Abstract
This article focus on logistics vehicle active localizing using multiple-input multiple output (MIMO) radar, and a fast localizing architecture that integrates edge computing and cloud platform is proposed. The core of the proposed methodology is to measure the angles of logistics vehicle using bistatic MIMO radars, which are configured with coprime electromagnetic vector sensors (EMVS). Unlike the existing localization systems, the proposed localizing architecture provides two-dimensional (2D) angle estimation, i.e., 2D direction-of-departure (DOD) estimation and 2D direction-of-arrival (DOA) estimation, and it offers additional polarization status of the logistics vehicle. A parallel factor (PARAFAC) estimator is developed. Firstly, it estimate the factor matrices via PARAFAC decomposition. Thereafter, the elevation angle estimation is accomplished via least squares (LS) technique, which are ambiguous due to the coprime property of the EMVS. The azimuth angle estimation are followed via vector cross-product. Besides, the polarization information of the targets can be obtained estimated via LS approach. The above process is accomplished at cloud platform. Finally, the localization of the logistics vehicle is achieved with the estimated 2D angles, which is computationally friendly and achieved via edge computing. Detailed analyses concerning degree-of-freedom, identifiability, complexity as well as Cramér-Rao bound (CRB) are provided. To show the effectiveness of the proposed architecture, numerical simulations have been designed.
Highlights
I NTERNET of vehicles (IoV) is an interesting topic for further logistics vehicle [1]
We focus on the active logistics vehicle localizing using multiple-input multiple output (MIMO) radar
In this paper, a logistics vehicle localizing that EMVSMIMO radar, edge computing and cloud platform is proposed
Summary
I NTERNET of vehicles (IoV) is an interesting topic for further logistics vehicle [1]. The AOA approaches become a nice choice since they can be and accurately measured via sensor array. The existing AOA approaches can be divided into two categories, the passive method and the active schemes. The former utilizes the wireless communication system to measure the AOA of the base stations, while the latter adopts the array radar to calculate the relative AOA of the vehicle with respective to the transmit sensors and the receive sensors, i.e., direction-of-departure (DOD) and direction-ofarrival (DOA). We focus on the active logistics vehicle localizing using multiple-input multiple output (MIMO) radar. The main task is to measure the DOD and DOA of logistics vehicle via MIMO radar
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