Dynamic-MUSIC: accurate device-free indoor localization

By Xiang Li, Shengjie Li, Daqing Zhang, Jie Xiong, Yasha Wang, Hong Mei. Ubiquitous Computing, 2016

352 citations recorded by the source corpus.

Device-free passive indoor localization is playing a critical role in many applications such as elderly care, intrusion detection, smart home, etc. However, existing device-free localization systems either suffer from labor-intensive offline training or require dedicated special-purpose devices. To address the challenges, we present our system named MaTrack, which is implemented on commodity off-the-shelf Intel 5300 Wi-Fi cards. MaTrack proposes a novel Dynamic-MUSIC method to detect the subtle reflection signals from human body and further differentiate them from those reflected signals from static objects (furniture, walls, etc.) to identify the human target's angle for localization. MaTrack does not require any offline training compared to existing signature-based systems and is insensitive to changes in environment. With just two receivers, MaTrack is able to achieve a median localization accuracy below 0.6 m when the human is walking, outperforming the state-of-the-art schemes.

Subfields: Electrical and Electronic Engineering, Ocean Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/6dd19b32544413b457b2df4e173f89dc61ec6639

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Kun Qian
2. Zheng Yang
3. Kyle Jamieson
4. Chenshu Wu
5. Yunhao Liu
6. Guidong Zhang
7. Yi Zhang
8. Yue Zheng

In Ocean Engineering

1. Kyle Jamieson
2. Zheng Yang
3. Chenshu Wu
4. Yunhao Liu
5. Kun Qian
6. Guidong Zhang
7. Yi Zhang
8. Shiwen Mao

In Signal Processing

1. Kyle Jamieson
2. Kun Qian
3. Yunhao Liu
4. Zheng Yang
5. Chenshu Wu
6. Shiwen Mao
7. Xuyu Wang
8. Lingjun Gao

Citations in the corpus

• Widar3.0: Zero-Effort Cross-Domain Gesture Recognition With Wi-Fi (2021, methodology): “On the signal side, existing approaches extract various parameters of signals reflected by human or emitted by portable devices, including DFS [11], [17], [20], ToF [21], [22], [23], [24], AoA/AoD [23], [24], [25], [26], ACF [27], and attenuation [28], [29].”
• Widar2.0: Passive Human Tracking with a Single Wi-Fi Link (2018, methodology): “0 in three different indoor scenarios and compare it with two state-of-the-art methods namely IndoTrack [17] and DynamicMusic [16].”
• Widar: Decimeter-Level Passive Tracking via Velocity Monitoring with Commodity Wi-Fi (2017, methodology): “As current precise passive localization methods [8, 16] do not directly apply to Widar , we leave the development of the localization method for future work.”
• Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi (2019, methodology): “On the signal side, existing approaches extract various parameters of signals reflected or shadowed by human, including DFS [26, 32, 44], ToF [2–4, 21], AoA/AoD [2, 5, 21, 25]”