scholarly journals Exploiting Fine-Grained Subcarrier Information for Device-Free Localization in Wireless Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3110 ◽  
Author(s):  
Yan Guo ◽  
Dongping Yu ◽  
Ning Li
Keyword(s):  
Compressive Sensing ◽  
Estimation Algorithm ◽  
Surface Model ◽  
Frequency Diversity ◽  
Fine Grained ◽  
Area Of Interest ◽  
Radio Signals ◽  
Device Free ◽  
Target Locations ◽  
Spatial Sparsity

Device-free localization (DFL) that aims to localize targets without carrying any electronic devices is addressed as an emerging and promising research topic. DFL techniques estimate the locations of transceiver-free targets by analyzing their shadowing effects on the radio signals that travel through the area of interest. Recently, compressive sensing (CS) theory has been applied in DFL to reduce the number of measurements by exploiting the inherent spatial sparsity of target locations. In this paper, we propose a novel CS-based multi-target DFL method to leverage the frequency diversity of fine-grained subcarrier information. Specifically, we build the dictionaries of multiple channels based on the saddle surface model and formulate the multi-target DFL as a joint sparse recovery problem. To estimate the location vector, an iterative location vector estimation algorithm is developed under the multitask Bayesian compressive sensing (MBCS) framework. Compared with the state-of-the-art CS-based multi-target DFL approaches, simulation results validate the superiority of the proposed algorithm.

Device-Free Indoor Localization Based on Ambient FM Radio Signals

2014 ◽  
Vol 6 (1) ◽  
pp. 35-44 ◽  
Author(s):  
Andrei Popleteev ◽  
Thomas Engel
Keyword(s):  
Indoor Localization ◽  
Temporal Stability ◽  
Frequency Diversity ◽  
Fine Grained ◽  
Fm Radio ◽  
Radio Signals ◽  
Localization Performance ◽  
Device Free ◽  
Passive Localization

This paper presents the concept of device-free indoor localization using only a passive receiver and ambient FM radio signals. Experimental results based on empirical measurements demonstrate the feasibility of the proposed approach. The authors also evaluate fine-grained localization performance of the system, its temporal stability, and highlight the role of frequency diversity for passive localization.

scholarly journals Enhancing the Accuracy and Robustness of a Compressive Sensing Based Device-Free Localization by Exploiting Channel Diversity

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1828
Author(s):  
Dongping Yu ◽  
Yan Guo ◽  
Ning Li ◽  
Xiaoqin Yang
Keyword(s):  
Compressive Sensing ◽  
Environmental Changes ◽  
Sparse Recovery ◽  
Superior Performance ◽  
Surface Model ◽  
Recovery Algorithm ◽  
Common Support ◽  
Channel Diversity ◽  
Shadowing Effects ◽  
Device Free

As an emerging and promising technique, device-free localization (DFL) estimates target positions by analyzing their shadowing effects. Most existing compressive sensing (CS)-based DFL methods use the changes of received signal strength (RSS) to approximate the shadowing effects. However, in changing environments, RSS readings are vulnerable to environmental dynamics. The deviation between runtime RSS variations and the data in a fixed dictionary can significantly deteriorate the performance of DFL. In this paper, we introduce ComDec, a novel CS-based DFL method using channel state information (CSI) to enhance localization accuracy and robustness. To exploit the channel diversity of CSI measurements, the DFL problem is formulated as a joint sparse recovery problem that recovers multiple sparse vectors with common support. To solve this problem, we develop a joint sparse recovery algorithm under the variational Bayesian inference framework. In this algorithm, dictionaries are parameterized based on the saddle surface model. To adapt to the environmental changes and different channel characteristics, dictionary parameters are modelled as tunable parameters. Simulation results verified the superior performance of ComDec as compared with other state-of-the-art CS-based DFL methods.

Bearing Estimation via Spatial Sparsity using Compressive Sensing

2012 ◽  
Vol 48 (2) ◽  
pp. 1358-1369 ◽  
Author(s):  
Ali Cafer Gurbuz ◽  
Volkan Cevher ◽  
James H. Mcclellan
Keyword(s):  
Compressive Sensing ◽  
Bearing Estimation ◽  
Spatial Sparsity

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 WiEat: Fine-grained Device-free Eating Monitoring Leveraging Wi-Fi Signals

2020 ◽  
Author(s):  
Zhenzhe Lin ◽  
Yucheng Xie ◽  
Xiaonan Guo ◽  
Yanzhi Ren ◽  
Yingying Chen ◽  
...  
Keyword(s):  
Fine Grained ◽  
Device Free

Progressive compressive sensing for exploiting frequency-diversity in GPR imaging

2018 ◽  
Vol 32 (9) ◽  
pp. 1164-1193 ◽  
Author(s):  
M. Salucci ◽  
A. Gelmini ◽  
L. Poli ◽  
G. Oliveri ◽  
A. Massa
Keyword(s):  
Compressive Sensing ◽  
Frequency Diversity

scholarly journals Bio-Inspired Robotic Solutions for Landslide Monitoring

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1256 ◽  
Author(s):  
Luca Patané
Keyword(s):  
Mechanical Design ◽  
Early Stage ◽  
Smart Sensors ◽  
Landslide Monitoring ◽  
Dynamic Simulations ◽  
Area Of Interest ◽  
Landslide Event ◽  
The Status ◽  
Design Systems ◽  
Target Locations

Bio-inspired solutions are often taken into account to solve problems that nature took millions of years to deal with. In the field of robotics, when we need to design systems able to perform in unstructured environments, bio-inspiration can be a useful instrument both for mechanical design and for the control architecture. In the proposed work the problem of landslide monitoring is addressed proposing a bio-inspired robotic structure developed to deploy a series of smart sensors on target locations with the aim of creating a sensor network capable of acquiring information on the status of the area of interest. The acquired data can be used both to create models and to generate alert signals when a landslide event is identified in the early stage. The design process of the robotic system, including dynamic simulations and robot experiments, will be presented here.

scholarly journals A Compressive Sensing-Based Dynamic Estimation Algorithm in Unified Laser TTC System

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 29963-29972
Author(s):  
Jianping An ◽  
Lichen Zhu ◽  
Xiangyuan Bu ◽  
Kai Yang
Keyword(s):  
Compressive Sensing ◽  
Estimation Algorithm ◽  
Dynamic Estimation
Sign in / Sign up

Export Citation Format

Share Document