The acquisition, analysis, and prediction of the radar cross section (RCS) of a target have extremely important strategic significance in the military. However, the RCS values at all azimuths are hardly accessible for non-cooperative targets, due to the limitations of radar observation azimuth and detection resources. Despite their efforts to predict the azimuth-dimensional RCS value, traditional methods based on statistical theory fails to achieve the desired results because of the azimuth sensitivity of the target RCS. To address this problem, an improved neural basis expansion analysis for interpretable time series forecasting (N-BEATS) network considering the physical model prior is proposed to predict the azimuth-dimensional RCS value accurately. Concretely, physical model-based constraints are imposed on the network by constructing a scattering-center module based on the target scattering-center model. Besides, a superimposed seasonality module is involved to better capture high-frequency information, and augmenting the training set provides complementary information for learning predictions. Extensive simulations and experimental results are provided to validate the effectiveness of the proposed method.
In this paper, a feature selection method for determining input parameters in antenna modeling is proposed. In antenna modeling, the input feature of artificial neural network (ANN) is geometric parameters. The selection criteria contain correlation and sensitivity between the geometric parameter and the electromagnetic (EM) response. Maximal information coefficient (MIC), an exploratory data mining tool, is introduced to evaluate both linear and nonlinear correlations. The EM response range is utilized to evaluate the sensitivity. The wide response range corresponding to varying values of a parameter implies the parameter is highly sensitive and the narrow response range suggests the parameter is insensitive. Only the parameter which is highly correlative and sensitive is selected as the input of ANN, and the sampling space of the model is highly reduced. The modeling of a wideband and circularly polarized antenna is studied as an example to verify the effectiveness of the proposed method. The number of input parameters decreases from 8 to 4. The testing errors of |S11| and axis ratio are reduced by 8.74% and 8.95%, respectively, compared with the ANN with no feature selection.
Low-frequency signals have been proven valuable in the fields of target detection and geological exploration. Nevertheless, the practical implementation of these signals is hindered by large antenna diameters, limiting their potential applications. Therefore, it is imperative to study the creation of low-frequency signals using antennas with suitable dimensions. In contrast to conventional mechanical antenna techniques, our study generates low-frequency signals in the spatial domain utilizing the principle of the Doppler effect. We also defines the antenna array architecture, the timing sequency, and the radiating element signal waveform, and provides experimental prototypes including 8/64 antennas based on earlier research. In the conducted experiments, 121 MHz, 40 MHz, and 10 kHz composite signals are generated by 156 MHz radiating element signals. The composite signal spectrum matches the simulations, proving our low-frequency signal generating method works. This holds significant implications for research on generating low-frequency signals with small-sized antennas.
Low Earth orbit (LEO) satellite networks exhibit distinct characteristics, e.g., limited resources of individual satellite nodes and dynamic network topology, which have brought many challenges for routing algorithms. To satisfy quality of service (QoS) requirements of various users, it is critical to research efficient routing strategies to fully utilize satellite resources. This paper proposes a multi-QoS information optimized routing algorithm based on reinforcement learning for LEO satellite networks, which guarantees high level assurance demand services to be prioritized under limited satellite resources while considering the load balancing performance of the satellite networks for low level assurance demand services to ensure the full and effective utilization of satellite resources. An auxiliary path search algorithm is proposed to accelerate the convergence of satellite routing algorithm. Simulation results show that the generated routing strategy can timely process and fully meet the QoS demands of high assurance services while effectively improving the load balancing performance of the link.
Piezo actuators are widely used in ultra-precision fields because of their high response and nano-scale step length. However, their hysteresis characteristics seriously affect the accuracy and stability of piezo actuators. Existing methods for fitting hysteresis loops include operator class, differential equation class, and machine learning class. The modeling cost of operator class and differential equation class methods is high, the model complexity is high, and the process of machine learning, such as neural network calculation, is opaque. The physical model framework cannot be directly extracted. Therefore, the sparse identification of nonlinear dynamics (SINDy) algorithm is proposed to fit hysteresis loops. Furthermore, the SINDy algorithm is improved. While the SINDy algorithm builds an orthogonal candidate database for modeling, the sparse regression model is simplified, and the Relay operator is introduced for piecewise fitting to solve the distortion problem of the SINDy algorithm fitting singularities. The Relay-SINDy algorithm proposed in this paper is applied to fitting hysteresis loops. Good performance is obtained with the experimental results of open and closed loops. Compared with the existing methods, the modeling cost and model complexity are reduced, and the modeling accuracy of the hysteresis loop is improved.
