An RFID Indoor Positioning Algorithm Based on Bayesian Probability and K-Nearest Neighbor.
An RFID Indoor Positioning Algorithm Based on Bayesian Probability and K-Nearest Neighbor.
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基于贝叶斯概率和K近邻的RFID室内定位算法
DOI：
10.3390/s17081806
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发表时间：
2017-08-05
期刊：
影响因子：
--
通讯作者：
Li Y
中科院分区：
文献类型：
--
作者：
Xu H;Ding Y;Li P;Wang R;Li Y
全球定位系统（GPS）广泛应用于室外环境定位。然而，GPS无法支持室内定位，因为在室内环境中没有定位信号。现在有很多情况需要室内定位，比如在图书馆找书，在机场找行李，火警紧急导航，机器人定位等。许多技术，如超声波、传感器、蓝牙、WiFi、磁场、射频识别（RFID）等，被用来进行室内定位。与其他技术相比，RFID用于室内定位更具成本效益和节能性。传统的RFID室内定位算法LANDMARC利用接收信号强度（RSS）指示器来跟踪物体。然而，RSS值容易受到环境噪声和其他干扰的影响。在本文中，我们的目的是减少LANDMARC中由多径和环境干扰引起的位置波动和误差。提出了一种基于贝叶斯概率和k近邻（BKNN）的室内定位算法。实验结果表明，高斯滤波器可以过滤掉一些异常的RSS值。与基于高斯的定位算法、LANDMARC算法和改进的KNN算法相比，所提出的BKNN算法具有最小的定位误差。用我们的方法估计位置的平均误差约为15 cm。
The Global Positioning System (GPS) is widely used in outdoor environmental positioning. However, GPS cannot support indoor positioning because there is no signal for positioning in an indoor environment. Nowadays, there are many situations which require indoor positioning, such as searching for a book in a library, looking for luggage in an airport, emergence navigation for fire alarms, robot location, etc. Many technologies, such as ultrasonic, sensors, Bluetooth, WiFi, magnetic field, Radio Frequency Identification (RFID), etc., are used to perform indoor positioning. Compared with other technologies, RFID used in indoor positioning is more cost and energy efficient. The Traditional RFID indoor positioning algorithm LANDMARC utilizes a Received Signal Strength (RSS) indicator to track objects. However, the RSS value is easily affected by environmental noise and other interference. In this paper, our purpose is to reduce the location fluctuation and error caused by multipath and environmental interference in LANDMARC. We propose a novel indoor positioning algorithm based on Bayesian probability and K-Nearest Neighbor (BKNN). The experimental results show that the Gaussian filter can filter some abnormal RSS values. The proposed BKNN algorithm has the smallest location error compared with the Gaussian-based algorithm, LANDMARC and an improved KNN algorithm. The average error in location estimation is about 15 cm using our method.
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DOI：
10.3390/s130505757
发表时间：
2013-05-03
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Fan H;Wu Q;Lin Y;Zhang J
通讯作者：
Zhang J
使用智能手机进行室内行人导航的与携带位置无关的用户航向估计
DOI：
10.3390/s16050677
发表时间：
2016-05-11
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Deng ZA;Wang G;Hu Y;Cui Y
通讯作者：
Cui Y
影响因子：
1.1
作者：
Arnitz, D.;Muehlmann, U.;Witrisal, K.
通讯作者：
Witrisal, K.
影响因子：
5.7
作者：
Guidi, Francesco;Sibille, Alain;Dardari, Davide
通讯作者：
Dardari, Davide
影响因子：
--
作者：
Kim, Soo-Cheol;Jeong, Young-Sik;Park, Sang-Oh
通讯作者：
Park, Sang-Oh