Representation Model for Electromagnetic Maps Reconstruction via Sparse Nodes
Gongxu Liu, Xu-An Liu, Lu Hong Huang, Long Li, Xinbo Gao · IEEE Transactions on Instrumentation and Measurement · 2025
Electromagnetic (EM) maps hold promise for providing strategic support for future localization and navigation. However, reconstruction of EM maps under sparse nodes conditions is a critical challenge, which constrains the reliable and rapid construction of EM maps. To address this issue, we absorb the concept of Moran’s index and use location index, fingerprint index, and scaling index to quantify the topology, quality, and quantity of EM nodes, respectively. Besides, three node selection criteria were used: one based on random selection, another using the K-means clustering, and the third involving affinity propagation (AP) clustering. Under each criterion, the overall EM maps were reconstructed using four representative interpolation methods: inverse distance weighting (IDW), modified Shepard’s method (MSM), Kriging algorithm (KGA), and biharmonic spline interpolation (v4). Extensive experiments were conducted to verify the representation model. Experimental results indicate that the node selection criterion based on AP clustering outperforms the K-means criterion, which itself is superior to random selection criterion. This is attributed to the fact that under the same conditions, the AP clustering node selection criterion can better take the quality and the topological relationship of EM nodes into account. Another significant finding is that with good node selection criterion and reconstruction algorithms, only approximately 0.3%–0.5% of the nodes are required to achieve the reconstruction performance that would otherwise require up to 20% or more of the nodes. The above conclusions are of good guidance for the reliable and rapid reconstruction of EM maps via sparse or limited nodes.