Anti-jamming performance evaluation has recently received significant attention. For Link-16, the anti-jamming performance evaluation and selection of the optimal anti-jamming technologies are urgent problems to be solved. A comprehensive evaluation method is proposed, which combines grey relational analysis (GRA) and cloud model, to evaluate the anti-jamming performances of Link-16. Firstly, on the basis of establishing the anti-jamming performance evaluation indicator system of Link-16, the linear combination of analytic hierarchy process (AHP) and entropy weight method (EWM) are used to calculate the combined weight. Secondly, the qualitative and quantitative concept transformation model, i.e., the cloud model, is introduced to evaluate the anti-jamming abilities of Link-16 under each jamming scheme. In addition, GRA calculates the correlation degree between evaluation indicators and the anti-jamming performance of Link-16, and assesses the best anti-jamming technology. Finally, simulation results prove that the proposed evaluation model can achieve the objective of feasible and practical evaluation, which opens up a novel way for the research of anti-jamming performance evaluations of Link-16.
Separated transmit and receive antennas are employed to improve transmit-receive isolation in conventional short-range radars, which greatly increases the antenna size and misaligns of the transmit/receive radiation patterns. In this paper, a dual circularly polarized (CP) monostatic simultaneous transmit and receive (MSTAR) antenna with enhanced isolation is proposed to alleviate the problem. The proposed antenna consists of one sequentially rotating array (SRA), two beamforming networks (BFN), and a combined decoupling structure. The SRA is shared by the transmit and receive to reduce the size of the antenna and to obtain a consistent transmit and receive pattern. The BFN achieve right-hand CP for transmit and left-hand CP for receive. By exploring the combined decoupling structure of uniplanar compact electromagnetic band gap (UC-EBG) and ring-shaped defected ground structure (RS-DGS), good transmit-receive isolation is achieved. The proposed antenna prototype is fabricated and experimentally characterized. The simulated and measured results show good agreement. The demonstrate transmit/receive isolation is height than 33 dB, voltage standing wave ratio is lower than 2, axial ratio is lower than 3 dB, and consistent radiation for both transmit and receive is within 4.25?4.35 GHz.
Deep learning has achieved excellent results in various tasks in the field of computer vision, especially in fine-grained visual categorization. It aims to distinguish the subordinate categories of the label-level categories. Due to high intra-class variances and high inter-class similarity, the fine-grained visual categorization is extremely challenging. This paper first briefly introduces and analyzes the related public datasets. After that, some of the latest methods are reviewed. Based on the feature types, the feature processing methods, and the overall structure used in the model, we divide them into three types of methods: methods based on general convolutional neural network (CNN) and strong supervision of parts, methods based on single feature processing, and methods based on multiple feature processing. Most methods of the first type have a relatively simple structure, which is the result of the initial research. The methods of the other two types include models that have special structures and training processes, which are helpful to obtain discriminative features. We conduct a specific analysis on several methods with high accuracy on public datasets. In addition, we support that the focus of the future research is to solve the demand of existing methods for the large amount of the data and the computing power. In terms of technology, the extraction of the subtle feature information with the burgeoning vision transformer (ViT) network is also an important research direction.
In challenging situations, such as low illumination, rain, and background clutter, the stability of the thermal infrared (TIR) spectrum can help red, green, blue (RGB) visible spectrum to improve tracking performance. However, the high-level image information and the modality-specific features have not been sufficiently studied. The proposed correlation filter uses the fused saliency content map to improve filter training and extracts different features of modalities. The fused content map is introduced into the spatial regularization term of correlation filter to highlight the training samples in the content region. Furthermore, the fused content map can avoid the incompleteness of the content region caused by challenging situations. Additionally, different features are extracted according to the modality characteristics and are fused by the designed response-level fusion strategy. The alternating direction method of multipliers (ADMM) algorithm is used to solve the tracker training efficiently. Experiments on the large-scale benchmark datasets show the effectiveness of the proposed tracker compared to the state-of-the-art traditional trackers and the deep learning based trackers.
The direction of ground-based interference reaching the satellite is generally very close to the spot beam of the satellite. The traditional array anti-jamming method may cause significant loss to the uplink signal while suppressing the interference. In this paper, an aperiodic multistage array is used, and a sub-array aperiodic distribution optimization scheme based on parallel differential evolution is proposed, which effectively improves the beam resolution and suppresses the grating lobe. On this basis, a two-stage signal processing method is used to suppress interference. Finally, the comprehensive performance of the proposed scheme is evaluated and verified.
