Far-field Radar Cross Section Research Articles

Sulfidation and reassembly strategy enables yolk-shell metal sulfide/alloy nanoparticles @carbon with efficient electromagnetic wave absorption

Constructing hybrid metal-organic frameworks (MOFs) demonstrates significant potential to improve electromagnetic wave (EMW) absorption performance by leveraging synergistic effects between composition and structure, addressing challenges in tuning electromagnetic parameters of single-composition MOFs. This study proposes a sophisticated strategy involving sulfidation-interface growth-reassembly: sulfide nanoparticles form by sulfidating the core (CoFe-MIL-88A), and then ZIF-67 guests grow and reassemble on the sulfided host MOF, resulting in a layered EMW absorption material. The composite material integrates synergistic effects, which include high conductive loss and defect/interface/dipole polarization induced by N and S atom doping. The CFSNC composite material outperforms previously published values, achieving a minimum reflection loss (RLmin) of −65.96 dB and an effective absorption bandwidth (EAB) of 6.15 GHz with a thickness of merely 2 mm. Simulation calculations of the real far-field radar cross-section (RCS) further validate its practical application potential. This work introduces novel ideas and methods for designing and preparing high-performance EMW absorption materials, offering both theoretical insights and practical applications.

Surface morphology of magnetic carbon foam regulated electromagnetic properties for microwave absorption

Electromagnetic pollution resulting from the electronic communication can be mitigated by using microwave absorbers. The magnetic N-doped carbon foams with diverse surface morphologies as absorbers were synthesized via alkalization treatment, hydrothermal reaction, and carbothermic reduction reaction. The microstructure, electromagnetic parameters, and microwave absorption properties can be tailored by adjusting thermal treatment temperatures. The results indicated that the composites exhibit exceptional microwave absorption properties, with a minimum reflection loss of −44.15 dB at 11.18 GHz and an effective absorption bandwidth of 5.3 GHz at a thickness of 1.53 mm. The exceptional microwave absorption properties can be attributed to the synergistic effect of superior impedance matching, robust dielectric loss, and magnetic loss. The far-field radar cross section (RCS) is also reduced, achieving a maximum RCS reduction value of 14.6 dBm2. The present study presents a novel concept for the fabrication of lightweight and broadband microwave absorbing materials.

A Linear-Scan-Based Wideband Single-Cut Near-Field RCS Measurement Technique With Wide Effective Angle Coverage

A linear-scan-based wideband near-field radar cross section (RCS) measurement technique for achieving wide effective-angle-coverage is proposed in this letter. Both the data preprocessing and linear near-field to far-field transformation (LNF2FFT) techniques are used to ensure the accurate measurement of wideband far-field RCS in a wide azimuthal angle range. With the linear-scan-based near-field sampling strategy, the object under test (OUT) keeps stationary during the measurement process, and the equipment of turntable is not required. Moreover, based on the 2D plane wave expansion, LNF2FFT is proposed to translate the linearly sampled near-field data into far-field RCS pattern. An adaptive time gating strategy is also proposed to deal with the varied background property in liner samplings. Serval important practical factors relating to the measurement accuracy, such as phase center calibration, background subtraction and calibration, are also discussed in detail. Two illustrative test examples are presented, in which RCS measurement with wide effective-angle-coverage of ±35° and the average wideband measurement errors less than 0.4 dB are achieved in 1 m limited measurement range.

Near-Field-to-Far-Field RCS Prediction Using Only Amplitude Estimation Technique Based on State Space Method

Measuring the radar cross-section (RCS) of a far-field (FF) target in engineering can be challenging, especially when remote measurement is difficult. To overcome this challenge, an FF RCS can be predicted by near-field (NF)-extrapolated transformation. However, due to the relative error between the theoretical and measured electric field (E-field) values in a NF, the extrapolation calculation of a FF can be carried out by correcting the NF amplitude. This paper proposes the use of the state space method (SSM) to estimate the amplitude-only of NF E-fields for improving the prediction accuracy of FFs. The simulation results demonstrate that the SSM can estimate NF amplitude, which can be transformed into a FF, and which can lead to improved prediction accuracy when compared to reference-FF-calculated and to circular-NF-to-FF-transform-(CNFFFT)-calculated RCSs.

