With the continuous improvement of 5G network infrastructure, the informatization process in the industrial sector is accelerating at an unprecedented pace. The market demand for gateways integrating 5G networks with multi-access edge computing (MEC) technology is growing increasingly strong. There is also a rising requirement for these gateways to possess capabilities such as high reliability, large bandwidth, fast processing speed, and localized data processing. This paper, in response to the practical management needs of photovoltaic power stations and industrial fields, proposes the design of a 5G artificial intelligence (AI) edge computing intelligent gateway with dual 5G modules. The design is based on the i.MX8M Plus processor and integrates dual 5G module design. It supports multiple communication modes, including 5G, Wi-Fi, Ethernet, and serial ports. The software incorporates edge computing scripts, various communication protocols, and a lightweight AI model, enabling real-time data acquisition and processing at the edge. It supports for several industrial standard protocols ultra-low latency acquisition, and intelligent operation features like remote over-the-air upgrade, secure shell secure operation, and configuration hot updates. The system was tested and validated in a typical photovoltaic application scenario. The results demonstrate that, compared with the traditional single-module Internet of Things gateways, the dual 5G AI edge gateway proposed in this paper reduces end-to-end latency by more than 70%, improves data reliability tenfold, enhances bandwidth by 100%–200%, extends coverage capability by 50%, effectively alleviates the bandwidth pressure on the core network, and enhances the overall reliability and security of the system.
Imagery modeling for microwave engineering generally requires a large number of training samples to generate images. In this paper, a variational autoencoder (VAE) with the multilayer perceptron (MLP) is proposed for efficient imagery modeling of electromagnetic behaviors of the microwave antenna and component. The input of VAE is the randomly generated binary images of a physical structure, and VAE is an unsupervised learning scheme. The training of MLP needs the labeled samples of the latent representation of VAE and the electromagnetic responses. Compared with supervised imagery modeling, VAE generates a large number of low-cost images applied to the image processing section. The implementation of unsupervised learning eliminates the need for labeled samples from full-wave simulations, resulting in significant time saving. The proposed model is confirmed with two examples of a pixel antenna and a microstrip/coplanar waveguide ultrawideband filter, and the results show its improvement in accuracy and efficiency.
Non-terrestrial network (NTN) possesses ubiquitous coverage and access capability, which needs high-reliability and high-throughput transmission supported by the advanced channel coding. In this paper, a cooperative transmission and optimization scheme is proposed based on polar codes in NTN. Firstly, the transmission system model is designed. Then the polar coded cooperation method among the cooperative satellite nodes (CSN) and destination is provided in Rayleigh slow fading channel. Moreover, to improve the overall error probability performance and throughput efficiency, the key coding rate and power allocation of CSN are jointly optimized using multiobjective reinforcement learning algorithm. Finally, simulation and analysis demonstrate the validity of our approach and show the error-correcting performance and throughput advantages of the proposed cooperative transmission scheme in NTN.
To address the significant overhead of the beam training in millimeter wave (mmWave) wireless communications, we propose a balanced multimodal fusion network with bi-directional enhancement in the previous work. It can predict the optimal mmWave beam according to the mmWave wide beam and sub-6 GHz channel state information. Utilizing the interaction and complementarity between different modes can improve the accuracy and robustness of predictions compared to the single mode (mmWave only or sub-6 GHz only). However, the feature extraction of one modality can potentially impact other modalities via parameter backpropagation. Although multimodal models perform better than unimodal models, they may be underutilized. To solve the problem, the adaptive multimodal gradient (AMMG) optimization method is designed to update the gradient of per feature extraction branch adaptively in this paper. Furthermore, a multimodal bi-directional enhancement (MBdE) block is proposed to combine features of other modalities, enhancing the complementarity of mmWave modality and sub-6 GHz modality. As revealed by the numerical results, it is evident that the proposed scheme delivers more precise outcomes with lower overhead compared to conventional and modern deep learning methods.