This paper considers an intelligent reflecting surface (IRS)-assisted multiple-input multiple-output (MIMO) system. To maximize the average achievable rate (AAR) under outdated channel state information (CSI), we propose a twin-timescale passive beamforming (PBF) and power allocation protocol which can reduce the IRS configuration and training overhead. Specifically, the short-timescale power allocation is designed with the outdated precoder and fixed PBF. A new particle swarm optimization (PSO)-based long-timescale PBF optimization is proposed, where mini-batch channel samples are utilized to update the fitness function. Finally, simulation results demonstrate the effectiveness of the proposed method.
Multi-objective optimization (MOO) for the microwave metamaterial absorber (MMA) normally adopts evolutionary algorithms, and these optimization algorithms require many objective function evaluations. To remedy this issue, a surrogate-based MOO algorithm is proposed in this paper where Kriging models are employed to approximate objective functions. An efficient sampling strategy is presented to sequentially capture promising samples in the design region for exact evaluations. Firstly, new sample points are generated by the MOO on surrogate models. Then, new samples are captured by exploiting each objective function. Furthermore, a weighted sum of the improvement of hypervolume (IHV) and the distance to sampled points is calculated to select the new sample. Compared with two well-known MOO algorithms, the proposed algorithm is validated by benchmark problems. In addition, two broadband MMAs are applied to verify the feasibility and efficiency of the proposed algorithm.
During high-speed flight, both thermal and mechanical loads can degrade the electrical performance of the antenna-radome system, which can subsequently affect the performance of the guidance system. This paper presents a method for evaluating the electrical performance of the radome when subjected to thermo-mechanical-electrical (TME) coupling. The method involves establishing a TME coupling model (TME-CM) based on the TME sharing mesh model (TME-SMM) generated by the tetrahedral mesh partitioning of the radome structure. The effects of dielectric temperature drift and structural deformation on the radome’s electrical performance are also considered. Firstly, the temperature field of the radome is obtained by transient thermal analysis while the deformation field of the radome is obtained by static analysis. Subsequently, the dielectric variation and structural deformation of the radome are accurately incorporated into the electrical simulation model based on the TME-SMM. The three-dimensional (3D) ray tracing method with the aperture integration technique is used to calculate the radome’s electrical performance. A representative example is provided to illustrate the superiority and necessity of the proposed method. This is achieved by calculating and analyzing the changes in the radome’s electrical performance over time during high-speed flight.
The parametric scattering center model of radar target has the advantages of simplicity, sparsity and mechanism relevant, making it widely applied in fields such as radar data compression and rapid generation, radar imaging, feature extraction and recognition. This paper summarizes and analyzes the research situation, development trend, and difficult problems on scattering center (SC) parametric modeling from three aspects: parametric representation, determination method of model parameters, and application.
Unmanned aerial vehicles (UAVs) may be subjected to unintentional radio frequency interference (RFI) or hostile jamming attack which will lead to fail to track global navigation satellite system (GNSS) signals. Therefore, the simultaneous realization of anti-jamming and high-precision carrier phase difference positioning becomes a dilemmatic problem. In this paper, a distortionless phase digital beamforming (DBF) algorithm with self-calibration antenna arrays is proposed, which enables to obtain distortionless carrier phase while suppressing jamming. Additionally, architecture of high precision Beidou receiver based on anti-jamming antenna arrays is proposed. Finally, the performance of the algorithm is evaluated, including antenna calibration accuracy, carrier phase distortionless accuracy, and carrier phase measurement accuracy without jamming. Meanwhile, the maximal jamming to signal ratio (JSR) and real time kinematic (RTK) positioning accuracy under wideband jamming are also investigated. The experimental results based on the real-life Beidou signals show that the proposed method has an excellent performance for precise relative positioning under jamming when compared with other anti-jamming methods.