Near-to-Far Field RCS Calculation Using Correction Optimization Technique

Radar cross section (RCS) is a scattering measure of an object that scatters to the radar. However, existing methods for near-field (NF) measurement and data processing rarely extract amplitude characteristics, and there is a lack of effective verification of far-field (FF) data in the process of NF to FF transformation, which leads to inaccuracies in FF prediction accuracy. In this paper, we propose a method to establish the relationship between the NF and FF RCS using the state space method (SSM), which is based on accurate estimation of the NF amplitude in NF measurement, and then deriving the FF RCS from the NF scattering signal convolved with a near-to-far kernel. The proposed solution to address the uncertainty issue in reference FF data involves using the geometric theory of diffraction (GTD) scattering center model as the reference FF data and establishing a linear equation with the derived FF model. The negative gradient search (NGS) system identification concept is used to optimize the FF model in order to reduce the discrepancy between the reference and derived values. Finally, the corrected RCS error is provided as additional proof of the effectiveness of these techniques in enhancing near-to-far transformation accuracy by examining the outcomes of three experiments.

Wideband Near-Field RCS Measurement Techniques With Improved Far-Field RCS Prediction Accuracies

Wideband near-field (NF) radar cross section (RCS) measurement techniques for improving the far-field RCS prediction accuracies are addressed in this article. Both the data preprocessing and NF-to-far-field transformation (NF2FFT) techniques are used to accurately measure wideband far-field RCSs in a wide azimuthal angle range. By utilizing the calibration set creation and some wideband data preprocessing techniques, including the vector background subtraction, the time-domain calibration and compensation, the time-gating steps, as well as the cylindrical mode expansion or the imaging-based NF2FFT techniques, the accurate wideband far-field RCSs in a wide azimuthal angle range can be obtained with the limited NF measurement range. Both the poor measurement accuracy and dynamic range as well as the main lobe distortion and angular undersampling effects existing in the NF measured raw data can be improved significantly. The proposed techniques are validated with an 8–12-GHz measurement of a 0.5-m-length cone target (in a 3-m measurement range, compared with the at least 30-m far-field range requirement), and the obviously improved RCS patterns and less than 0.5-dB average wideband measurement errors are achieved.

Image-Based Radar Cross Section Synthesis for a Cluster of Multiple Static Targets

This study proposes a novel image-based method for radar cross-section (RCS) prediction of a cluster of multiple static targets by synthesizing replicas of an original radar image. In this approach, we first measured the near-field backscattering of a target and reconstructed a corresponding radar image. Subsequently, modified copies of this image with rotation, translation, and spatial filtering were generated according to the predefined desired arrangement, and they were coherently summed to create a single synthesized image in which all scattering contributions contained in the modified images were virtually included. Finally, the synthesized image was utilized to predict the far-field RCS of multiple targets based on the theory of image-based near-field-to-far-field transformation (NFFFT). By employing the proposed algorithm, we can avoid building multiple test targets, resulting in reduced production costs. Moreover, we can easily test several different experimental layouts for multiple targets without repeating real measurements. Numerical simulations and experiments are conducted to demonstrate the validity of the proposed image-based RCS synthesis. The measurement results show that the proposed approach is valid when the multiple scattering between targets can be ignored, and the shadowing, which refers to the effect that a target is masked by another target, is insignificant.

Programmable metasurface RCS prediction under obstacles based on DNN

Programmable metasurfaces have attracted significant attention in various applications such as radar and 6G communications, owing to their ability freely shape the far-field pattern. However, complex calculations and simulations are always required when designing specific far-field patterns, especially when irregular obstacles are outside the metasurface. In this article, we propose a method using a four-layer artificial neural network to realize the far-field radar cross section (RCS) prediction of programmable metasurfaces in an environment with obstacles, and the prediction value agreed with the simulation data reasonably well. Results show that the proposed prediction model is characterized by better learning and generalization capacity. Our work has broad application prospects and value in complex environment signal transmission, metasurface inverse design, etc.

