Radar Cross Section Prediction Research Articles

Metallic Cylinder Reflected Power Measurement For 93.1GHz Frequency Modulated Continuous Wave Radar Calibration

A metallic cylinder is one of the best materials and shapes to calibratingradar system performance. In addition toits reflective properties, metallic cylindersallow foraccurate modeling for prediction of radar cross-section (RCS). This study presents themeasurement of a metallic cylinder with a diameter of 4cm and a height of 3cm as a target,analyzedinthe millimeter-wave (mm-wave) spectrum. Theexperiment was conducted in thereal airport environment at Kuala Lumpur International Airport, Malaysia, underclear sky conditions. The measurement was carried out at 93.1 GHz utilizing aFrequency Modulated Continuous Wave (FMCW) radar to consistently detect the target. TheRCSof the metallic cylinder wasmeasured at differentangles relative to therunway pavement. The results showed that the measurements exhibited smaller RCS value at longer ranges, with an average of –43.47 dBsm,compared to –30.16 dBsm at shorter ranges,indicating a total change of 13.31 dBsm. The reflectivity characteristics of the radar target, theoretical measurementsof the metallic cylinder, incident anglesfrom the radar target, and measurement evaluationsare presented in this paper.

Experimental characterization of scattering from the trihedral corner reflector in the E‐band

AbstractCanonical objects with known radar cross section (RCS), for example, the trihedral corner reflector (TCR), play a crucial role in the calibration of automotive radar sensors. Moreover, these canonical objects are also used in the validation of simulated RCS obtained using asymptotic methods, such as hybrid geometric optics (GO) and the physical optics (PO) based methods. However, accurate RCS prediction with asymptotic methods is highly dependent on the individual scattering mechanisms considered in a simulation, for example reflection and diffraction from the TCR surfaces and edges, respectively. Reliable measurements are therefore required to evaluate if a particular interaction mechanism can be neglected to reduce computation complexity without adversely affecting the accuracy of the predicted RCS. In this letter, the monostatic scattering characteristics of three metallic TCRs are investigated with varying geometrical sizes in the E‐band, that is, from 60 GHz to 90 GHz. The ultra‐wideband (UWB) measurements, which offer a high delay resolution, can enable the identification of the individual scattering mechanisms. Diffraction from the TCR edges is experimentally demonstrated to contribute to a non‐negligible scattered power in this frequency band.

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.

A Dynamic RCS and Noise Prediction and Reduction Method of Coaxial Tilt-Rotor Aircraft Based on Phase Modulation.

For tilt-rotor aircraft with coaxial rotors (coaxial rotor aircraft), reduction of radar cross section as well as acoustic noise can be essential for stealth design, and the rotation of the coaxial rotors can have an influence on noise and dynamic radar cross section (RCS) characteristics. In this paper, an approach to the prediction of both the sound pressure level (SPL) of noise and the dynamic RCS of coaxial-tilt aircraft is carried out, based on the theories of the FW-H equation, the physics optics method (PO) and the physical theory of diffraction (PTD) method. In order to deal with the rotating parts (mainly including coaxial rotors), a generated rotation matrix (GRM) is raised, aiming at giving a universal formula for the time-domain grid coordinate transformation of all kinds of rotation parts with arbitrary rotation centers and rotation axis directions. Moreover, a compass-scissors model (CSM) reflecting the phase characteristics of coaxial rotors is established, and a method of noise reduction and RCS reduction based on the phase modulation method is put forward in this paper. The simulation results show that with proper CSM parameter combinations, the reduction of noise SPL can reach approximately 3~15 dB and the reduction of dynamic RCS can reach 1.6 dBsm at most. The dynamic RCS and noise prediction and reduction method can be meaningful for the radar-acoustic stealth design of coaxial tilt-rotor aircrafts.

