RSSI-Based Indoor Localization and Tracking Using Sigma-Point Kalman Smoothers

By Anindya Sao Paul, Eric A. Wan. IEEE Journal on Selected Topics in Signal Processing, 2009

298 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/767217f113054f62d47d2b1ff2eb130b338f9312

How researchers see this paper

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

In Artificial Intelligence

1. Petros Spachos
2. Sebastian Sadowski

In Computer Networks and Communications

1. Petros Spachos
2. Hongbo Liu
3. Jian Liu
4. Yan Wang
5. Sebastian Sadowski
6. Yingying Chen
7. Alex X. Liu

In Electrical and Electronic Engineering

1. Petros Spachos
2. Chen Wang
3. Hongbo Liu
4. Jian Liu
5. Yan Wang
6. Sebastian Sadowski
7. Yingying Chen
8. Alex X. Liu

Citations in the corpus

• Wireless Sensing for Human Activity: A Survey (2020, methodology): “To improve the accuracy, Güvenc [73] and Paul and Wan [74] propose to use Kalman filter algorithm to the propagation model of RSSI-based localization system.”
• RSSI-Based Indoor Localization With the Internet of Things (2018, methodology): “RSSI is one of the most popular and simplest methods for localization [21]–[23].”