A wideband low-profile magneto-electric (ME) dipole antenna is proposed in this paper. In order to reduce the profile of ME-dipole antenna, a planar wideband balun with metallic holes connection is proposed to replace the formal feeding structures. Four resonant modes are excited in the ME-dipole for wide impedance matching bandwidth. By combing the wideband balun with multi-mode ME-dipole, an impedance bandwidth of 81.3% (2.7−6.4 GHz) for voltage standing wave ratio (VSWR) less than 2.5 is achieved. This antenna structure is designed based on the multi-layer printed circuit board (PCB) technology, with the advantage of low cost and easy to fabricate. The overall size of the fabricated antenna is (0.5×0.5×0.061)λL (λL is the wavelength of low frequency). The radiation patterns of this antenna are measured with an average gain of 5.3 dB in each resonant modes. In addition, the average radiation efficiency of this antenna is more than 75%, and the cross polarization is lower than −20 dB over the whole frequency band.
The radar cross section (RCS) scale transformation of non-metallic targets is mainly limited by the frequency dispersion relationship of material, making it difficult to be implemented in actual measurements. If the scale dependences of scattering centers can be found to avoid the influence of frequency, it is expected to provide a new solution for the scale measurement of RCS of non-metallic targets. Therefore, based on the high-frequency asymptotic method, this paper first derives the scale dependences of the specular reflection mechanism and the edge diffraction mechanism for three common specular reflection structures and two edge diffraction structures. Then, referring to the expression of the frequency dependent factor in the geometric theory of diffraction (GTD) model, the scale dependent factors of the above scattering mechanisms are analogously proposed. The scale dependent factors are extended to thin-layer coated structures and arbitrary multiple scattering cases. Simulation experiments verified the correctness of the scale dependent factors. Finally, this paper presents an extended expression form of the GTD model, laying a theoretical foundation for the subsequent RCS scale transformation of non-metallic targets based on scattering centers.
The physical architecture of optical convolution restricts its capacity to capture multi-scale features from targets, thus impeding the precision of network recognition. In this work, we propose a multi-scale optical convolutional neural network (MS-OCNN), which uses convolution kernels with different resolutions in the convolution layer to extract different scale features, along with attention mechanism and residual structure to analyze features. By separating the training and inference platforms of the network, we facilitate the electronic training of model parameters on a computer and the optical deployment on a system equipped with a spatial light modulator, enabling efficient target classification. The proposed MS-OCNN exhibits 1% to 3% improvement in classification performance on the modified national institute of standards and technology (MNIST) and Fashion-MNIST datasets compared to single-scale optical inference models. Online experimental systems in real-world scenarios have validated the target recognition capabilities of this method, which yielded classification accuracies of 97% and 87% on the MNIST and Fashion-MNIST datasets, respectively. This work enhances the feature acquisition capabilities of optical convolutional networks, elevates network recognition accuracy, and significantly propels the application of optical computing in domains such as guidance systems, autonomous driving, and robotics.
As is well known, mutual coupling between array elements has a significant negative impact on direction of arrival (DOA) estimation. To achieve DOA estimation under unknown mutual coupling, this paper proposes a low computational complexity Newton-like method. Firstly, a block sparse model based on the signal subspace is established, and the Lagrangian function is established according to the block sparse model. Secondly, since the Hessian matrix of the Lagrangian function cannot always ensure positive definiteness and the computational complexity of the inverse matrix of the Hessian matrix is enormous, the Newton method is no longer applicable. Therefore, this paper proposes a Newton-like method to achieve DOA estimation under mutual coupling and reduce the computational complexity by matrix inversion lemma. Finally, compared with existing methods of DOA estimation under array mutual coupling, the simulation results validate the effectiveness of the proposed method.