A novel variable step-size modified super-exponential iteration (MSEI) decision feedback blind equalization (DFE) algorithm with second-order digital phase-locked loop is put forward to improve the convergence performance of super-exponential iteration DFE algorithm. Based on the MSEI-DFE algorithm, it is first proposed to develop an error function as an improvement to the error function of MSEI, which effectively achieves faster convergence speed of the algorithm. Subsequently, a hyperbolic tangent function variable step-size algorithm is developed considering the high variation rate of the hyperbolic tangent function around zero, so as to further improve the convergence speed of the algorithm. In the end, a second-order digital phase-locked loop is introduced into the decision feedback equalizer to track and compensate for the phase rotation of equalizer input signals. For the multipath underwater acoustic channel with mixed phase and phase rotation, quadrature phase shift keying (QPSK) and 16 quadrature amplitude modulation (16QAM) modulated signals are used in the computer simulation of the algorithm in terms of convergence and carrier recovery performance. The results show that the proposed algorithm can considerably improve convergence speed and steady-state error, make effective compensation for phase rotation, and efficiently facilitate carrier recovery.
A generalized labeled multi-Bernoulli (GLMB) filter with motion mode label based on the track-before-detect (TBD) strategy for maneuvering targets in sea clutter with heavy tail, in which the transitions of the mode of target motions are modeled by using jump Markovian system (JMS), is presented in this paper. The close-form solution is derived for sequential Monte Carlo implementation of the GLMB filter based on the TBD model. In update, we derive a tractable GLMB density, which preserves the cardinality distribution and first-order moment of the labeled multi-target distribution of interest as well as minimizes the Kullback-Leibler divergence (KLD), to enable the next recursive cycle. The relevant simulation results prove that the proposed multiple-model GLMB-TBD (MM-GLMB-TBD) algorithm based on K-distributed clutter model can improve the detecting and tracking performance in both estimation error and robustness compared with state-of-the-art algorithms for sea clutter background. Additionally, the simulations show that the proposed MM-GLMB-TBD algorithm can accurately output the multitarget trajectories with considerably less computational complexity compared with the adapted dynamic programming based TBD (DP-TBD) algorithm. Meanwhile, the simulation results also indicate that the proposed MM-GLMB-TBD filter slightly outperforms the JMS particle filter based TBD (JMS-MeMBer-TBD) filter in estimation error with the basically same computational cost. Finally, the impact of the mismatches on the clutter model and clutter parameter is investigated for the performance of the MM-GLMB-TBD filter.
To reduce the negative impact of the power amplifier (PA) nonlinear distortion caused by the orthogonal frequency division multiplexing (OFDM) waveform with high peak-to-average power ratio (PAPR) in integrated radar and communication (RadCom) systems is studied, the channel estimation in passive sensing scenarios. Adaptive channel estimation methods are proposed based on different pilot patterns, considering nonlinear distortion and channel sparsity. The proposed methods achieve sparse channel results by manipulating the least squares (LS) frequency-domain channel estimation results to preserve the most significant taps. The decision-aided method is used to optimize the sparse channel results to reduce the effect of nonlinear distortion. Numerical results show that the channel estimation performance of the proposed methods is better than that of the conventional methods under different pilot patterns. In addition, the bit error rate performance in communication and passive radar detection performance show that the proposed methods have good comprehensive performance.
To analyze the influence of time synchronization error, phase synchronization error, frequency synchronization error, internal delay of the transceiver system, and range error and angle error between the unit radars on the target detection performance, firstly, a spatial detection model of distributed high-frequency surface wave radar (distributed-HFSWR) is established in this paper. In this model, a method for accurate extraction of direct wave spectrum based on curve fitting is proposed to obtain accurate system internal delay and frequency synchronization error under complex electromagnetic environment background and low signal to noise ratio (SNR), and to compensate for the shift of range and Doppler frequency caused by time-frequency synchronization error. The direct wave component is extracted from the spectrum, the range estimation error and Doppler estimation error are reduced by the method of curve fitting, and the fitting accuracy of the parameters is improved. Then, the influence of frequency synchronization error on target range and radial Doppler velocity is quantitatively analyzed. The relationship between frequency synchronization error and radial Doppler velocity shift and range shift is given. Finally, the system synchronization parameters of the trial distributed-HFSWR are obtained by the proposed spectrum extraction method based on curve fitting, the experimental data is compensated to correct the shift of the target, and finally the correct target parameter information is obtained. Simulations and experimental results demonstrate the superiority and correctness of the proposed method, theoretical derivation and detection model proposed in this paper.
