Research Article
A Refined Direct Position Determination Method for Information Fusion in Sensor Networks
@INPROCEEDINGS{10.4108/eai.18-12-2025.2365303, author={Yuan Zhang and Guizhou Wu and Fucheng Guo and Shuqiang Zhang}, title={A Refined Direct Position Determination Method for Information Fusion in Sensor Networks}, proceedings={Proceedings of the 13th International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2025, 18-21 December 2025, Chengdu, China}, publisher={EAI}, proceedings_a={IIKI}, year={2026}, month={6}, keywords={direct position determination sensor networks localization cost function eigenvalue decomposition}, doi={10.4108/eai.18-12-2025.2365303} }- Yuan Zhang
Guizhou Wu
Fucheng Guo
Shuqiang Zhang
Year: 2026
A Refined Direct Position Determination Method for Information Fusion in Sensor Networks
IIKI
EAI
DOI: 10.4108/eai.18-12-2025.2365303
Abstract
Passive localization using sensor networks often employs direct position determination (DPD), which performs well in low-SNR conditions. To improve the localization spectrum for subsequent multi-source data fusion, this paper proposes a refined DPD method based on maximum eigenvalue trace (MET-DPD). Unlike conventional DPD, which uses only the maximum eigenvalue, MET-DPD exploits eigendecomposition information more thoroughly by constructing a cost function from the ratio of the maximum eigenvalue to the sum of the remaining eigenvalues of the signal covariance matrix. Simulations show that MET-DPD yields a sharper and more accurate spectrum than existing methods, thereby providing higher-quality preprocessed image data for fusion with optical, infrared, and other sensing modalities.