A millimeter-wave (mm-Wave) dual circularly polarized (CP) antenna in gap waveguide (GWG) technology with high port isolation is proposed in this paper. It is consisted of a simplified orthomode transducer (OMT) and an improved multi-section hexagonal waveguide CP horn antenna. The OMT is composed of two metal layers without the traditional septum or iris, which makes the structure simpler. The CP horn antenna can be easily integrated with the OMT without mode conversion. The principle analysis as well as the simulated and measured results of the proposed antenna are given in this paper. The simulated and measured results agree very well with each other. The port isolation of more than 27 dB over bandwidth of 26.5?31 GHz (|S11|< ?15 dB) is achieved with relative bandwidth of 15.7%. The axial ratio (AR) lower than 2.5 dB for both left-hand and right-hand CP (LHCP and RHCP) are achieved over the bandwidth. The proposed antenna is a candidate for mm-Wave satellite communications or beyond fifth-generation (5G) communications applications.
The calibration of parameters for onboard stereo cameras is crucial for achieving efficient visual-assisted driving. However, in practical scenarios, low-resolution images often result in inaccuracies in feature point extraction, thereby affecting the accuracy of camera parameter calibration. To address this issue, this paper proposes a self-calibration method for stereo cameras based on joint de-noising, de-mosaic, de-ringing, and super-resolution network (JDDDSN) super-resolution reconstruction. By reconstructing images into higher-resolution images with richer details, feature points are extracted for extrinsic calibration of stereo cameras. For real-world driving scenarios, the reconstructed images achieve noise and ringing artifact reduction while obtaining clearer high-resolution images. This study further investigates the impact of the super-resolution reconstruction network on target area calibration at various distances. Additionally, it highlights the significant role of super-resolution in enhancing stereo camera calibration accuracy by removing dynamic points and focusing on static regions. Through a series of experiments, this paper validates the effectiveness and practicality of the JDDDSN super-resolution reconstruction network in improving stereo camera calibration accuracy, demonstrating its application value in the field of stereoscopic vision.
A dual-frequency aperiodic planar scanning array with diversified radiation elements is proposed. The proposed element consists of an elliptic patch and two parasitic rectangular patches, which can work both at 5.28?5.33 GHz with difference radiation beam and 5.78?5.83 GHz with sum radiation beam. A four equivalent magnetic currents model has been established to explain the radiation principle of the proposed element. Compared to the beamwidth of the sum beam, the 3 dB beamwidth of the difference beam is broadened about 30% and the radiation gain of the sum beam is obviously improved about 48%. A 64-element aperiodic array with diversified radiation elements is constructed. The array can scan at a wide angle of ±70° with no grating lobes at difference radiation mode and can scan in the range of ±60° with high gain at sum radiation mode.
To deal with the problem that the block sparse Bayesian algorithm exists in grid estimation, an off-grid weighted block sparse Bayesian algorithm is proposed based on coherent accumulation. The algorithm first uses the signal characteristics to coherently accumulate the polarization-sensitive array received data to enhance the signal-to-noise ratio (SNR); then the first-order Taylor expansion of the steering vector is performed, and an off-grid real-valued model is introduced by improving the block structure; then the weighting vectors are introduced to accelerate the iteration of the algorithm and reduce the number of iterations; and finally, the solution of the off-grid parameters is achieved by iterative optimization of the parameters. Compared with the traditional block sparse Bayesian learning (BSBL) algorithm, the method iterates faster and achieves efficient joint off-grid polarization-DOA estimation. Simulation results show the effectiveness of the algorithm.
Frequency planning is of great significance which can effectively dispatch the battlefield resources of radio equipment. To enhance the efficiency of scheduling, this paper investigates the frequency planning problem (FPP) and puts forward a multi-objective approach. The proposed multi-objective model considers the coordination constraints of radio equipment alongside diverse resources, defining key points to delineate the cooperative interactions among radio equipment. The model integrates considerations of time, space, and energy, focusing on electromagnetic interference, frequency demand satisfaction and frequency occupancy as its primary optimization objectives. To improve the accuracy of the solution, this study proposes a multi-population multi-objective memetic algorithm (MPMA). This algorithm employs a segment-based coding strategy and a specialized genetic operator to facilitate the integration of global and local search techniques. Additionally, chaos initialization and a multi-population-based scheduling approach are incorporated to enhance global search performance. The experimental results demonstrate the superiority of the proposed model and MPMA in meeting the diverse scheduling needs of radio equipment across various scenarios.
