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A Hybrid Wi-Fi Fingerprint-Based Localization Scheme Achieved by Combining Fisher Score and Stacked Sparse Autoencoder Algorithms
Mobile Information Systems Pub Date : 2020-04-14 , DOI: 10.1155/2020/5710450
Zhongyuan Wang 1, 2 , Zijian Wang 1, 2 , Li Fan 3 , Zhihao Yu 1, 2
Affiliation  

Along with the advancement of wireless technology, indoor localization technology based on Wi-Fi has received considerable attention from academia and industry. The fingerprint-based method is the mainstream approach for Wi-Fi indoor localization and can be easily implemented without additional hardware. However, signal fluctuations constitute a critical issue pertaining to the extraction of robust features to achieve the required localization performance. This study presents a fingerprint feature extraction method commonly referred to as the Fisher score–stacked sparse autoencoder (Fisher–SSAE) method. Some features with low Fisher scores were eliminated, and the representative features were then extracted by the SSAE. Furthermore, this study establishes a hybrid localization model constructed with the use of the global model and the submodel to avoid significant coordinate localization errors attributed to subregional localization errors. Combined with three accessible fingerprint-based positioning methods, namely, the support vector regression, random forest regression, and the multiplayer perceptron classification, the experimental results demonstrate that the proposed methods improve the localization accuracy and response time compared to other feature extraction methods and the single localization model. Compared with some state-of-the-art methods, the proposed methods have better localization performances when large number of features are used.

中文翻译:

Fisher分数和堆叠式稀疏自动编码器算法相结合实现的基于混合Wi-Fi指纹的本地化方案

随着无线技术的发展,基于Wi-Fi的室内定位技术受到了学术界和业界的广泛关注。基于指纹的方法是Wi-Fi室内定位的主流方法,无需额外的硬件即可轻松实现。然而,信号波动构成与提取鲁棒特征以实现所需定位性能有关的关键问题。这项研究提出了一种指纹特征提取方法,通常称为Fisher分数堆积的稀疏自动编码器(Fisher-SSAE)方法。消除了Fisher分数较低的某些特征,然后由SSAE提取了代表性特征。此外,这项研究建立了一个混合定位模型,该模型使用全局模型和子模型构建,以避免因子区域定位误差而导致的显着坐标定位误差。结合支持向量回归,随机森林回归和多人感知器分类这三种基于指纹的可访问定位方法,实验结果表明,与其他特征提取方法相比,该方法提高了定位精度和响应时间。单一本地化模型。与某些最新方法相比,当使用大量特征时,所提出的方法具有更好的定位性能。结合支持向量回归,随机森林回归和多人感知器分类这三种基于指纹的可访问定位方法,实验结果表明,与其他特征提取方法相比,该方法提高了定位精度和响应时间。单一本地化模型。与某些最新方法相比,当使用大量特征时,所提出的方法具有更好的定位性能。结合支持向量回归,随机森林回归和多人感知器分类这三种基于指纹的可访问定位方法,实验结果表明,与其他特征提取方法相比,该方法提高了定位精度和响应时间。单一本地化模型。与某些最新方法相比,当使用大量特征时,所提出的方法具有更好的定位性能。实验结果表明,与其他特征提取方法和单一定位模型相比,该方法提高了定位精度和响应时间。与某些最新方法相比,当使用大量特征时,所提出的方法具有更好的定位性能。实验结果表明,与其他特征提取方法和单一定位模型相比,该方法提高了定位精度和响应时间。与某些最新方法相比,当使用大量特征时,所提出的方法具有更好的定位性能。
更新日期:2020-04-14
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