As the safety problems and economic losses caused by traffic accidents are becoming more and more serious, intelligent transportation system (ITS) came into being. After the outbreak of COVID-19, how to achieve effective traffic scheduling and macro command under less contact has attracted more attention. Therefore, the location estimation of traffic objectives is a key issue. In the developed framework, for the target parameter estimation in traffic, frequency diversity array multiple-input multiple-output (FDA-MIMO) radar is introduced into ITS, and tensor decomposition is used to process transportation big data (TBD) to improve the real-time performance of target location estimation. Unfortunately, spatial colored noise and array gain-phase error will affect the performance of FDA-MIMO radar in ITS. An algorithm that can solve the angle-range estimation problem of FDA-MIMO radar in the co-existence of array gain-phase error and spatial colored noise is proposed. Firstly, the four-dimensional tensor is constructed by using the temporal un-correlation of colored noise. Therefore, the influence of colored noise in ITS is removed. Secondly, the direction matrix containing target information is obtained by parallel factor (PARAFAC) decomposition. For the array gain-phase error, the optimization problem is constructed, and the Lagrange multiplier is employed to calculate the optimal solution. The effect of gain-phase error is eliminated by utilizing the optimal solution and the direction matrices. Finally, the location information of motor vehicle is achieved by calculating the solution of least square (LS) fitting. The developed scheme can achieve the location information of motor vehicles in the co-existence of array gain-phase error and spatial colored noise. Comprehensive numerical experiments illustrate that the developed scheme in ITS can efficiently obtain the location information of motor vehicles.

An equivalence principle algorithm (EPA) method is extended to analyze the electromagnetic scattering from multiple bodies of revolution (MBoR) with the axes arbitrarily oriented. Equivalence spheres are used to enclose each BoR and the equivalence currents are expanded by the basis functions of bodies of revolution (BoR). To obtain the scattering operators and translation operators of EPA for Fourier modes independently, the rotational symmetry systems are established in local BoR coordinate systems. The origin of the local BoR coordinate system is located at the center of the equivalence sphere and the z-axis coincides with the axis of the enclosed BoR to obtain the scattering operator of each equivalence sphere, whereas the origin is located at the observation sphere and z-axis passes through the center of the source sphere to obtain the translation operator of each pair of equivalence spheres. The current coefficient transformation algorithm is used to transform the equivalence currents among local BoR coordinate systems. The total equation is iteratively solved in the global coordinate system. The proposed scheme is especially efficient for the analysis of scattering from MBoR randomly distributed in electrically large scale region. Numerical results are given to demonstrate the efficiency.

Smart city enters the new 3.0 era, and Internet of Things (IoT) perform as the urban neural network in smart city. In the industrial areas of smart city, the IoT focuses on industrial applications, such as logistics and environmental monitoring. In this work, an auxiliary position analysis framework composed of IoT and distributed mobile-edge computing (MEC) is proposed to analyze the position of vehicles in the industrial areas of smart city. In the proposed framework, IoT are deployed by multiple unmanned aerial vehicles (UAVs) equipped with uniform linear array (ULA), which receives the signal emitted by vehicles for obtaining the direction of arrival (DOA), and the distributed MEC provides computing and synchronization services to auxiliary positioning. The DOA estimation is a key issue for auxiliary positioning in the proposed framework. To realize DOA estimation with unknown mutual coupling (MC) existing in IoT nodes, a novel block sparse Bayesian learning (SBL) algorithm is developed. In the developed algorithm, the unknown MC existing between sensors in each IoT nodes is first fused with the signal by parameterizing steering vector. Then, a block SBL (BSBL) procedure is presented to perform DOA estimation by using the inherent block sparse structure in the equivalent signal obtained after fusion. Benefiting from the fusion of MC and signal, the developed DOA estimation algorithm does not require a separate estimation of the unknown MC and also does not cause the loss of the array aperture. Based on the DOA estimation information, the position of vehicles in the industrial environment is effectively analyzed through weighted multiple cross-locations. Synthetic data set simulation is carried out to verify that the vehicle positions in smart city can be efficiently analyzed and estimated based on the presented framework and algorithm.

In this communication, the adaptive cross approximation (ACA) algorithm is utilized to analyze the scattering from arbitrary metallic bodies of revolution (BoRs) that is formulated by the electric field integral equation approach. For a given mode, a multilevel partitioning is used to group the basis functions of the BoRs along the longitudinal dimension. The interactions of the adjacent groups are calculated directly by the method of moments (MoM), and the interactions of well-separated groups are compressed by the ACA algorithm. The memory requirement and CPU time consumption are reduced drastically.

We propose a multiscale solver of equivalence principle algorithm with a body of revolution (BoR) equivalence surface (ES). First, the whole object is decomposed into subdomains; the ESs are defined as proper BoRs (e.g., spheres in this work) to enclose each subdomain. The Rao-Wilton-Glisson (RWG) and BoR basis functions are defined on each sphere, respectively. Second, the octree is constructed in each nonzero subdomain; the multilevel fast multipole algorithm (MLFMA) is employed to solve the equivalent currents on the ES individually, and then the equivalent currents are projected from RWG onto the BoR basis functions. The couplings between neighboring subdomains are evaluated directly using MLFMA, and the separated subdomains are substituted by the ES-to-ES couplings, and are evaluated efficiently by the BoR method of moments (BoR-MoM). To solve the equation system, a hybrid inner and outer iterative solver is employed, where the inner iteration is used to solve each local subdomains and the outer iteration is used to update the global solutions by collecting all the local solutions. Numerical results and discussions demonstrate the validity of the proposed work.

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.