Radar Point Cloud Clustering Method Based on Optimization

Shen, Lulu; Li, Kun; Zhu, Lin; Liu, Xuewei; Ma, Liyong

doi:10.1109/ccet52649.2021.9544261

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2024

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Cited by 3 publications

References 9 publications

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Unmanned Surface Vehicle Target Detection Based on LiDAR

Li,

Wang,

Xie

et al. 2024

Lecture Notes in Electrical Engineering

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Unmanned Surface Vehicle Target Detection Based on LiDAR

Li,

Wang,

Xie

et al. 2024

Lecture Notes in Electrical Engineering

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Visual target detection for energy consumption optimization of unmanned surface vehicle

Liu

Yong

et al. 2022

Energy Reports

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A Novel Method to Select Hyperparameters of the DBSCAN Algorithm for RADAR Applications

Payghan,

Prajapati,

Chauhan

2024

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">As the automotive industry is coming up with various ADAS solutions, RADAR is playing an important role. There are many parameters concerning RADAR detections to acknowledge. Unsupervised Clustering methods are used for RADAR applications. DBSCAN clustering method which is widely used for RADAR applications. The existing clustering DBSCAN is not aligned very well with its hyperparameters such as epsilon (the radius within which each data point checks the density) and minimum points (minimum data points required within a circle to check for core point) for which a calibration is needed. In this paper, different methods to choose the hyperparameters of DBSCAN are compared and verified with different clustering evaluation criteria. <b>A novel method to select hyperparameters of the DBSCAN algorithm</b> is presented with the paper. For testing the given algorithm, ground truth data is collected, and the results are verified with MATLAB-Simulink.</div></div>

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Radar Point Cloud Clustering Method Based on Optimization

Cited by 3 publications

References 9 publications

Unmanned Surface Vehicle Target Detection Based on LiDAR

Unmanned Surface Vehicle Target Detection Based on LiDAR

Visual target detection for energy consumption optimization of unmanned surface vehicle

A Novel Method to Select Hyperparameters of the DBSCAN Algorithm for RADAR Applications

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