Flexible MIMO Radar Antenna Selection for Vehicle Positioning in IIOT Based on CNN

Yang Xiong, Ke Wang, Liangtian Wan
2020 Mathematical Problems in Engineering  
Unmanned vehicles are widely used in industrial scenarios; their positioning information is vital for emerging the industrial internet of thing (IIOT); thus, it has aroused considerable interest. Cooperative vehicle positioning using multiple-input multiple-output (MIMO) radars is one of the most promising techniques, the core of which is to measure the direction-of-arrival (DOA) of the vehicle from various viewpoints. Owing to power limitations, the MIMO radar may be unable to utilize all the
more » ... ntenna elements to transmit/receive (Tx/Rx) signal. Consequently, it is necessary to deploy a full array and select an optimal Tx/Rx solution. Owing to the industrial big data (IBD), it is possible to obtain a massive labeled dataset offline, which contains all possible DOAs and the array measurement. To pursuit fast and reliable Tx/Rx selection, a convolutional neural network (CNN) framework is proposed in this paper, in which the antenna selection is formulated as a multiclass-classification problem. Herein, we assume the DOA of the vehicle has been known as a prior, and the optimization criterion is to minimize the Crame´r–Rao based on DOA estimation when we use the selected Tx/Rx subarrays. The proposed framework is flexible and energy friendly. Simulation results verify the effectiveness of the proposed framework.
doi:10.1155/2020/2048606 fatcat:ydzero74xrhvbic4texyqxljvq