Sign language recognition (SLR) can be improved by using millimeter-wave radar, which safeguards the privacy of those who are hearing-impaired. However, existing SLR methods do not fully utilize the unique features of radar echoes, resulting in limited accuracy. Sign language is composed of an individual’s poses and hand movements. To obtain these recognition features, this paper presents a multi-modal-feature-based SLR (MMF-SLR) network framework. This method first constructs a transformer-pose network to extract the human skeleton information, which represents the poses in sign language, from the radar images. Additionally, hand movement information can be represented by the range-Doppler sequence and micro-Doppler signatures. The human skeleton and hand movement information are input into a multimodal fusion network to achieve high-accuracy SLR. The experimental results demonstrate that the proposed method can enhance the recognition accuracy of the sign language with similar poses or movements compared to the traditional SLR methods.
The track association of clustered targets is a crucial step in integrating detection results from multiple sensors. Nonetheless, traditional association methods are frequently impaired by reduced accuracy due to challenges such as high-density clusters and observation mismatches. To address these issues, a dual-channel TCN-GRU network is developed which leverages temporal convolutional networks (TCN) and gated recurrent units (GRU) to capture subtle differences in track features. Furthermore, an association module based on the global nearest neighbor (GNN) approach is elaborated to refine scenario perception of the association task. Experimental findings indicate that the proposed method attains a track association accuracy of 87.16%, with a 6.29% improvement credited to the GNN module. This work signifies the novel integration of deep learning models with traditional methods in the realm of clustered targets track association, providing significant insights for the advancement of track association methodologies.
This paper proposes a three-dimensional (3D) interferometer direction-finding (DF) method to address the reduced accuracy of conventional two-dimensional (2D) interferometer DF methods in non-planar antenna configurations. First, we enhance the multi-channel soft synchronization (MCSS) technique by dynamically compensating for sampling point offsets, achieving phase estimation accuracy better than 0.01 sampling points. Second, we construct a 3D baseline model based on the 3D distribution characteristics of the antennas and introduce a 3D error allocation model to improve the system error estimation method, allowing for dynamic correction of calibration deviations. This effectively addresses the phase ambiguity issues caused by 3D mechanical errors. Finally, we develop a 3D DF algorithm and an optimized multi-pulse fusion approach utilizing the maximum-ratio combining (MFA-MRC) method to reduce DF errors and enhance system stability. Simulation experiments and flight tests demonstrate that the proposed method has a computational load of only 0.31% of the multiple signal classification (MUSIC) algorithm. When the baseline offset exceeds 10 mm, the angular accuracy improves from 0.9° to 0.3°, and the positioning accuracy is enhanced from approximately 10 km to around 1 km. The study holds significant theoretical and practical value in engineering.
In view of the large amount of data and dense pixel points in point cloud files, this article proposes a multiple point cloud file encryption algorithm based on principal component analysis (PCA) and fractional Fourier transform (FrFT). In this method, a point cloud data matrix (PCDM) is generated by extracting the coordinates and color information of the point cloud, then using PCA to reduce the dimension of a sequence of PCDMs, which are spliced and scrambled to produce a feature vector matrix and a dimension-reduced matrix (DRM) for encryption and reconstruction. Then using the hyperchaotic Lorenz system to generate the random phase masks and the orders of the FrFT. These two parameters will be used as keys to encrypt the point cloud feature vector matrix. The simulation results verify that the encryption algorithm can quickly encrypt multiple point cloud files, and the quality of the point cloud files obtained by decryption and reconstruction is good. The algorithm also has a large enough key space and highly sensitive keys, which means it has good security and strong robustness to different attacks.
