Year: 2025
Type: article
Abstract: To address the problem of overlooking target movement characteristics and historical activity patterns in conventional path estimation methods, we propose a method based on the principle of multi-source spatio-temporal data fusion. It integrates optical image data with navigation and positioning data and improves the A* algorithm. While seeking the shortest path, the algorithm prioritizes points within hotspot areas to achieve accurate target path estimation. The algorithm extracts hotspot areas using spatial analysis methods such as kernel density analysis and uses them as the basis for path estimation. Through many simulation experiments, it is verified that the proposed improved the A* algorithm is more consistent with the actual path than the traditional A* algorithm.
Source: Electronics
Authors: Qinying Hu, Gege Sun, Hang Chen
Institution: Space Engineering University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Awards: G5459997253, G7626489592
OpenAlex ID: W4407701250