用粒子滤波追踪地表回波，提升雷达探雷准确率
Ground tracking for improved landmine detection in a GPR system
- 通过粒子滤波建模地表回波位置为隐状态，逐帧更新
- 实验显示该方法显著降低干扰，提升探雷性能
- 自适应调整参数，适合不同土壤与天气条件
地下穿透雷达（GPR）在检测低金属含量地雷方面具有潜力。然而，由土壤与空气介电常数突变引起的地表回波（GB）是主要干扰源，严重降低探测效果。本文提出基于卡尔曼滤波（KF）和粒子滤波（PF）框架的GB跟踪算法。将雷达信号中的GB位置建模为随机系统中的隐状态，观测数据为沿航向逐扫描输入的二维雷达图像。初始训练阶段自动设置参数以适应不同地面和天气条件，同时根据新数据自适应更新与GB相关的特征。通过相邻两个通道/扫描的信息传播预测给定位置的先验分布，确保整体GB表面平滑。使用真实数据进行实验验证，结果表明所提方法优于其他GB跟踪技术，有效提升地雷检测性能。
原文摘要 · Abstract (English)
Ground penetrating radar (GPR) provides a promising technology for accurate subsurface object detection. In particular, it has shown promise for detecting landmines with low metal content. However, the ground bounce (GB) that is present in GPR data, which is caused by the dielectric discontinuity between soil and air, is a major source of interference and degrades landmine detection performance. To mitigate this interference, GB tracking algorithms formulated using both a Kalman filter (KF) and a particle filter (PF) framework are proposed. In particular, the location of the GB in the radar signal is modeled as the hidden state in a stochastic system for the PF approach. The observations are the 2D radar images, which arrive scan by scan along the down-track direction. An initial training stage sets parameters automatically to accommodate different ground and weather conditions. The features associated with the GB description are updated adaptively with the arrival of new data. The prior distribution for a given location is predicted by propagating information from two adjacent channels/scans, which ensures that the overall GB surface remains smooth. The proposed algorithms are verified in experiments utilizing real data, and their performances are compared with other GB tracking approaches. We demonstrate that improved GB tracking contributes to improved performance for the landmine detection problem.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。