Imaging detection is an important means to obtain target information. The traditional imaging detection technology mainly collects the intensity information and spectral information of the target to realize the classification of the target. In practical applications, due to the mixed scenario, it is difficult to meet the needs of target recognition. Compared with intensity detection, the method of polarization detection can effectively enhance the accuracy of ground object target recognition (such as the camouflage target). In this paper, the reflection mechanism of the target surface is studied from the microscopic point of view, and the polarization characteristic model is established to express the relationship between the polarization state of the reflected signal and the target surface parameters. The polarization characteristic test experiment is carried out, and the target surface parameters are retrieved using the experimental data. The results show that the degree of polarization (DOP) is closely related to the detection zenith angle and azimuth angle. The (DOP) of the target is the smallest in the direction of light source incidence and the largest in the direction of specular reflection. Different materials have different polarization characteristics. By comparing their DOP, target classification can be achieved.
Code acquisition is the kernel operation for signal synchronization in the spread-spectrum receiver. To reduce the computational complexity and latency of code acquisition, this paper proposes an efficient scheme employing sparse Fourier transform (SFT) and the relevant hardware architecture for field programmable gate array (FPGA) and application-specific integrated circuit (ASIC) implementation. Efforts are made at both the algorithmic level and the implementation level to enable merged searching of code phase and Doppler frequency without incurring massive hardware expenditure. Compared with the existing code acquisition approaches, it is shown from theoretical analysis and experimental results that the proposed design can shorten processing latency and reduce hardware complexity without degrading the acquisition probability.
The perception module of advanced driver assistance systems plays a vital role. Perception schemes often use a single sensor for data processing and environmental perception or adopt the information processing results of various sensors for the fusion of the detection layer. This paper proposes a multi-scale and multi-sensor data fusion strategy in the front end of perception and accomplishes a multi-sensor function disparity map generation scheme. A binocular stereo vision sensor composed of two cameras and a light deterction and ranging (LiDAR) sensor is used to jointly perceive the environment, and a multi-scale fusion scheme is employed to improve the accuracy of the disparity map. This solution not only has the advantages of dense perception of binocular stereo vision sensors but also considers the perception accuracy of LiDAR sensors. Experiments demonstrate that the multi-scale multi-sensor scheme proposed in this paper significantly improves disparity map estimation.
Aiming at evaluating and predicting rapidly and accurately a high sensitivity receiver’s adaptability in complex electromagnetic environments, a novel testing and prediction method based on dual-channel multi-frequency is proposed to improve the traditional two-tone test. Firstly, two signal generators are used to generate signals at the radio frequency (RF) by frequency scanning, and then a rapid measurement at the intermediate frequency (IF) output port is carried out to obtain a huge amount of sample data for the subsequent analysis. Secondly, the IF output response data are modeled and analyzed to construct the linear and nonlinear response constraint equations in the frequency domain and prediction models in the power domain, which provide the theoretical criteria for interpreting and predicting electromagnetic susceptibility (EMS) of the receiver. An experiment performed on a radar receiver confirms the reliability of the method proposed in this paper. It shows that the interference of each harmonic frequency and each order to the receiver can be identified and predicted with the sensitivity model. Based on this, fast and comprehensive evaluation and prediction of the receiver’s EMS in complex environment can be efficiently realized.
In this paper, an efficient unequal error protection (UEP) scheme for online fountain codes is proposed. In the build-up phase, the traversing-selection strategy is proposed to select the most important symbols (MIS). Then, in the completion phase, the weighted-selection strategy is applied to provide low overhead. The performance of the proposed scheme is analyzed and compared with the existing UEP online fountain scheme. Simulation results show that in terms of MIS and the least important symbols (LIS), when the bit error ratio is $ {10^{ - 4}} $, the proposed scheme can achieve $ 85{\text{% }} $ and $ 31.58{\text{% }} $ overhead reduction, respectively.
Acoustic source localization (ASL) and sound event detection (SED) are two widely pursued independent research fields. In recent years, in order to achieve a more complete spatial and temporal representation of sound field, sound event localization and detection (SELD) has become a very active research topic. This paper presents a deep learning-based multi-overlapping sound event localization and detection algorithm in three-dimensional space. Log-Mel spectrum and generalized cross-correlation spectrum are joined together in channel dimension as input features. These features are classified and regressed in parallel after training by a neural network to obtain sound recognition and localization results respectively. The channel attention mechanism is also introduced in the network to selectively enhance the features containing essential information and suppress the useless features. Finally, a thourough comparison confirms the efficiency and effectiveness of the proposed SELD algorithm. Field experiments show that the proposed algorithm is robust to reverberation and environment and can achieve higher recognition and localization accuracy compared with the baseline method.