In wireless sensor networks, ensuring communication security via specific emitter identification (SEI) is crucial. However, existing SEI methods are limited to closed-set scenarios and lack the ability to detect unknown devices and perform class-incremental training. This study proposes a class-incremental open-set SEI approach. The open-set SEI model calculates radio-frequency fingerprints (RFFs) prototypes for known signals and employs a self-attention mechanism to enhance their discriminability. Detection thresholds are set through Gaussian fitting for each class. For class-incremental learning, the algorithm freezes the parameters of the previously trained model to initialize the new model. It designs specific losses: the RFFs extraction distribution difference loss and the prototype transformation distribution difference loss, which force the new model to retain old knowledge while learning new knowledge. The training loss enables learning of new class RFFs. Experimental results demonstrate that the open-set SEI model achieves state-of-the-art performance and strong noise robustness. Moreover, the class-incremental learning algorithm effectively enables the model to retain old device RFFs knowledge, acquire new device RFFs knowledge, and detect unknown devices simultaneously.
The theoretical implementation aspects of scattered field prediction and angular glint calculation in near-field region are proposed in this work. First of all, a more refined expression of the Green function is developed. In this representation, an expansion center is adopted within the neighborhood of the sources. Then a high-frequency electromagnetic scattering evaluation algorithm is formulated, combining the refined physical optics (PO) and equivalent edge current (EEC) algorithm. The modified method not only retains the conciseness and efficiency of the standard code but also can be directly used in the near field (NF) scattering estimation. Afterwards, two basic concepts of the angular glint are briefly introduced and formulated. The proposed procedure makes preparation for the computation of NF linear deviation. Numerical examples demonstrate the accuracy and efficiency of the NF scattering prediction algorithm. The angular glint characteristics in near-field scenarios are also presented and analyzed in the final section.
In this paper, the joint design of transmit and receive beamformers for transmit subaperturing multiple-input-multiple-output (TS-MIMO) radar is investigated, aiming to enhance its low probability of intercept (LPI) capability. The main objective is to simultaneously minimize the transmission power, suppress the transmit sidelobe levels, and minimize the probability of intercept, thus bolstering the LPI performance of the radar system while maintaining the desired target detection performance. An alternative optimization method is proposed to jointly optimize the transmit and receive beamformers, yielding an unified LPI optimization framework. Particularly, the proposed iterative algorithm based on the Lagrange duality theory for transmit beamforming is more efficient than the conventional convex optimization method. Numerical experiments highlight the effectiveness of the proposed approach in sidelobe suppression and computational efficiency.
Weakly supervised semantic segmentation (WSSS) is a tricky task, which only provides category information for segmentation prediction. Thus, the key stage of WSSS is to generate the pseudo labels. For convolutional neural network (CNN) based methods, in which class activation mapping (CAM) is proposed to obtain the pseudo labels, and only concentrates on the most discriminative parts. Recently, transformer-based methods utilize attention map from the multi-headed self-attention (MHSA) module to predict pseudo labels, which usually contain obvious background noise and incoherent object area. To solve the above problems, we use the Conformer as our backbone, which is a parallel network based on convolutional neural network (CNN) and Transformer. The two branches generate pseudo labels and refine them independently, and can effectively combine the advantages of CNN and Transformer. However, the parallel structure is not close enough in the information communication. Thus, parallel structure can result in poor details about pseudo labels, and the background noise still exists. To alleviate this problem, we propose enhancing convolution CAM (ECCAM) model, which have three improved modules based on enhancing convolution, including deeper stem (DStem), convolutional feed-forward network (CFFN) and feature coupling unit with convolution (FCUConv). The ECCAM could make Conformer have tighter interaction between CNN and Transformer branches. After experimental verification, the improved modules we propose can help the network perceive more local information from images, making the final segmentation results more refined. Compared with similar architecture, our modules greatly improve the semantic segmentation performance and achieve 70.2% mean intersection over union（mIoU） on the PASCAL VOC 2012 dataset.
