Multiband fusion (MF) technology can greatly enhance the range resolution of radar by generating ultrawideband echo using several subbands echoes (SBEs). A critical issue of MF is how to accurately estimate and compensate for incoherence between SBEs. In this letter, a novel high-accuracy incoherence compensation method is proposed. First, the phase-coherent coefficient (PCC) is introduced to quantify the phase correlation between SBEs, which is not exactly analyzed in the existing MF methods. Then, it is noticed that for PCC, the linear phase and the fixed phase are coupled and difficult to be estimated simultaneously. Accordingly, this letter proposes to estimate the linear phase and the fixed phase separately, where the linear phase is estimated and compensated by applying the information entropy minimization criterion, and the fixed phase is then compensated based on PCC. Finally, the validity and advantages of the proposed method are investigated by numerous simulation results.

Positioning accuracy and efficiency are two important features for embedded positioning devices of Internet of Things (IoT) in smart cities. In modern urban canyon environments, Non Line-of-Sight (NLoS) satellite signals may degrade positioning accuracy. The mirror ray tracing algorithm can be used to reconstruct NLoS propagation path and enhance positioning accuracy. However, for the embedded positioning devices of IoT, real-time reconstruction of NLoS propagation path is difficult. In this article, an inherited sampling algorithm ensuring positioning accuracy and efficiency is proposed. Experimental results show that the proposed inherited sampling algorithm saves computation time by 69.8% compared to the double sampling algorithm. Furthermore, a mirror ray tracing accelerator is designed and built in FPGA, which greatly reduces the NLoS path reconstruction time. Finally, a real-time positioning system with a CPU and FPGA heterogeneous architecture is built. For a single trajectory point, test results show that the correction time of the proposed inherited sampling algorithm with an FPGA accelerator takes only 0.45 s, which is 80 times faster than that with CPU calculation alone.

In this article, we investigate a resource allocation problem for a multiunmanned aerial vehicle (UAV) assisted integrated sensing and communication (ISAC) system, where a group of dual-functional UAVs perform simultaneous radar sensing of a target and data communication with multiple ground users (GUs). In particular, the trajectory of UAVs, user association, and beamforming design are jointly considered to maximize the sum weighted bit rate of all GUs while ensuring the sensing beampattern gain of the target. To cope with the above mixed-integer nonconvex optimization problem, we propose an efficient strategy by decomposing the original problem into two subproblems under the alternating optimization framework. For the user association and beamforming design, we propose a novel algorithm to circumvent the coupling relationship among GUs and UAVs by leveraging matching theory and fractional programming theory. For the nonconvex UAV trajectory subproblem, we apply the sequential quadratic programming to obtain a suboptimal solution by solving a sequence of quadratic programming problems. The above two subproblems are iteratively solved and a stable solution is obtained upon convergence. Simulation results show that the proposed strategy outperforms various benchmark schemes that are based on the deferred acceptance algorithm, K-means algorithm, and a heuristic algorithm. It is demonstrated that the proposed strategy efficiently improve the sensing beampattern gain and communication rate.

This article proposes convex optimization of mutual inductance between multiantiparallel coils (MACs) for distance-insensitive wireless charging of air–ground robots. In order to reduce optimization costs, the proposed optimization method is developed by hybridizing analytical and numerical models. First, it is shown that the commonly used analytical model for MAC design becomes inaccurate as frequency increases. Second, a trial mutual inductance is introduced as an optimization variable. From the trial mutual inductance, an MAC is designed using the analytical model. Third, the realized mutual inductance of the analytically designed MAC is computed by a numerical model to correct the error of the analytical model. The difference vector$\bar {\rho }$between the realized and optimal mutual inductances is written as a function of the trial mutual inductance, and the$l_{2}$-norm of$\bar {\rho }$is minimized by changing the trial mutual inductance. The resultant optimization problem is shown to be convex, and it is conveniently solved by using a local searching method. Simulation results show that the proposed design method satisfies design specification much better than the conventional analytical-model-based design method. Using the proposed method, an MAC prototype is designed and fabricated, and a wireless charging system for air–ground robots is constructed and tested. Measurement results show that the efficiency of the designed MAC is maintained at around 80% in the transfer distance range of 15–55 mm, and stable efficiency is obtained when charging real air–ground robots of different heights.

Equivalent models of antennas are useful for the fast simulation of vehicle-to-everything (V2X) communication. The existing antenna-equivalent models are inflexible because they assume rectangular antenna contour. This article presents a hybrid-equivalent surface-edge current model to overcome the limitation of the existing equivalent models. Based on Huygens' principle, a V2X communication antenna is equivalent to a conducting plate excited by equivalent surface magnetic current (ESMC). The sources of fields reflected by the conducting plate and diffracted by its edges are modeled as the image of ESMC and equivalent edge current (EEC), respectively. A hybrid-equivalent surface-edge current model is thus established, and it consists of ESMC, the image of ESMC, and EEC. The unknown sources in the proposed model are solved from near fields of the antennas, and the proposed model is then used to simulate the radiation performance of antennas installed on vehicles. The transmission coefficient between V2X communication antennas can also be calculated by using the proposed model and the electromagnetic reaction theorem. Simulations and experiments are performed to demonstrate the effectiveness of the proposed method. It is shown that the proposed equivalent model not only accurately models antennas with arbitrarily shaped contour but also significantly accelerates the integrated simulation of antennas and vehicles.

Electrical modeling of vehicular antennas is required for design of modern vehicle-to-everything communication networks. However, full-wave modeling and the existing equivalent source methods still ask for long computation time. To address this issue, a fast simulation method is proposed in this paper: a sparse equivalent source model is developed based on Huygens’ principle where a vehicular antenna is made equivalent to magnetic current placed on a finite conducting plate. The image theory and the uniform theory of diffraction are then applied, a modified Green’s function is derived, and the sparse equivalent source model is constructed to replace the original vehicular antenna. Because of its simple geometry and material, in comparisons with the existing methods, the proposed model uses significantly less expenditures for simulation of vehicular antennas while has better accuracy due to the consideration of the finite conducting plate. Simulations are performed to demonstrate the effectiveness and advantages of the proposed method, and a real-world experiment is designed and conducted to demonstrate the superiority of the proposed method with real measurement data. It is shown that the proposed method can save simulation time by four times, with less than 1-dB error compared to measurement.

Radio frequency (RF) source reconstruction is useful for RF radiation analysis and interference diagnosis in Internet of Things. Near field (NF) sampling is a critical step of RF source reconstruction. Accurate RF source reconstruction usually requires a large number of NF samples, which results in tremendous effort of NF sampling. This article presents an adaptive NF sampling method based on the skeletonization scheme. First, sources to be reconstructed are related to NF samples through integral equation (IE). Second, the IE is discretized with the method of moments, and thus the interaction matrix between source and field points is found. Third, strong rank-revealing QR factorization is applied to the interaction matrix, which results in a permutation matrix, a row skeleton matrix, and a transformation matrix. Finally, a small number of skeleton sampling points are selected by analyzing the permutation matrix. The fields at skeleton sampling points can be used to calculate the fields at other sampling points through the transformation matrix. Hence, one only needs to perform NF sampling at a small number of skeleton sampling points, which significantly reduces the expenditure of NF sampling. Simulations using synthetic and measurement data are presented to show the effectiveness and advantages of the proposed sampling method.