摘要
RSSI与TOF是当前处理室内定位问题的两种应用较为广泛的技术,但是其适用性受到限制且精度不高。针对室内复杂环境跟踪定位问题,采用基于RSSI和TOF两种节点测距算法进行融合计算以提高测距精度。对两种测距算法分别进行分析,建立系统运动模型,通过卡尔曼滤波对采集数据进行有效的噪音过滤;在统一的实验平台实现3种融合算法,分别为平均法,加权法和神经网络法,对仿真图形进行分析对比。实验结果表明,神经网络法收敛速度快,拟合程度高,优化结果非常理想,有利于室内定位技术精度的提高。
Both RSSI and TOF are widely applied for indoor location technology, however the applicability of them is limited and the accuracy is not high. A data fusion algorithm based on RSSI and TOF is studied to improve ranging accuracy in the indoor complex environment. The location algorithm involves two steps. Firstly, performance of RSSI and TOF is separately analyzed. Kalman filter by modeling is an efficient way to filter noise of location. Secondly, three effective fusion algorithm is accomplished on a unified platform, including average, weighted and neural networks method. The experimental results are compared and analyzed in detail. Simulation result shows that the method based on neural networks is effective and the final positioning result is excellent, can achieve a significant improvement for indoor location application.
出处
《计算机工程与设计》
CSCD
北大核心
2014年第5期1526-1530,共5页
Computer Engineering and Design
基金
国家自然科学基金项目(61174116)
北京市教委科研计划课题基金项目(KM201110009012)
关键词
距离测量
室内定位
融合算法
卡尔曼滤波
神经网络
distance measurement
indoor-location
data fusion
Kalman filter
neural networks
