Detection of range-spread targets based on order statistics

Chen, Xinliang; Hou, Kaiyue; Chang, Shaoqiang; Liu, Quanhua; Ren, Wei

doi:10.1016/j.dsp.2022.103803

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…The point cloud output from the LiDAR provides feedback on the distance of the object and reflectivity information. Using prior calibration information and the characteristics of the track presented in the point cloud, it is also possible to determine the tracked area and identify intruders within the tracked area [19,24,25]. However, methods based on LiDAR input are more dependent on the quality of the point cloud.…”

Section: Lidar Based Methodsmentioning

confidence: 99%

Robust Artificial Intelligence-Aided Multimodal Rail-Obstacle Detection Method by Rail Track Topology Reconstruction

Cao,

Li,

2024

Applied Sciences

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Detecting obstacles in the rail track area is crucial for ensuring the safe operation of trains. However, this task presents numerous challenges, including the diverse nature of intrusions, and the complexity of the driving environment. This paper presents a multimodal fusion rail-obstacle detection approach by key points processing and rail track topology reconstruction. The core idea is to leverage the rich semantic information provided by images to design algorithms for reconstructing the topological structure of railway tracks. Additionally, it combines the effective geometric information provided by LiDAR to accurately locate the railway tracks in space and to filter out intrusions within the track area. Experimental results demonstrate that our method outperforms other approaches with a longer effective working distance and superior accuracy. Furthermore, our post-processing method exhibits robustness even under extreme weather conditions.

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Section: Lidar Based Methodsmentioning

confidence: 99%

Robust Artificial Intelligence-Aided Multimodal Rail-Obstacle Detection Method by Rail Track Topology Reconstruction

Cao,

Li,

2024

Applied Sciences

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“…Softmax(•) represents the softmax function. f dot (•, •) represents dot product similarity, as shown in (5). It should be noted that the sizes of W Q and W K are related to the size of input HRRPs.…”

Section: Adaptive Range Cells Weights Calculation Modulementioning

confidence: 99%

“…On the other hand, polarization can provide different scattering characteristics information between clutter and targets [3,4]. In polarimetric high-resolution radar, the energy of a target splits in the resulting polarimetric high-resolution range profile (HRRP), leading to the range extended target (RET, also known as range spread target or range distributed target) detection problem [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Polarimetric Range Extended Target Detection via Adaptive Range Weighted Feature Extraction

et al. 2023

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In ground static target detection, polarimetric high-resolution radar can distinguish the target from the strong ground clutter by reducing the clutter power in the range cell and providing additional polarimetric features. Since the energy of a target is split over several range cells, the resulting detection problem is called polarimetric range extended target (RET) detection, where all target scattering centers should be considered. In this paper, we propose a novel polarimetric RET detection method via adaptive range weighted feature extraction. Specifically, polarimetric features of range cells are extracted, and a pretrained attention-mechanism-based module is used to adaptively calculate range cells weights, which are used to accumulate the range cells features as detection statistics. While calculating weights, both amplitude and polarimetric features are considered. This method can make the most of polarization information and improve the accumulation effect, thus increasing the discrimination between targets and clutter. The effectiveness of the proposed method is verified compared to both popular energy-domain detection methods and existing feature-domain detection methods, and the results show that our method exhibits superior detection performance. Moreover, we further analyze our method on different target models and different clutter distributions to prove that our method is suitable for different types of targets and clutter.

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A novel Laplacian vector directional filter designed for real-time removal of impulsive noise from color images

Ben Atitallah,

Alanazi

2024

Computers and Electrical Engineering

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Detection of range-spread targets based on order statistics

Cited by 5 publications

References 20 publications

Robust Artificial Intelligence-Aided Multimodal Rail-Obstacle Detection Method by Rail Track Topology Reconstruction

Robust Artificial Intelligence-Aided Multimodal Rail-Obstacle Detection Method by Rail Track Topology Reconstruction

Polarimetric Range Extended Target Detection via Adaptive Range Weighted Feature Extraction

A novel Laplacian vector directional filter designed for real-time removal of impulsive noise from color images

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