A methodology for the reduction of radar cross section (RCS) of cambered platforms within the target airspace is presented, which utilizes a dual-polarized ultra-wide-angle artificial electromagnetic absorbing surface. By applying the theory of generalized Brewster complex wave impedance matching, five distinct unit cell designs are developed to attain more than 95% absorption rate for dual-polarized incident waves within five angular ranges: 0°?30°, 30°?50°, 50°?60°, 60°?70°, and 70°?80°. To optimally reduce the RCS of a cambered platform, the five types of units can be evenly distributed on the surface based on the local incident angles of plane waves originating from the target airspace. As an illustrative example, the leading edge of an airfoil is taken into account, and experimental measurements validate the efficiency of the proposed structure. Specifically, the absorbing surface achieves more than 10 dB of RCS reduction in the frequency ranges from 5-10 GHz (about 66.7% relative bandwidth) for dual polarizations.
Mega low Earth orbit (LEO) satellite networks serve as effective complements to terrestrial networks. However, the dual mobility of users and LEO satellites makes inter-satellite handovers more frequent for users. Moreover, there are both ascending and descending segments in widely deployed walker-delta constellations. Even if the locations of users do not change, when the access satellites of the communicating parties are not in the same ascending or descending segment, the end-to-end latency between them will increase. To address this challenge, the self-decision handover (SDH) strategy and the joint decision handover (JDH) strategy are proposed, and they both incorporate the routing hops as a crucial handover criterion to minimize the end-to-end latency. In addition, the shortest route hop-count algorithm is designed to assist in the handover decision-making process. Simulations demonstrate that the proposed handover strategies outperform the traditional handover strategies in terms of the number of handovers and end-to-end latency.
Lightweight convolutional neural networks (CNNs) have simple structures but struggle to comprehensively and accurately extract important semantic information from images. While attention mechanisms can enhance CNNs by learning distinctive representations, most existing spatial and hybrid attention methods focus on local regions with extensive parameters, making them unsuitable for lightweight CNNs. In this paper, we propose a self-attention mechanism tailored for lightweight networks, namely the brief self-attention module (BSAM). BSAM consists of the brief spatial attention (BSA) and advanced channel attention blocks. Unlike conventional self-attention methods with many parameters, our BSA block improves the performance of lightweight networks by effectively learning global semantic representations. Moreover, BSAM can be seamlessly integrated into lightweight CNNs for end-to-end training, maintaining the network’s lightweight and mobile characteristics. We validate the effectiveness of the proposed method on image classification tasks using the Food-101, Caltech-256, and Mini-ImageNet datasets.
The power inversion (PI) algorithm lacks specific constraints on desired signals. Thus, the beampattern has fluctuation in all directions other than the jamming sources. This phenomenon will damage the reception of desired signals. In high signal-to-noise ratio (SNR) application, the desired signal is inevitably suppressed by the PI algorithm, resulting in a deterioration to the out signal-to-interference-and-noise ratio (SINR). This paper proposes an improved PI algorithm based on derivative constraint. Firstly, the proposed method uses subspace projection to extract jamming-free data, the derivative constraint is imposed to the non-jamming data, and subsequently the Lagrange multiplier can be used to calculate the array weight vector. Simulation results demonstrate that, the proposed algorithm in this paper has a higher output SNR, flat gains in non-jamming directions, and applicability of high SINR than the PI algorithm, thus verifying the effectiveness of the algorithm.
Automatic modulation classification(AMC) is an essential technique in both civil and military applications. While deep learning has surpassed traditional methods in accuracy, distinguishing high-order modulations remain challenging. Current efforts prioritize complex network designs, neglecting the integration of deep features and tailored feature engineering to reslove high-order ambiguities. Therefore, a multi-feature extraction framework is proposed, which directly concatenates the deep feature extracted by a newly designed lightweight neural network and the proposed spectrum secondary features or de-noised high-order statistical features. The proposed features and lightweight network both demonstrate superior overall accuracy than other competing features or networks. Furthermore, the effectiveness of the feature extraction framework is also validated. The average classification accuracy on high-order modulation sets reaches 67.39% on the RadioML2018.01A dataset, increasing more than 2% compared with the other competitive networks under the framework. The results indicate the effectiveness of the proposed feature extraction framework for its representational ability by combing the deep features with the proposed domain features.
