IMM-EKF Algorithm-Based Indoor Moving Target Localization
-
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.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.
-
-
期刊类型引用(5)
1. 王明,张倩. 我国基于深度学习的图像识别技术在农作物病虫害识别中的研究进展. 中国蔬菜. 2023(03): 22-28 . 百度学术
2. 覃伟荣,劳燕玲. 基于3D关联规则深度学习的异构遥感数据检测. 计算机仿真. 2023(09): 482-486 . 百度学术
3. 吕晓洁. 基于深度学习的分布式光伏发电系统电压稳定性评估. 电子设计工程. 2022(17): 114-118 . 百度学术
4. 宋美佳,贾鹤鸣,林志兴,卢仁盛,刘庆鑫. 自适应学习率梯度下降的优化算法. 三明学院学报. 2021(06): 36-44 . 百度学术
5. 郑俊浩. 基于深度学习的乳腺癌MRI影像预处理. 智能计算机与应用. 2020(01): 231-232+236 . 百度学术
其他类型引用(6)
计量
- 文章访问数:
- HTML全文浏览量: 0
- PDF下载量:
- 被引次数: 11