Abstract
Edge computing has a wide range of applications in the Internet of Things (IoT), especially suitable for low latency, high bandwidth, and high reliability. Edge computing enabling IoT can locate the vehicles rapidly with the help of edge computing. The sensors of IoTs can construct the Frequency diverse array (FDA) radar system, which has been widely concerned by scholars in recent years. The monostatic FDA and multiple-input-multiple-output (FDA-MIMO) is a research hotspot in parameter estimation field, but the research based on bistatic FDA-MIMO radar is insufficient. In this paper, we propose a tensor-based target location method in bistatic FDA-MIMO radar, which implements joint direction of arrival (DOA), direction of departure (DOD) and range parameters estimation. First of all, subarrays with different transmission frequency increment are used to construct the transmitting array to overcome the coupling between DOD and range. Then the tensor-based third-order signal model is established, which saves the multidimensional structure characteristics of received signal. And the signal subspace of each subarray is estimated by high-order-singular value decomposition (HOSVD). Furthermore, the phase period ambiguity is eliminated by limiting the range of the target, and the method for DOA, DOD and range parameters matching is provided. Theoretical analysis and numerical simulations demonstrate the effectiveness and superiority of the proposed method.
Highlights
Edge computing can support unmanned driving, traffic flow diversion and congestion prediction, etc, in urban traffic through the real-time data processing and analysis functions of edge computing
Edge computing enabling Internet of Things (IoT) can locate the vehicles rapidly with the help of edge computing based on the sensors of IoTs [1]–[4], which can be constructed with the radar system
Edge computing enabling IoT for vehicle location system is shown in Figure 1, the sensors IoTs are constructed with bistatic Frequency diverse array (FDA)-MIMO radar system
Summary
Edge computing can support unmanned driving, traffic flow diversion and congestion prediction, etc, in urban traffic through the real-time data processing and analysis functions of edge computing. T. Xu et al.: Vehicle Location in Edge Computing Enabling IoTs Based on Bistatic FDA-MIMO Radar to suppress clutter. In [21], some scholars proposed a method for estimating target positioning parameters using sub-array FDA. The research of FDA-MIMO radar mainly focuses on the monostatic scenario, because the VOLUME 9, 2021 estimation of angle and range can be achieved by extending the method based on MIMO radar [35], [39], [40]. We proposed a tensor-based method for joint estimation of DOA, DOA and range in bistatic FDAMIMO radar with the subarray mode. (1) The proposed method implements the FDA-MIMO radar target location in the tensor domain.
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