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Tracking of Extended Objects with Multiple Three-Dimensional High-Resolution Automotive Millimeter Wave Radar
Technical Paper
2019-01-0122
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Estimating the motion state of peripheral targets is a very important part in the environment perception of intelligent vehicles. The accurate estimation of the motion state of the peripheral targets can provide more information for the intelligent vehicle planning module which means the intelligent vehicle is able to anticipate hazards ahead of time. To get the motion state of the target accurately, the target’s range, velocity, orientation angle and yaw rate need to be estimated. Three-dimensional high-resolution automotive millimeter wave radar can measure radial range, radial velocity, azimuth angle and elevation angle about multiple reflections of an extended target. Thus, the three-dimensional range information and three-dimensional velocity information can be obtained. With multiple three-dimensional high-resolution automotive millimeter-wave radar, it is possible to measure information in various directions of a target. For tracking of extended objects with multiple three-dimensional high-resolution automotive millimeter wave radar, firstly, the information of one target in different radar is obtained by the clustering algorithm, and then it is fused by an unscented Kalman filter, and the tracking algorithm needs to consider the information of different scattering points of the target, and the speed information component of the target needs to be considered as the tracking feature. Thus, a motion model with target’s range, velocity, orientation angle and yaw rate are established for the tracking algorithm. The results show that the tracking algorithm is able to estimating the target information with acceptable error.
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Bai, J., Tan, B., Bi, X., and Huang, L., "Tracking of Extended Objects with Multiple Three-Dimensional High-Resolution Automotive Millimeter Wave Radar," SAE Technical Paper 2019-01-0122, 2019, https://doi.org/10.4271/2019-01-0122.Data Sets - Support Documents
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