Target localization is essential for emergency dispatching situations. Maximum likelihood estimation (MLE) methods are widely used to estimate the target position based on the received signal strength measurements. However, the performance of MLE solvers is significantly affected by the initialization (i.e., initial guess of the solution or solution search space). To address this, a previous study proposed the semi-definite programming (SDP)-based MLE initialization. However, the performance of the SDP-based initialization technique is largely affected by the shadowing variance and geometric diversity between the target and receivers. In this study, a radio frequency (RF) fingerprinting-based MLE initialization is proposed. Further, a maximum likelihood problem for target localization combining RF fingerprinting is formulated. In the three test environments of open space, urban, and indoor, the proposed RF fingerprinting-aided target localization method showed a performance improvement of up to 63.31% and an average of 39.13%, compared to the MLE algorithm initialized with SDP. Furthermore, unlike the SDP-MLE method, the proposed method was not significantly affected by the poor geometry between the target and receivers in our experiments.
|Title of host publication||2022 IEEE 95th Vehicular Technology Conference - Spring, VTC 2022-Spring - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 2022|
|Event||95th IEEE Vehicular Technology Conference - Spring, VTC 2022-Spring - Helsinki, Finland|
Duration: 2022 Jun 19 → 2022 Jun 22
|Name||IEEE Vehicular Technology Conference|
|Conference||95th IEEE Vehicular Technology Conference - Spring, VTC 2022-Spring|
|Period||22/6/19 → 22/6/22|
Bibliographical noteFunding Information:
The authors would like to thank the Communication System Laboratory of Hanyang University, Korea, for the outdoor data collection. This research was supported in part by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (KNPA) (2019-0-01291) and in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2021R1A6A3A13046688).
© 2022 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Electrical and Electronic Engineering
- Applied Mathematics