Localization in a battlefield environment is increasingly challenging as GPS
connectivity is often denied or unreliable, and physical deployment of anchor
nodes across wireless networks for localization can be difficult in hostile
battlefield terrain. Existing range-free localization methods rely on
radio-based anchors and their average hop distance which suffers from accuracy
and stability in dynamic and sparse wireless network topology. Vision-based
methods like SLAM and Visual Odometry use expensive sensor fusion techniques
for map generation and pose estimation. This paper proposes a novel framework
for localization in non-GPS battlefield environments using only the passive
camera sensors and considering naturally existing or artificial landmarks as
anchors. The proposed method utilizes a customcalibrated stereo vision camera
for distance estimation and the YOLOv8s model, which is trained and fine-tuned
with our real-world dataset for landmark recognition. The depth images are
generated using an efficient stereomatching algorithm, and distances to
landmarks are determined by extracting the landmark depth feature utilizing a
bounding box predicted by the landmark recognition model. The position of the
unknown node is then obtained using the efficient least square algorithm and
then optimized using the L-BFGS-B (limited-memory quasi-Newton code for
bound-constrained optimization) method. Experimental results demonstrate that
our proposed framework performs better than existing anchorbased DV-Hop
algorithms and competes with the most efficient vision-based algorithms in
terms of localization error (RMSE).

提出一种新的框架，利用仅使用被动相机传感器和作为锚点的自然存在或人工地标，实现在非 GPS 战场环境中的定位。该方法使用经过校准的双目相机进行距离估计，并使用基于我们的真实世界数据集训练和微调的 YOLOv8s 模型进行地标识别。通过提取地标深度特征，利用地标识别模型预测的边界框确定地标距离。然后，利用最小二乘法算法获得未知节点的位置，并使用 L-BFGS-B 方法进行优化。实验结果表明，我们提出的框架在定位误差 (RMSE) 方面优于现有的基于锚点的 DV-Hop 算法，并与效率最高的基于视觉的算法竞争。