Narrow Results

This paper focuses on the intelligent recognition problem of radar detection targets. Aiming at the low accuracy and slow speed of millimeter-wave radar clustering point cloud information, a feature algorithm of millimeter-wave radar suitable for detecting targets is proposed. In the detection of targets by millimeter-wave radar, distance is the biggest factor affecting the number and degree of sparsity. A method that combines the feature information of the point cloud with the KD tree proximity search algorithm and the DBSCAN clustering algorithm is proposed, which can adapt to the problems of uneven target point cloud, small amount of data and slow clustering speed. The improved algorithm can use the KD tree to quickly find adjacent points and calculate the distance between adjacent points. The corresponding number of thresholds is set according to the distance where the target is located, and the radius of the target area reflected by the millimeter-wave radar plus the distance of the last threshold point is used as the neighborhood radius of the improved algorithm. Therefore, fast and adaptive parameter adjustment of the millimeter-wave radar can be realized. Simulation tests show that the improved clustering algorithm has better parameters. The accuracy of the improved algorithm is increased by 4.2%, and it also greatly improves the clustering speed.

The ground 3D laser scanning technology has the advantages such as normal traffic flow, large amount of data and high efficiency, so it is suitable for application in surveying and mapping of existing road reconstruction and expansion. This paper takes a specific road reconstruction and expansion project as an example to study the process which is suitable for 3D laser scanning technology from the survey program design, equipment selection, geodetic chain and target layout aspects. In the meantime, related software to complete the point cloud data filtering, splicing, coordinate conversion and simplification are utilized along with the CASS software to generate DTM model for road reconstruction and expansion design. It turns out that this technology has high data accuracy. According to the test results, the difference between the data obtained by this technology and by traditional measurement methods respectively is under 4 mm. It can fully meet the design requirements of road reconstruction and expansion and has very good application prospects.