Poster abstract: EIL — An environment-independent Device-free Passive Localization approach

2014 ◽  
Author(s):  
Liqiong Chang ◽  
Dingyi Fang ◽  
Zhe Yang ◽  
Xiaojiang Chen ◽  
Ju Wang ◽  
...  
Keyword(s):  
Poster Abstract ◽  
Device Free ◽  
Localization Approach ◽  
Passive Localization

DAFI

2021 ◽  
Vol 5 (4) ◽  
pp. 1-21
Author(s):  
Hang Li ◽  
Xi Chen ◽  
Ju Wang ◽  
Di Wu ◽  
Xue Liu
Keyword(s):  
Indoor Localization ◽  
Domain Adaptation ◽  
Target Domain ◽  
Localization Accuracy ◽  
Source Domain ◽  
Fine Grained ◽  
Time Signals ◽  
Environmental Variations ◽  
Device Free ◽  
Localization Approach

WiFi-based Device-free Passive (DfP) indoor localization systems liberate their users from carrying dedicated sensors or smartphones, and thus provide a non-intrusive and pleasant experience. Although existing fingerprint-based systems achieve sub-meter-level localization accuracy by training location classifiers/regressors on WiFi signal fingerprints, they are usually vulnerable to small variations in an environment. A daily change, e.g., displacement of a chair, may cause a big inconsistency between the recorded fingerprints and the real-time signals, leading to significant localization errors. In this paper, we introduce a Domain Adaptation WiFi (DAFI) localization approach to address the problem. DAFI formulates this fingerprint inconsistency issue as a domain adaptation problem, where the original environment is the source domain and the changed environment is the target domain. Directly applying existing domain adaptation methods to our specific problem is challenging, since it is generally hard to distinguish the variations in the different WiFi domains (i.e., signal changes caused by different environmental variations). DAFI embraces the following techniques to tackle this challenge. 1) DAFI aligns both marginal and conditional distributions of features in different domains. 2) Inside the target domain, DAFI squeezes the marginal distribution of every class to be more concentrated at its center. 3) Between two domains, DAFI conducts fine-grained alignment by forcing every target-domain class to better align with its source-domain counterpart. By doing these, DAFI outperforms the state of the art by up to 14.2% in real-world experiments.

scholarly journals An Accurate and Efficient Device-Free Localization Approach Based on Sparse Coding in Subspace

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 61782-61799 ◽  
Author(s):  
Huakun Huang ◽  
Haoli Zhao ◽  
Xiang Li ◽  
Shuxue Ding ◽  
Lingjun Zhao ◽  
...  
Keyword(s):  
Sparse Coding ◽  
Device Free ◽  
Localization Approach

LTE Signal Fingerprinting Device-Free Passive Localization Robust to Environment Changes

2018 ◽  
Author(s):  
Giovanni Pecoraro ◽  
Ernestina Cianca ◽  
Simone Di Domenico ◽  
Mauro De Sanctis
Keyword(s):  
Environment Changes ◽  
Device Free ◽  
Passive Localization

scholarly journals Nuzzer: A Large-Scale Device-Free Passive Localization System for Wireless Environments

2013 ◽  
Vol 12 (7) ◽  
pp. 1321-1334 ◽  
Author(s):  
Moustafa Seifeldin ◽  
Ahmed Saeed ◽  
Ahmed E. Kosba ◽  
Amr El-Keyi ◽  
Moustafa Youssef
Keyword(s):  
Large Scale ◽  
Localization System ◽  
Wireless Environments ◽  
Device Free ◽  
Passive Localization
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