Vehicle Location in Edge Computing Enabling IoTs Based on Bistatic FDA-MIMO Radar

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.