时序测量信息触发的递归贝叶斯定位算法

Algorithm for recursive Bayesian localization triggered bytemporalseries measurement information

为了提高定位系统抗干扰性,降低定位误差,提出一种基于递归贝叶斯的指纹定位算法。针对离线阶段指纹数据采集的盲目性与不可靠性,利用样本方差对采样值的可信度进行衡量,降低环境因素对数据的影响,为在线匹配提供更有效的可靠指纹库。利用目标运动过程中相邻时刻间的约束关系构建马尔可夫模型,以预测当前时刻目标位置,克服传统定位算法经常出现的位置估计跳动范围大、鲁棒性差等问题,提高了定位精度。经仿真模拟与路演实景的双重可重复性测试,所提算法的平均定位误差绝对值在模拟与实景测试中均不大于0.927m,较已有经典同类定位方法,其定位精度提高30%以上。

To improve the robustness of a position system and reduce the localization error,this paper proposes a fingerprint positioning method based on the recursive Bayesian.To solve the blindness and unreliability of the location fingerprint data in an offline phase,the fingerprint database based on the sample variance is developed to measure the confidence of sampling values and reduce the impact of environmental factors,improving the reliability for online localization.The proposed method provides the target position at the current moment by utilizing the Markov model that is established by the constraint relationship between moments in the source movement,which avoids the jump problem of the position estimation and poor robustness and improves the localization accuracy.Extensive experimental results demonstrate that the average localization error norm of the proposed algorithm is no more than 0.927m,indicating significantly lower errors than other traditional schemes(often by more than 30 percent).