In order to solve the problem that the performance of traditional localization methods for mixed near-field sources (NFSs) and far-field sources (FFSs) degrades under impulsive noise, a robust and novel localization method is proposed. After eliminating the impacts of impulsive noise by the weighted outlier filter, the direction of arrivals (DOAs) of FFSs can be estimated by multiple signal classification (MUSIC) spectral peaks search. Based on the DOAs information of FFSs, the separation of mixed sources can be performed. Finally, the estimation of localizing parameters of NFSs can avoid two-dimension spectral peaks search by decomposing steering vectors. The Cramer-Rao bounds (CRB) for the unbiased estimations of DOA and range under impulsive noise have been drawn. Simulation experiments verify that the proposed method has advantages in probability of successful estimation (PSE) and root mean square error (RMSE) compared with existing localization methods. It can be concluded that the proposed method is effective and reliable in the environment with low generalized signal to noise ratio (GSNR), few snapshots, and strong impulse.
In electromagnetic countermeasures circumstances, synthetic aperture radar (SAR) imagery usually suffers from severe quality degradation from modulated interrupt sampling repeater jamming (MISRJ), which usually owes considerable coherence with the SAR transmission waveform together with periodical modulation patterns. This paper develops an MISRJ suppression algorithm for SAR imagery with online dictionary learning. In the algorithm, the jamming modulation temporal properties are exploited with extracting and sorting MISRJ slices using fast-time autocorrelation. Online dictionary learning is followed to separate real signals from jamming slices. Under the learned representation, time-varying MISRJs are suppressed effectively. Both simulated and real-measured SAR data are also used to confirm advantages in suppressing time-varying MISRJs over traditional methods.
Large calculation error can be formed by directly employing the conventional Yee’s grid to curve surfaces. In order to alleviate such condition, unconditionally stable Crank-Nicolson Douglas-Gunn (CNDG) algorithm with is proposed for rotationally symmetric multi-scale problems in anisotropic magnetized plasma. Within the CNDG algorithm, an alternative scheme for the simulation of anisotropic plasma is proposed in body-of-revolution domains. Convolutional perfectly matched layer (CPML) formulation is proposed to efficiently solve the open region problems. Numerical example is carried out for the illustration of effectiveness including the efficiency, resources, and absorption. Through the results, it can be concluded that the proposed scheme shows considerable performance during the simulation.
For time-of-flight (TOF) light detection and ranging (LiDAR), a three-channel high-performance transimpedance amplifier (TIA) with high immunity to input load capacitance is presented. A regulated cascade (RGC) as the input stage is at the core of the complementary metal oxide semiconductor (CMOS) circuit chip, giving it more immunity to input photodiode detectors. A simple smart output interface acting as a feedback structure, which is rarely found in other designs, reduces the chip size and power consumption simultaneously. The circuit is designed using a 0.5 μm CMOS process technology to achieve low cost. The device delivers a 33.87 dB? transimpedance gain at 350 MHz. With a higher input load capacitance, it shows a ?3 dB bandwidth of 461 MHz, indicating a better detector tolerance at the front end of the system. Under a 3.3 V supply voltage, the device consumes 5.2 mW, and the total chip area with three channels is 402.8×597.0 μm2 (including the test pads).
Non-uniform linear array (NULA) configurations are well renowned due to their structural ability for providing increased degrees of freedom (DOF) and wider array aperture than uniform linear arrays (ULAs). These characteristics play a significant role in improving the direction-of-arrival (DOA) estimation accuracy. However, most of the existing NULA geometries are primarily applicable to circular sources (CSs), while they limitedly improve the DOF and continuous virtual aperture for non-circular sources (NCSs). Toward this purpose, we present a triad-displaced ULAs (Tdis-ULAs) configuration for NCS. The Tdis-ULAs structure generally consists of three ULAs, which are appropriately placed. The proposed antenna array approach fully exploits the non-circular characteristics of the sources. Given the same number of elements, the Tdis-ULAs design achieves more DOF and larger hole-free co-array aperture than its sparse array competitors. Advantageously, the number of uniform DOF, optimal distribution of elements among the ULAs, and precise element positions are uniquely determined by the closed-form expressions. Moreover, the proposed array also produces a filled resulting co-array. Numerical simulations are conducted to show the performance advantages of the proposed Tdis-ULAs configuration over its counterpart designs.