RCS Prediction Using Prony Method in High-Frequency Band for Military Aircraft Models

Obtaining Radar Cross Section (RCS) data, one of the essential parameters for aircraft design, generally takes a lot of time and cost. Measurement time and accuracy of measurement results may be affected depending on the RCS measurement method and environment. When it comes to the RCS measurement method, the direct approach, which measures RCS on a real item, is more accurate than the indirect approach, which is implemented through simulation. However, in consideration of balancing accuracy, time and cost, the indirect approach is more generally used due to its efficiency. In this paper, in order to find an optimized method for more improved prediction results of indirect approach in the high-frequency band, three prediction methods are proposed: the Prony method, the Matrix pencil method (MPM) and the Rational Function method. It is confirmed that the RCS prediction result utilizing the Prony method in the high-frequency band has the minimum error in the case of Prony and MPM Methods, which have not been utilized for RCS prediction in the high-frequency band, and the Rational function method with currently applicable cases are employed. The prediction methods are, respectively, applied to a model based on three military aircraft models such as Jet Plane, F-117 and Transport Plane, and its simulation is performed under identical conditions. The original data and the extrapolated data obtained from the methods are compared at a certain angle for each model, and the errors between the extrapolated data are also compared in order to verify the efficacy of the prediction methods.

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.

LPI radar target detection performance optimization based on joint cognitive frequency transmission and power allocation

In the Low Probability of Intercept (LPI) radar system, in order to optimize the radar target detection performance, the energy consumption should be reduced while improving the target detection performance. Based on the Hidden Markov Model (HMM), this paper proposes an optimization algorithm for target detection performance joint cognitive frequency transmission and power allocation combined with the target Radar Cross Section (RCS) frequency response function prediction. The algorithm uses the target RCS's characteristics with different frequency response functions at different frequencies. The radar transmits a set of broadband pulse signals with different frequencies at the current time to irradiate the target and obtain the echo. The passive receiving system processes the echo to obtain the target RCS frequency response measurement value, and determines it as a priori information. The RCS frequency response function of the target at the subsequent time is predicted by the prior information and HMM, and the complete RCS prediction information of the target is obtained. Finally, using the radar equation, the radar transmitting power is calculated through the maximum frequency response function in the predicted values at different times and its corresponding transmitting frequency. The optimized information is then transmitted to the detection radar to complete the joint cognitive frequency transmission and power allocation. The simulation results show that the method used in this paper can lead to more accurate prediction information than the traditional algorithm, efficiently reduce the target detection transmitting power, and improve the radar target detection and LPI performance, which has a high engineering application value.

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.

Radar Cross Section Prediction Using Iterative Physical Optics With Physical Theory of Diffraction

A new approach is presented to predict radar cross section (RCS) of electrically large conducting geometries using iterative physical optics (IPO) in conjunction with physical theory of diffraction (PTD). IPO is based on iterative refinement of physical optics (PO) currents to include multiple reflections. However, IPO does not yield sufficient accuracy in diffraction dominant regions since it does not properly include effects of edge diffractions. Hence, the proposed approach includes diffraction effects using PTD, which introduces edge currents. Diffracted fields due to these edge currents are radiated on triangular facets to modify the PO currents before IPO iterations. Scattered fields are computed from converged IPO surface currents and PTD edge currents. A new set of PTD edge currents excited by the fields due to the IPO currents are also found and the fields radiated by these PTD edge currents are added to the scattered fields. Therefore, interactions between the surface and edge currents are included. Computations are accelerated by parallel processing using message passing interface (MPI). Monostatic and bistatic RCS are predicted using the proposed approach for tail-wing and trihedral corner reflector geometries. The IPO-PTD results are compared with method of moments (MoM) and multilevel fast multipole method results obtained using field calculations involving bodies of arbitrary shape (FEKO) commercial electromagnetic simulation software for validation.

Joint waveform selection and power allocation algorithm in manned/unmanned aerial vehicle hybrid swarm based on chance-constraint programming

In this paper, we propose a joint waveform selection and power allocation (JWSPA) strategy based on chance-constraint programming (CCP) for manned/unmanned aerial vehicle hybrid swarm (M/UAVHS) tracking a single target. Accordingly, the low probability of intercept (LPI) performance of system can be improved by collaboratively optimizing transmit power and waveform. For target radar cross section (RCS) prediction, we design a random RCS prediction model based on electromagnetic simulation (ES) of target. For waveform selection, we build a waveform library to adaptively manage the frequency modulation slope and pulse width of radar waveform. For power allocation, the CCP is employed to balance tracking accuracy and power resource. The Bayesian Cramér-Rao lower bound (BCRLB) is adopted as a criterion to measure target tracking accuracy. The hybrid intelli gent algorithms, in which the stochastic simulation is integrated into the genetic algorithm (GA), are used to solve the stochastic optimization problem. Simulation results demonstrate that the proposed JWSPA strategy can save more transmit power than the traditional fixed waveform scheme under the same target tracking accuracy.