An Analysis on the Reduction of Measurement Time Using Interpolation Algorithm in Near-field RCS Measurements for Aircraft Shape

The importance of stealth technology is increasing in modern warfare, and Radar Cross Section(RCS) is widely used as an indicator of stealth technology. It is useful to measure RCS using an image-based near-field to far-field transformation algorithm in short-range monostatic conditions. However, the near-field measurement system requires a longer measurement time compared to other methods. In this work, it is proposed to reduce the measured data using an interpolation method in azimuth angular domain. The calculated far-field RCS values according to the sampling rate is shown, and the performance of the algorithm applied with interpolation in the angular domain is presented. It is shown that measurement samples can be reduced several times by using the redundancy in the angular domain while producing results similar to the conventional method.

Far-Field Radar Cross Section Determination From Near-Field 3-D Synthetic Aperture Imaging With Arbitrary Antenna-Scanning Surfaces

In this study, we propose a generalized algorithm for far-field radar cross section determination by using 3-D synthetic aperture imaging with arbitrary antenna-scanning surfaces. This method belongs to a class of techniques called image-based near-field-to-far-field transformation. The previous image-based approaches have been formulated based on a specific antenna-scanning trajectory or surface, such as a line, plane, circle, cylinder, and sphere; the majority of these approaches consider 2-D radar images to determine the azimuth radar cross section. We generalize the conventional image-based technique to accommodate an arbitrary antenna-scanning surface and consider a 3-D radar image for radar cross section prediction in both the azimuth and zenith directions. We validate the proposed algorithm by performing numerical simulations and anechoic chamber measurements.

On Design of a Triple Elliptical Super Wideband Antenna for 5G Applications

A novel compact triple elliptical monopole antenna has been designed, analyzed and fabricated for super wideband applications. To enhance the impedance bandwidth, the author proposed a monopole antenna with a triple elliptical patch excited by a 50Ω triangular tapered microstrip feedline. Further improvement is achieved by optimally modifying the Defected Ground Structure (DGS) of the antenna. The proposed patch structure is mounted on a Roger’s RT-Duroid 5880 substrate (ϵ_r= 2.2, tan δ = 0.0009) and has compact physical dimensions of 27 mm × 29.5 mm. The fabricated antenna covers the frequency band from 1.91 GHz to 43.5 GHz for S₁₁ ≤ -10 dB with a fractional bandwidth of >183.17% and has a ratio bandwidth of 22.77:1. This extended bandwidth coverage permits the antenna to operate for wide range of application including 5G and Internet of Things (IoT). The proposed antenna has a very Bandwidth Dimension Ratio (BDR) of 5761.87 which makes it the best candidate for superwideband antenna. Far field measurements disclose an omnidirectional radiation pattern across the frequency of operation. Further, the farfield Radar Cross Section (RCS) is analyzed through simulation and it is to be noted that relatively low values are obtained through out the frequency of operation. In addition, the compactness of the proposed antenna is theoretically verified with the aid of fundamental dimension limit theorem.

An RCS Measurement Method Using Sparse Imaging Based 3-D SAR Complex Image

Three-dimensional (3-D) synthetic aperture radar (SAR) near-field imaging provides a novel radar cross section (RCS) measurement method, which has been widely concerned in the microwave field. However, the accuracy of RCS measurement based on image is easily affected by sidelobes of matching filtering (MF) imaging and background clutter. Although the existing observation-matrix-based sparse SAR imaging algorithm can suppress sidelobes and clutter, it requires huge computational cost. To solve the above problems, an RCS measurement method using sparse imaging based 3-D SAR complex image is proposed. First, 3-D sparse imaging based complex image is used to suppress sidelobe and clutter of MF image. After that, the far-field RCS of the target is obtained by the compensation factor. Compared with the observation-matrix-based sparse SAR imaging algorithm, the proposed algorithm can reduce the computation time while retain the phase information of scene distribution. Compared with the RCS measurement based on MF image, the RCS measurement performance of the proposed method is improved. The simulation and real data of the system results validate the proposed method.

