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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 antenna 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.

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