算法相比,定位精度和波动性均有明显提高,应用在室内定位中能够得到较为真实的目标移动轨迹。

An improved LANDMARC indoor localization algorithm based on CKF

Aiming at the problems of low location accuracy and poor adaptability in the traditional LANDMARC localization algorithm by the interference of the indoor environment, an improved LANDMARC indoor location algorithm based on CKF is proposed in this paper.Firstly, the algorithm obtains the state prediction value of the target to be targeted by the traditional LANDMARC algorithm.Then, with the purpose of improving the localization accuracy of the algorithm and reducing the fluctuation of the positioning result, the obtained state prediction value is taken as the observation and filtered by the Cubature Kalman Filter (CKF) algorithm.Finally, the results obtained by filtering are used instead of the estimated values obtained by LANDMARC as the state estimates of the targets to be targeted.The experimental results show that the proposed algorithm improves the localization accuracy and volatility compared with the traditional LANDMARC localization algorithm and the LANDMARC localization algorithm based on the Unscented Kalman Filter (UKF) algorithm, with the localization error of 60% in the tested label is less than 0.5m, it can get a realistic goal of moving trajectories used in indoor localization.