室内环境下基于IMM-EKF算法的移动目标定位

张云洲; 付文艳; 项姝; 魏东飞; 杨兵

doi:10.7544/issn1000-1239.2014.20131073

室内环境下基于IMM-EKF算法的移动目标定位

(东北大学信息科学与工程学院沈阳 110819) (zhangyunzhou@ise.neu.edu.cn)

基金项目: 国家自然科学基金项目(61273078)；中国博士后科学基金面上项目(2012M511164)；中央高校基本科研业务费专项资金项目(N130404023,N110804004)；辽宁省博士启动基金项目(20121004)

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中图分类号: TP393
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- 发布日期: 2014-10-31

IMM-EKF Algorithm-Based Indoor Moving Target Localization

(College of Information Science and Engineering, Northeastern University, Shenyang 110819)

摘要

摘要: 如何在视距(line-of-sight, LOS)与非视距(non-line-of-sight, NLOS)混合的室内环境中提高移动目标定位的精度，这是一项富有挑战性的工作.移动目标在室内环境移动时，其与传感器网络节点之间的信号传播在LOS与NLOS之间随机切换，导致移动节点定位精度下降.提出一种交互式多模型-扩展卡尔曼滤波(interactive multiple model-extended Kalman filter, IMM-EKF)定位算法.根据LOS/NLOS环境下不同的测距误差特性，在IMM框架中采用2个平行的卡尔曼滤波器(Kalman filter, KF)模型对测量距离同时进行滤波，根据滤波结果和测量值计算2个模型的似然概率，模型间的转换通过Markov链实现，2个KF滤波结果加权融合后获得IMM距离估计值.在EKF定位阶段，通过位置预测和更新估计出移动目标位置.仿真结果表明，IMM-EKF算法能够有效抑制NLOS对目标定位的影响，其定位精度优于单模型算法.
- 无线传感器网络 /
- 移动目标定位 /
- 室内环境 /
- 非视距 /
- 交互式多模型
Abstract: It is a challenging task to improve the accuracy of the mobile localization in LOS (line-of-sight) and NLOS (non-line-of-sight) mixed environment. When the MN (moving node) moves in indoor environment, due to the obstacles such as walls, doors, and furniture, the communicating signal between MN and ANs(anchor nodes) change between LOS and NLOS frequently and randomly, which has negative effect on the accuracy of MN location estimation. To guarantee the accuracy, a KF (Kalman filter) based IMM (interacting multiple model) is proposed to filter the measured distance under the LOS/NLOS mixed environment. Due to the different characteristic of ranging errors between LOS and NLOS, two parallel KFs with different parameters are employed in order to suit for LOS mode and NLOS mode, both of the mode probabilities are calculated by the mode likelihoods and history probabilities. The modes transition between LOS/NLOS modes is based on Markov chain and mode probabilities. The weighted mean of the two modes filtering results is taken as the estimated distance of IMM. Once the estimated distances are obtained, the EKF (extended Kalman filter) is applied to locate the MN. The simulation results demonstrate the IMM can significantly mitigate the positive range error and achieve high localization accuracy.
- wireless sensor network (WSN) /
- mobile localization /
- indoor environment /
- non-line-of-sight (NLOS) /
- interacting multiple model (IMM)

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