Position Error Detection and Compensation for Far-Field Radar Cross-Section Measurement

The calibration process is an essential kernel step in far-field radar cross-section measurement. Nevertheless, in the turntable test scenario, the calibrator cannot always be placed at the rotation center of the turntable. Because the geometric center and the rotation center of the upper surface of the turntable are not always coincident. To address this issue, we propose an error estimation method to predict and correct the calibrator position error caused by this issue; an error estimation method is proposed based on the vector rotation theory. Initially, the scattered power of the calibrator is recorded in the monostatic test mode. Then, the collected data are normalized and median filtered. Next, the position error vector is evaluated by determining the values of the radius and the polar angle of the calibrator in the experimental coordinate system. Simulation and experimental results show that the predicting accuracy with the proposed method is higher than 86%, which confirms the efficiency of this method.

Near-Field to Far-Field RCS Prediction on Arbitrary Scanning Surfaces Based on Spherical Wave Expansion.

Near-field to far-field transformation (NFFFT) is a frequently-used method in antenna and radar cross section (RCS) measurements for various applications. For weapon systems, most measurements are captured in the near-field area in an anechoic chamber, considering the security requirements for the design process and high spatial costs of far-field measurements. As the theoretical RCS value is the power ratio of the scattered wave to the incident wave in the far-field region, a scattered wave measured in the near-field region needs to be converted into field values in the far-field region. Therefore, this paper proposes a near-field to far-field transformation algorithm based on spherical wave expansion for application in near-field RCS measurement systems. If the distance and angular coordinates of each measurement point are known, the spherical wave functions in an orthogonal relationship can be calculated. If each weight is assumed to be unknown, a system of linear equations as numerous as the number of samples measured in the near electric field can be generated. In this system of linear equations, each weight value can be calculated using the iterative least squares QR-factorization method. Based on this theory, the validity of the proposed NFFFT is verified for several scatterer types, frequencies and measurement distances.

Radar Cross Section Near-Field to Far-Field Prediction for Isotropic-Point Scattering Target Based on Regression Estimation.

Radar cross section near-field to far-field transformation (NFFFT) is a well-established methodology. Due to the testing range constraints, the measured data are mostly near-field. Existing methods employ electromagnetic theory to transform near-field data into the far-field radar cross section, which is time-consuming in data processing. This paper proposes a flexible framework, named Neural Networks Near-Field to Far-Filed Transformation (NN-NFFFT). Unlike the conventional fixed-parameter model, the near-field RCS to far-field RCS transformation process is viewed as a nonlinear regression problem that can be solved by our fast and flexible neural network. The framework includes three stages: Near-Field and Far-field dataset generation, regression estimator training, and far-field data prediction. In our framework, the Radar cross section prior information is incorporated in the Near-Field and Far-field dataset generated by a group of point-scattering targets. A lightweight neural network is then used as a regression estimator to predict the far-field RCS from the near-field RCS observation. For the target with a small RCS, the proposed method also has less data acquisition time. Numerical examples and extensive experiments demonstrate that the proposed method can take less processing time to achieve comparable accuracy. Besides, the proposed framework can employ prior information about the real scenario to improve performance further.

A Near-Field to Far-Field RCS Measurement Method for Multiple-Scattering Target

It is challenging to perform the near-field to far-field transformation with a high precision for the multiple-scattering targets using the conventional monostatic radar cross section (RCS) measurement. In this paper, a novel near-field to far-field transformation method with bistatic RCS measurement is proposed. First, the RCS measurement is perceived as a double radiation model. Doing this, the relationship of near-field data and far-field data at different angles is derived, and the convolution function is given under the cylindrical wave. Second, the near-field scattering data are acquired using the proposed bistatic RCS measurement method that combines one transmission and multiple receptions with the rotation of a revolving table. The near-field data are then extrapolated twice with the convolution function, and the far-field RCS are obtained accordingly. Moreover, this paper also analyzes the truncation angular and sampling intervals for the measurement consideration. Finally, the effectiveness and the high precision of the proposed transformation approach for the multiple-scattering target are demonstrated using the numerical simulations and the experimental characterization with an example of dihedral structures.

