Ground tracking for improved landmine detection in a GPR system
- URL: http://arxiv.org/abs/2506.18258v1
- Date: Mon, 23 Jun 2025 03:06:55 GMT
- Title: Ground tracking for improved landmine detection in a GPR system
- Authors: Li Tang, Peter A. Torrione, Cihat Eldeniz, Leslie M. Collins,
- Abstract summary: Ground bounce (GB) that is present in GPR data is a major source of interference.<n>GB tracking algorithms formulated using both a Kalman filter (KF) and a particle filter (PF) framework are proposed.<n>We demonstrate that improved GB tracking contributes to improved performance for the landmine detection problem.
- Score: 4.627435780506373
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- Extending RAIM with a Gaussian Mixture of Opportunistic Information [1.9688858888666714]
Original receiver autonomous integrity monitoring (RAIM) was not designed for securing.
We extend RAIM by incorporating all opportunistic information, i.e., measurements from terrestrial infrastructures and onboard sensors.
The objective is to assess the likelihood of spoofing by analyzing locations derived from extended RAIM solutions.
arXiv Detail & Related papers (2024-02-05T19:03:18Z) - Radar-Lidar Fusion for Object Detection by Designing Effective
Convolution Networks [18.17057711053028]
We propose a dual-branch framework to integrate radar and Lidar data for enhanced object detection.
The results show that it surpasses state-of-the-art methods by $1.89%$ and $2.61%$ in favorable and adverse weather conditions.
arXiv Detail & Related papers (2023-10-30T10:18:40Z) - Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue
with Autonomous Heterogeneous Robotic Systems [56.838297900091426]
Smoke and dust affect the performance of any mobile robotic platform due to their reliance on onboard perception systems.
This paper proposes a novel modular computation filtration pipeline based on intensity and spatial information.
arXiv Detail & Related papers (2023-08-14T16:48:57Z) - Echoes Beyond Points: Unleashing the Power of Raw Radar Data in
Multi-modality Fusion [74.84019379368807]
We propose a novel method named EchoFusion to skip the existing radar signal processing pipeline.
Specifically, we first generate the Bird's Eye View (BEV) queries and then take corresponding spectrum features from radar to fuse with other sensors.
arXiv Detail & Related papers (2023-07-31T09:53:50Z) - Semantic Segmentation of Radar Detections using Convolutions on Point
Clouds [59.45414406974091]
We introduce a deep-learning based method to convolve radar detections into point clouds.
We adapt this algorithm to radar-specific properties through distance-dependent clustering and pre-processing of input point clouds.
Our network outperforms state-of-the-art approaches that are based on PointNet++ on the task of semantic segmentation of radar point clouds.
arXiv Detail & Related papers (2023-05-22T07:09:35Z) - Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol
Particles for Frontier Exploration [55.41644538483948]
This paper introduces a multimodal dataset from the harsh and unstructured underground environment with aerosol particles.
It contains synchronized raw data measurements from all onboard sensors in Robot Operating System (ROS) format.
The focus of this paper is not only to capture both temporal and spatial data diversities but also to present the impact of harsh conditions on captured data.
arXiv Detail & Related papers (2023-04-27T20:21:18Z) - RaLiBEV: Radar and LiDAR BEV Fusion Learning for Anchor Box Free Object
Detection Systems [13.046347364043594]
In autonomous driving, LiDAR and radar are crucial for environmental perception.
Recent state-of-the-art works reveal that the fusion of radar and LiDAR can lead to robust detection in adverse weather.
We propose a bird's-eye view fusion learning-based anchor box-free object detection system.
arXiv Detail & Related papers (2022-11-11T10:24:42Z) - Improved Orientation Estimation and Detection with Hybrid Object
Detection Networks for Automotive Radar [1.53934570513443]
We present novel hybrid architectures that combine grid- and point-based processing to improve radar-based object detection networks.
We show that a point-based model can extract neighborhood features, leveraging the exact relative positions of points, before grid rendering.
This has significant benefits for a following convolutional detection backbone.
arXiv Detail & Related papers (2022-05-03T06:29:03Z) - Rethinking Drone-Based Search and Rescue with Aerial Person Detection [79.76669658740902]
The visual inspection of aerial drone footage is an integral part of land search and rescue (SAR) operations today.
We propose a novel deep learning algorithm to automate this aerial person detection (APD) task.
We present the novel Aerial Inspection RetinaNet (AIR) algorithm as the combination of these contributions.
arXiv Detail & Related papers (2021-11-17T21:48:31Z) - SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic
Point Generation [28.372067223801203]
In autonomous driving, a LiDAR-based object detector should perform reliably at different geographic locations and under various weather conditions.
While recent 3D detection research focuses on improving performance within a single domain, our study reveals that the performance of modern detectors can drop drastically cross-domain.
We present Semantic Point Generation (SPG), a general approach to enhance the reliability of LiDAR detectors against domain shifts.
arXiv Detail & Related papers (2021-08-15T10:00:39Z) - Dense Label Encoding for Boundary Discontinuity Free Rotation Detection [69.75559390700887]
This paper explores a relatively less-studied methodology based on classification.
We propose new techniques to push its frontier in two aspects.
Experiments and visual analysis on large-scale public datasets for aerial images show the effectiveness of our approach.
arXiv Detail & Related papers (2020-11-19T05:42:02Z) - EHSOD: CAM-Guided End-to-end Hybrid-Supervised Object Detection with
Cascade Refinement [53.69674636044927]
We present EHSOD, an end-to-end hybrid-supervised object detection system.
It can be trained in one shot on both fully and weakly-annotated data.
It achieves comparable results on multiple object detection benchmarks with only 30% fully-annotated data.
arXiv Detail & Related papers (2020-02-18T08:04:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.