Indoor Location System Based on Discriminant-Adaptive Neural Network in IEEE 802.11 Environments
Indoor Location System Based on Discriminant-Adaptive Neural Network in IEEE 802.11 Environments
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DOI：
10.1109/tnn.2008.2005494
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发表时间：
2008-11-01
影响因子：
--
通讯作者：
Lin, Tsung-Nan
中科院分区：
文献类型：
--
作者：
Fang, Shih-Hau;Lin, Tsung-Nan
本文提出了一种新的定位算法--判别-自适应神经网络(DANN)，该算法以接入点(AP)的接收信号强度(RSS)作为输入，在无线局域网(LAN)环境中推断客户的位置。我们将有用的信息提取到判别成分(DC)中，用于网络学习。然后，通过递增地插入DC并递归地更新网络中的权重，直到不需要进一步改进为止，精确地构建RSS和位置之间的非线性关系。我们的定位系统是在真实的无线局域网环境中开发的，收集了真实的RSS测量数据。在相同的测试平台上实现了加权k近邻(WKNN)、最大似然(ML)和多层感知器(MLP)等传统方法，并对结果进行了比较。实验结果表明，与其他检测技术相比，该算法具有更高的准确率。这一改进归功于只有效地提取有用信息用于定位，而将冗余信息视为噪声而丢弃。最后，分析表明，我们的网络智能地完成了学习，而插入的DC提供了足够的信息。
This brief paper presents a novel localization algorithm, named discriminant-adaptive neural network (DANN), which takes the received signal strength (RSS) from the access points (APs) as inputs to infer the client position in the wireless local area network (LAN) environment. We extract the useful information into discriminative components (DCs) for network learning. The nonlinear relationship between RSS and the position is then accurately constructed by incrementally inserting the DCs and recursively updating the weightings in the network until no further improvement is required. Our localization system is developed in a real-world wireless LAN WLAN environment, where the realistic RSS measurement is collected. We implement the traditional approaches on the same test bed, including weighted k-nearest neighbor (WKNN), maximum likelihood (ML), and multilayer perceptron (MLP), and compare the results. The experimental results indicate that the proposed algorithm is much higher in accuracy compared with other examined techniques. The improvement can be attributed to that only the useful information is efficiently extracted for positioning while the redundant information is regarded as noise and discarded. Finally, the analysis shows that our network intelligently accomplishes learning while the inserted DCs provide sufficient information.