Abstract
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.
Highlights
Industrial internet of thing (IIOT) is acknowledged as the trend of the manufacturing industry [1], which aims to promote product innovation, improve operation level, and expand novel business models
We investigated the problem of optimal Tx/ Rx selection for vehicle positioning in IIOT. e object is to minimize the Cramer–Rao bound (CRB) on multiple-input multipleoutput (MIMO) radar DOA estimation error with limited transmit power. e Tx/Rx selection issue is treated as a multiclass-classification problem, and a CNNauxiliary framework is proposed
We consider a monostatic MIMO radar setup, which is configured with M transmit elements and N receive elements in total, both of which are uniform linear arrays (ULA) with halfwavelength spacing
Summary
Industrial internet of thing (IIOT) is acknowledged as the trend of the manufacturing industry [1], which aims to promote product innovation, improve operation level, and expand novel business models. In [12], the sensors were dynamically adjusted via minimizing the Cramer–Rao bound (CRB) on parameter estimation, and the greedy search algorithm was adopted Another reconfigurable receive array selection framework was addressed in [13], in which the conditional Bobrovski–Zakai bound (BZB) on DOA estimation was chosen as a performance metric. We investigated the problem of optimal Tx/ Rx selection for vehicle positioning in IIOT. E object is to minimize the CRB on MIMO radar DOA estimation error with limited transmit power. E covariance matrix of the MIMO radar node is fed to a CNN to find an optimal Tx/Rx pair for the scans. Once DOA of the target vehicle is obtained, the position information can be accurately recovered via solving an inverse problem [3]. We only focus on how to select the optimal Tx/Rx pair
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