摘要
现如今智慧社区正在快速发展,各种公共设施及建筑的建设使得社区内环境复杂,影响着居民中弱势群体的安全.故实时的居民定位便格外重要.智慧定位作为精准化智慧服务之一,主要通过RFID技术实现社区内老幼等人群的轨迹跟踪,以进行安全保障.除此之外还可分析居民聚集场所趋势,为社区建设公共设施提出建议.本文针对RFID阅读器传播数据时被各种噪声影响导致的定位精度差、定位结果偏差等传统问题,引用了Kalman滤波消除信号传递过程中的过程噪声和观测噪声,在该算法中插入改进的密度聚类算法以消除环境噪声影响,设计了基于改进密度聚类算法的Kalman滤波轨迹定位方法 (Kalman filter for improved density peak clustering, K-IDPC).经实验验证,相对于Kalman滤波, K-IDPC的定位精度在0.565 m左右,准确度大幅提高.
Nowadays, smart communities are developing rapidly. The construction of various public facilities and buildings makes the community environment complex, which affects the safety of vulnerable groups of residents. Thus,the localization of residents in real time is particularly important. As one of the precise intelligent services, intelligent localization mainly realizes the trajectory tracking of the elderly and young people in a community through radio frequency identification(RFID) technology to ensure security. In addition, it can also analyze the trend of residents gathering so that it is capable of putting forward suggestions for the construction of public facilities in communities. In this study, to tackle the traditional problems such as poor localization accuracy and localization result deviation caused by various noises when an RFID reader transmits data, we use a Kalman filter to eliminate the process noise and observation noise during signal transmission and insert the improved density clustering algorithm into the traditional algorithm to eliminate the influence of environmental noise. Further, we design a Kalman filter method based on improved density peak clustering(K-IDPC) for localization. Experimental verification shows that the localization accuracy of K-IDPC is about 0.565 m, which is greatly improved compared with that of the Kalman filter.
作者
苑明海
周凯文
张晨希
裴凤雀
YUAN Ming-Hai;ZHOU Kai-Wen;ZHANG Chen-Xi;PEI Feng-Que(College of Mechanical and Electrical Engineering,Hohai University,Changzhou 213022,China)
出处
《计算机系统应用》
2022年第6期265-270,共6页
Computer Systems & Applications
基金
常州市科技支撑计划(社会发展)项目(CE20205045)
江苏省自然科学基金综合项目(BK20201162)。
关键词
智慧社区
RFID
KALMAN滤波
密度峰值聚类
定位算法
smart community
radio frequency identification(RFID)
Kalman filter
density peak clustering(DPC)
localization algorithm