Calibrator Irregularity Error Compensation for Radar Cross Section Measurement

Calibration isan important step in measuring the electromagnetic scattering from a target. Metal spheres are usually selected as calibrators in the calibration process. However, real spherical calibrators are not perfect spheres, which cause calibration errors due to their geometric irregularity and thus affect the radar cross section (RCS) prediction accuracy for the target. A method of estimation and compensation based on high-resolution range profiles (HRRPs) of calibrators is proposed. First, the scattering of a real-world spherical calibrator is measured using the sweep-angle and the sweep-frequency strategies. Then, the error curves are obtained by comparing the HRRPs of the real-world calibrator and its theoretical solutions at each incident angle, which are also used to compensate the RCS error of the real-world spherical calibrator. Finally, the corrected RCS of the calibrator is adopted to access the accurate RCS of the target being tested. The results of simulation and testing show that the proposed method reduces the root mean square error of the RCS of the target by 40%, which demonstrates the effectiveness of the presentedmethod.

An Accelerated Shooting and Bouncing Ray Method Based on GPU and Virtual Ray Tube for Fast RCS Prediction

An accelerated shooting and bouncing ray (SBR) method is presented in this letter to improve the efficiency of radar cross section prediction for electrically large and complex objects. The SBR method is inefficient because it needs to trace a large number of ray tubes. Therefore, its efficiency can be improved by reducing the amount of ray tubes and increasing the speed of ray tracing. In this letter, we use rays instead of ray tubes to reduce the amount of rays that need to be traced, and construct a virtual ray tube at the ray exit position for field integration. Moreover, ray tracing and integration operations are executed on the graphics processing unit to further improve the efficiency of SBR method. Several examples are designed to prove the effectiveness of the proposed method.

Physical Optics Radar Cross Section predictions for an anti-ship cruise missile

The capability of the first strike is crucial in the modern battlefield. An important parameter is the radar signature or Radar Cross Section (RCS) of a weapon system, such as a fighter aircraft, a warship, or a missile, affecting the range at which this weapon system would be detected as a target by an enemy radar. If the attacker is detected too late, there will be minimal time for the defender to react, possibly not sufficient to counter the threat. The RCS of a weapon system is considered generally as classified information. However, it can be measured at a suitable measurement test range, if that weapon system is available. Otherwise, it can be predicted with the help of computational electromagnetics. Concerning the second approach, the following procedure was recently proposed: construction of a three-dimensional model of a target, based on available images and any relevant data, and then computation of the target RCS, with the Physical Optics approximative method. In the present approach, this procedure is applied to an anti-ship cruise missile in order to compute its RCS. Finally, the expected detection range for various naval radars is calculated.

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.

Efficient RCS Prediction of the Conducting Target Based on Physics-Inspired Machine Learning and Experimental Design

In this article, we propose a hybrid approach that combines machine learning and experimental design to efficiently and accurately predict the monostatic radar cross section (RCS) of a conducting target versus the incident angle. The approach is called physical optics-inspired support vector regression (POI-SVR). The design of its kernel function is inspired by PO. Uniform design (UD) and uniform design sampling (UDS) are introduced to obtain highly representative training samples. Numerical experiments dealing with simple and complex targets are carried out to evaluate the accuracy and efficiency of the proposed method. The results show that our method can reduce the predictive root-mean-square error (RMSE) by 29.38%-64.78% compared with the alternative methods of combining a Gaussian SVR with the centrically located sampling (CLS), the Latin hypercube sampling (LHS), or the simple random sampling (SRS). Under the same sampling strategies (i.e., UD and UDS), POI-SVR can reduce the predictive RMSE by 11.30%-53.56% compared with the Gaussian SVR. The well-trained POI-SVR can predict the monostatic RCS of the target in any direction within 0.1 s, and in 20 000 directions within 10 s.

