Name
Title | ||
|---|---|---|
An Accurate And Efficient Device-Free Localization Approach Based On Gaussian Bernoulli Restricted Boltzmann Machine |
Abstract | ||
|---|---|---|
As an emerging technology, device-free localization (DFL), using radio frequency (RF) sensor networks to detect targets who do not carry any attached devices, has spawned extensive applications. Many existing works formulate DFL as a classification problem, and a key problem is how to extract discriminative features to characterize the raw wireless signal. In this paper, we present an autoencoder-based deep neural network for feature extraction, moreover, multiple Gaussian Bernoulli restricted Boltzmann machines (GBRBMs) are utilized for pre-training and dimension reduction. Experiment results show that this method of GBRBM-based autoencoder (GBRBM-AE) can achieve a high accuracy and efficient performance, which outperforms the conventional autoencoder. When the dimensions of input data are reduced from 784 to 20 dims, our algorithm can maintain a high accuracy of 97.1% and is robust to noise with SNR = 5dB. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/SMC.2018.00399 | 2018 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) |
Keywords | Field | DocType |
Device-free Localization, Gaussian Bernoulli Restricted Boltzmann Machine, Autoencoder, Dimensionality Reduction | Restricted Boltzmann machine,Boltzmann machine,Dimensionality reduction,Autoencoder,Computer science,Algorithm,Feature extraction,Gaussian,Artificial intelligence,Artificial neural network,Wireless sensor network,Machine learning | Conference |
ISSN | Citations | PageRank |
1062-922X | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lingjun Zhao | 1 | 176 | 22.10 |
| Huakun Huang | 2 | 8 | 1.80 |
| Shuxue Ding | 3 | 235 | 33.84 |
| Xiang Li | 4 | 116 | 13.27 |