This paper introduces a hybrid configuration design to enhance the precision of satellite antenna position measurement. By fixing the circular array antenna on the antenna mounting surface and integrating coordinate system transformation relationships with interferometric direction finding (DF) and positioning technology, accurate estimation of the antenna position is ensured. This method optimizes the quality and stability of data fusion by integrating pulse parameter characteristics, satellite orbit and attitude information, as well as the field of view information from observation stations, using techniques such as maximum-ratio-combining (MRC) and orbit extrapolation. Specifically, the sampling-importance resampling particle-filtering and Kalman-filtering (SIR-PF-KF) hybrid filtering prediction technology is employed to precisely predict and correct the three-dimensional (3D) position errors of the L-array antenna. Through data processing of five to nine orbits, accurate estimation of the antenna’s 3D position is achieved, achieving an estimation accuracy of 3 μm, significantly improving the accuracy of on-orbit rapid calibration. Experimental results show that the interferometer positioning accuracy is improved from 7.9 km before antenna position correction to within 0.2 km after correction, verifying the effectiveness and practicability of this method, which aims to address issues with positioning accuracy.
The Global Position System (GPS) is a reliable method for positioning in most scenarios, but it falls short in harsh environments like urban vehicular scenarios, where numerous trees or flyovers obstruct the signals. This presents an unprecedented challenge for autonomous vehicles or applications requiring high accuracy. Fortunately, vehicular ad-hoc networks (VANET) offer an effective solution, where vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications are used to enhance location awareness. In V2I communications, the roadside units (RSU) transmit beacon packets, and the vehicle receives numerous packets from different RSUs to establish communication. To further improve localization accuracy, a cross-covariance matrices-alternating least square (CCM-ALS) algorithm is proposed. The algorithm relies on ALS of the CCM for obtaining the position of vehicles in V2I communications. The algorithm is highly precise compared to traditional angle of arrival (AOA) positioning and not inferior to direct position determination (DPD) approaches while being low in complexity, which is crucial for moving vehicles. The numerical results verify the superiority of the proposed method.
During actual high-speed flights, the electromagnetic (EM) properties of aircraft radomes are influenced by dielectric temperature drift, leading to substantial drift in the boresight errors (BSEs) from their room temperature values. However, applying thermal loads to the radome during ground-based EM simulation tests is challenging. This paper presents an EM equivalent physical model (EEPM) for high-speed aircraft radomes that account for the effects of dielectric temperature drift. This is achieved by attaching dielectric slices of specific thicknesses to the outer surface of a room-temperature radome (RTR) to simulate the increase in electrical thickness resulting from high temperatures. This approach enables accurate simulations of the BSEs of high-temperature radomes (HTRs) under high-speed flight conditions. An application example, supported by full-wave numerical calculations and physical testing, demonstrates that the EEPM exhibits substantial improvement in approximating the HTR compared to the RTR, facilitating precise simulations of the BSEs of HTRs during high-speed flights. Overall, the proposed EEPM is anticipated to considerably enhance the alignment between the ground-based simulations of high-speed aircraft guidance systems and their actual flight conditions.
Thinning of antenna arrays has been a popular topic for the last several decades. With increasing computational power, this optimization task acquired a new hue. This paper suggests a genetic algorithm as an instrument for antenna array thinning. The algorithm with a deliberately chosen fitness function allows synthesizing thinned linear antenna arrays with low peak sidelobe level (SLL) while maintaining the half-power beamwidth (HPBW) of a full linear antenna array. Based on results from existing papers in the field and known approaches to antenna array thinning, a classification of thinning types is introduced. The optimal thinning type for a linear thinned antenna array is determined on the basis of a maximum attainable SLL. The effect of thinning coefficient on main directional pattern characteristics, such as peak SLL and HPBW, is discussed for a number of amplitude distributions.
