Research on crowd-sourced localization algorithm for narrow and closed battlefield environment

Abstract

In order to solve the problem of how to quickly and accurately obtain the location information of self-organizing networks or target objects in the narrow and closed battlefield environment, such as the enemy command center, cabins, and underground buildings, this paper proposes a positioning algorithm based on the communication module provided by individual intelligent terminal equipment, we use the support of hardware devices such as visible light sensors, and study the communication signal fingerprint of swarm intelligence perception, combined with image matching algorithm. This algorithm mainly communicates with fingerprint data to achieve preliminary positioning, combines a weighted average algorithm of fused images and attitude sensors, and uses swarm intelligence perception to supplement and update positioning data. By adjusting the image matching strategy, while maintaining accuracy, compared to a single image matching positioning algorithm, it reduces computational power requirements and improves real-time performance in battlefield environments with complex communication conditions. Compared with the standard WKNN (Weighted K-Nearest Neighbors) algorithm, it improves the stability of localization in complex environments, and the average localization error is less than 1.72 m, reducing the error by about 50%.

Keywords

• narrow battlefield environment|crowd-sourced|localization algorithm