Dynamically Tunable Scattering Manipulation of Dielectric and Conducting Cylinders Using Nanostructured Graphene Metasurfaces

Nanostructured graphene metasurface is considered for the dynamically tunable scattering manipulation of dielectric and conducting cylinders. It is analytically shown that changing scattering width of dielectric and conducting cylinders into one of the cylinders with desired radii is possible. To achieve this purpose, the required surface impedance of the covering metasurface is derived. By properly tuning the chemical potential of the graphene, the given cylinder can controllably be seen as one with larger or smaller radii without changing the geometry of the metasurface. The simulation results of the scattering width and the 3-D far-field radar cross-section of the cylinders verify the analytical approach.

The Truncation Region to Predict the Far-Field RCS From the Bistatic Near Field

Because of the distortion of scatter pattern when measuring multiple-scattering targets in the condition of not satisfying the far-field condition, existing measurements are not favorable. We present a novel algorithm to research near-field to far-field transformation (NFFFT) for multiple-scattering targets. This paper introduces the method of NFFFT using “multiple emission multiple reception” and then chooses a dihedral angle, which has multiple-scattering structure to simulate. The results show a good agreement with the far-field radar cross section (RCS) pattern. Taking into account the feasibility of design, the cost of system, and other limitations in practical application, the bistatic measurement system with a full 360° is obviously hard to achieve. In the bandwidth-limited scattering field, we give the rule of angular truncation based on the transfer function for reducing receiving region. To make the conclusion more reliable, simulations should be set in different parameters, concluding testing frequencies, target size and measuring distance, to observe their effects on the receiving region. We get the mean error of different receiving region by comparing the results of NFFFT and far-field RCS. As a result, a principle for choosing a proper receiving region, say angular truncation, is summarized for multiple-scattering targets, with mean errors 0.5 dB below the full 360° results near the center of far-field pattern.

Plane-Wave Synthesis and RCS Extraction via 3-D Linear Array SAR

This letter presents an improved back-projection (BP) technique for far-field radar cross section (RCS) feature extraction from near-field measurement. The presented technique can synthesize a plane-wave response from a 2-D near-field planar scanner and obtain a 3-D scattering coefficient image. Due to the 3-D resolving power, it can spatially extract different parts of a complex object, or target’s scattering coefficient from the whole image which may include environment noise such as ground and support. From the 3-D scattering coefficient image, far-field RCS can be obtained as a function of direction and frequency. Simulations of RCS feature extraction for three squares and a 3-D complex-shaped electric-large car model are presented to validate the improved BP method.

Preparation of nanofiber polythiophene layered on Ba x Sr1−x Fe12O19/Fe3O4/polyacrylic acid core–shell structure and its microwave absorption investigation

Ba x Sr1−x Fe12O19/Fe3O4/polyacrylic acid/polythiophene (Ba x Sr1−x Fe12O19/Fe3O4/PAA/PTh) nanocomposites with multi-core–shell structure were successfully synthesized by four steps. The samples were characterized by FTIR, X-ray diffraction (XRD), transmission electron microscope (TEM), vibrating sample magnetometer, and radar absorbing material reflectivity far-field radar cross-section method, respectively. XRD and TEM results indicated that the obtained nanoparticles have multi-core–shell morphology. The magnetic properties and microwave absorption analyses reveal that there are interphase interactions at the interface of Ba x Sr1−x Fe12O19, Fe3O4, PAA, and PTh, which can affect the magnetic properties and microwave absorption properties of the samples. The microwave-absorbing properties of nanocomposites were investigated at 8–14 GHz. A typical layer absorber exhibited an excellent microwave absorption with a −26 dB maximum absorption at 14 GHz. Compared with core material, the coercivity and saturation magnetization of multi-core–shell nanocomposites decrease obviously, but the microwave absorption properties of nanocomposites are improved greatly. The results show that these composite could be used as advancing absorption and shielding materials due to their favorable microwave-absorbing properties.