Adaptive Autocorrelation Approach for Fingerprint-based Distance Dependent Positioning Algorithms in WLAN Indoor Areas
Adaptive Autocorrelation Approach for Fingerprint-based Distance Dependent Positioning Algorithms in WLAN Indoor Areas
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DOI：
10.4304/jnw.6.10.1475-1482
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发表时间：
2011-01
期刊：
影响因子：
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通讯作者：
Mu Zhou;Yubin Xu;Lin Ma
中科院分区：
文献类型：
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作者：
Mu Zhou;Yubin Xu;Lin Ma
提出了无线局域网（WLAN）室内环境下基于指纹的距离相关定位算法（DDPA）的自适应自相关方法。据我们所知，虽然最近邻（NN）、K最近邻（KNN）和加权KNN（WKNN）算法等DDPA算法已被广泛应用于室内和室外的基于位置的服务（LBS）中，但如何保证定位的准确性和精确度一直是人们关注的重要问题之一。因此，在响应于这一具有挑战性的任务，预期的错误和相关的置信概率的数学推导的对数衰减模型和高斯分布的接收机处接收的无线电信号强度（RSS）的假设。然而，由于非视距（NLOS）特性、时变干扰和多径效应，在实际的室内环境中，测量的无线电强度变化很大。因此，为了填补这一空白，一种新的自适应自相关预处理方法被用来消除奇异强度从原始预存储的无线电地图，提高DDPA的匹配精度。最后，通过与传统的不带自适应自相关预处理的DDPA算法的比较，验证了基于自适应自相关的DDPA算法的可行性和有效性，平均误差从1.13 m减小到0.75 m。
This paper addresses the adaptive autocorrelation approach for the fingerprinting-based distance dependent positioning algorithms (DDPAs) in wireless local area network (WLAN) indoor environment. As far as we know, although the DDPAs, like nearest neighbor (NN), K nearest neighbors (KNN) and weighted KNN (WKNN) algorithms, have been widely utilized for the indoor and outdoor location based services (LBS), the guarantee of location accuracy and precision has always been one of the significant compelling problems. Therefore, in response to this challenging task, the expected errors and associated confidence probabilities are mathematically deduced by the assumptions of logarithmic attenuation model and Gaussian distributions of received radio signal strength (RSS) at the receiver. However, because of the non line of sight (NLOS) property, time-varying interference and multipath effect, the measured radio strength varies a lot in the real-world indoor environment. Therefore, in order to fill this gap, a novel adaptive autocorrelation preprocessing approach is utilized to eliminate the singular strength from the original prestored radio map and improve the matching accuracy of DDPAs. Finally, compared with traditional DDPAs without adaptive autocorrelation preprocessing, the feasibility and effectiveness of the adaptive autocorrelation-based DDPAs are verified by approximately decreasing the average errors from 1.13m to 0.75m.