Effects of Wind Wave Spectra on Radar Backscatter From Sea Surface at Different Microwave Bands: A Numerical Study

Wind wave spectrum describes the quasi-periodic nature of the ocean surface oscillations and plays an indispensable role in the study of microwave electromagnetic scattering from sea surface. A reliable spectrum model suitable for radar cross section (RCS) predictions at different radar frequencies is desired. This paper evaluated the performances of five common spectrum models (i.e., Fung spectrum, Durden-Vesecky spectrum, Apel spectrum, Elfouhaily spectrum, and the newest version of Hwang spectrum, H18) on the normalized radar backscattering cross section (NRBCS) simulations based on advanced integral equation model (AIEM) at L-, C-, X-, and Ku-bands versus incidence angle, wind direction, and wind speed by comparing with the model and measured data for validation. These results indicate no single wave spectrum of them is satisfying for all the four radar frequencies, e.g., Apel and H18 spectra are better for L- and C-bands, Apel spectrum for X-band, and Elfouhaily and H18 spectra for Ku-band. Given this, three average composite spectrum models are constructed using different spectral models (i.e., all five spectra, Apel + Elfouhaily + H18, and Apel + H18) to simulate NRBCSs, similar to that of the individual spectrum model. It is concluded that the combination of Apel and H18 spectra overall performs best among the individual one and other composited spectra in like-polarized NRBCSs versus incidence angles, wind directions, and wind speeds, for wind speed greater than 30 m/s where the combination of the five spectra work well at Ku-band.

On Bayesian Tracking and Prediction of Radar Cross Section

We consider the problem of Bayesian tracking of radar cross section. The adopted observation model employs the gamma family, which covers all Swerling cases in a unified framework. State dynamics are modeled using a nonstationary autoregressive gamma process. The principal component of the proposed solution is a nontrivial gamma approximation, applied during the time update recursion. The superior performance of the proposed approach is confirmed using simulations and a real-world dataset.

Multidisciplinary analysis of subsonic stealth unmanned combat aerial vehicles

In this paper, the GENUS multidisciplinary aircraft design and analysis environment is presented in its application to the conceptual design of tailless, low-observable unmanned combat aerial vehicles (UCAVs). Analysis disciplines comprise a variety of low to medium fidelity, physics-based and empirical methodologies, as well as higher order panel method aerodynamic analysis. Stealth considerations have been included in terms of a radar cross section analysis through a physical optics approximation method, with results verified against a well-known radar cross section prediction code. Preliminary results show good agreement for gross and empty masses when compared to several existing UCAV demonstrators and conceptual designs. A further validation of the presented methodologies is evaluated through the design, analysis, and optimisation of an unmanned strike fighter concept.

RCS prediction at high frequency using Cauchy method

AbstractNumerical calculation of radar cross section (RCS) at high frequency is quite challenging. In this correspondence, we apply the Cauchy method and TLS theory to RCS data at a low‐frequency band and predict RCS data at a high‐frequency band. The numerical demonstration shows that the proposed approach generates an estimation that falls within the acceptable error range.

Dynamic RCS Prediction of Rapidly Blooming Chaff Cloud and its Validation using Measurement on Scaled-down

In modern warfare scenario, chaff cloud consisting of millions of small individual microwave scatterers, is used as a passiveelectronic countermeasure to rapidly create a false target having Radar Cross Section (RCS) more than a real target. RCS prediction of chaff cloud by modeling and simulation is a complex phenomenon as each scatterer has different resonating length, oriented randomly and interact with each other in a complex form. Monostatic RCS measurement of the chaff cloud in the real scenario is also difficult as the target size is quite big and shape and size are changing with time. In the present paper, the dynamic RCS prediction of chaff cloud has been carried out by modeling and simulation using the in-house developed software in MATLAB. Various regular shapes like a plume, sphere, cylindrical and cubical have been presumed and interaction of each scatterer has been analytically evaluated based on screening effect. RCS response of chaff clouds of different shapes and sizes were predicted. To validate the predicted RCS values, miniaturized models of different shapes and sizes are prepared to simulate scale down dynamic chaff cloud blooming. Their monostatic RCS responses were measured in the anechoic chamber for 8-18 GHz. A good agreement is observed in measurement and predicted values.