EM scattering of a target over sea surface based on physical optics

This paper presents the characteristics of radio wave scattering from rough sea surfaces in Ka-band. The physical optics method, which is one of the high frequency approximation algorithm, is employed to calculate radar cross section of electromagnetic simulation models of rough sea surfaces with Ship-EDF software. Rough sea surfaces are divided into six levels according to the wind speed over them. The simulation results show the effection that wind speed, the grazinng angle and frequency of radio wave have on the monostatic radar cross section of rough sea surfaces under the conditions of both horizontal and vertical polarization. With the increase of the roughness of sea surfaces and grazing angle of radio wave, the radar cross section value of rough sea surfaces increase. The frequencies of Ka-band have little contribution to the radar cross section value.

Compared with scattering from a rough surface only, composite scattering from a target above a rough surface has become so practical that it is a subject of great interest. At present, this problem has been solved by some numerical methods which will produce an enormous calculation amount. In order to overcome this shortcoming, the reciprocity theorem (RT) and the method of equivalent edge currents (MEC) are used in this paper. Due to the advantage of RT, the difficulty in computing the secondary scattered fields is reduced. Simultaneously, MEC, a high-frequency method with edge diffraction considered, is used to calculate the scattered field from the cone-cylinder target with a high accuracy and efficiency. The backscattered field and the polarization currents of the rough sea surface are evaluated by the Kirchhoff approximation (KA) method and physical optics (PO) method, respectively. The effects of the backscattering radar cross section (RCS) and the Doppler spectrum on the size of the target and the windspeed of the sea surface for different incident angles are analysed in detail.

Wave scattering from rough surface is important in many diverse areas, including radio communications, microwave remote sensing, underwater acoustics. The recent interest in bistatic radar, or the study of radio links above the sea surface, has led to the need for information concerning bistatic reflectivity of clutter viewed by such systems, particularly for near grazing incidence. The problem of developing a model for a rough surface is very difficult since at best /spl sigma/ is dependent upon (at least) radar frequency, geometrical parameters, physical parameters, incident and observation angles, and polarization. Therefore, it is necessary to restrict the areas of applicability of such models. A first major new consideration in this paper is to study the domain of validity of the Kirchhoff and small-perturbation rough surface scattering models (in the bistatic case). Comparisons between the numerical calculations and the models are presented for various surface rms height and correlation length both normalized to the incident wave number (denoted by k/spl sigma/ and kL, respectively). These two parameters form a two-dimensional space, into which the approximate regions of validity are established. The second new major consideration is the development of the theoretical composite model describing bistatic reflectivity. One application is implemented for the ocean surface case. Numerical results for the cross sections are compared to those found using Kirchhoff approximation (Physical Optics), small-perturbation model, and the phase perturbation technique.

A new and simple method of interpreting the of the backscatter radar cross section (RCS) from ocean waves of individual wavelength is presented. Using the Kirchhoff scattering (Physical Optics) theory, the RCS from the ambient waveheight spectrum is first computed as a function of the wavenumber. Differentiating this cumulative RCS yields the distribution function of the RCS from ocean waves of different wavenumbers. The present study shows, as a general feature, that the maximum RCS contribution tends to come from the ocean waves of wavenumbers closer to the Bragg wavenumber as the radar incidence angle increases. Under low wind speeds (/spl sim/2 m/s) and at incidence angles between 200 and 45/spl deg/, the dominant RCS contribution at both C-band and L-band comes from ocean waves of wavenumbers close to the Bragg wavenumber. Under intermediate to high wind speeds (/spl sim/10-15 m/s) and at the small incidence angle of 200, the radar backscatter is dominated by long ocean waves with little contribution from the Bragg waves at both bands. At the intermediate incidence angles (/spl sim/35/spl deg/ to 45/spl deg/), the RCS distribution is centered at wavenumbers near the Bragg wavenumber at all wind speeds from 2 m/s to 15 m/s. These features are more pronounced at C-band than at L-band. The present theory is based on the Kirchhoff scattering model, and as such, its validity may also be limited to a range of small to intermediate incidence angles where the polarization effect is not very significant. The RCS distribution is evaluated for the ambient sea surface and the surface perturbed by varying currents induced by the interaction between the current and bottom topography.

In this study, an efficient method of designing laminate composite radar absorbing structures (RAS) is proposed with consideration given to the structural shape so as to improve aircraft stealth performance. The calculation of the radar cross section (RCS) should be decreased to enhance the efficiency of the stochastic optimization when designing an RAS. In the proposed method, RAS are optimized to match up the input impedance of the minimal RCS, which is obtained by using physical optics and the transmission line theory. Single and double layer dielectric RAS for aircraft wings are employed as numerical examples and designed using the proposed method, RCS minimization and reflection coefficient minimization. The availability of the proposed method is assessed by comparing the similarity of the results and computation time with other design methods. According to the results, the proposed method produces the same results as the stochastic optimization, which adopts the RCS as the objective function, and can improve RAS design efficiency by reducing the number of RCS analyses.

The problem of electromagnetic scattering from a randomly rough surface is analyzed using the Kirchhoff approximation (stationary phase, scalar approximation), the small perturbation model and the two-scale model. The authors show that physical and geometrical optics approximations can be used for a rough sea surface. A small perturbation model gives a satisfactory solution. Unfortunately, it only applies to a smooth sea surface. Therefore, these methods have been shown to be limited in their regions of validity. Comparisons between the numerical calculations and the models are made for various surface rms heights and correlation lengths both normalized to the incident wave number (denoted by k/spl sigma/ and kL, respectively). The two-scale model proposed in this paper is the method which tries to conciliate the two previous aspects. One application is made in the ocean surface case. A set of experimental data, describing simultaneous bistatic radar cross section and forward radar cross section measurements of sea clutter taken with a moderately high resolution X-band scatterometer system operating near grazing incidence for both horizontal and vertical polarizations over a range of low grazing bistatic angles, is used but not presented in this paper to examine the applicability of bistatic scattering models.

This paper describes the Radar Cross Section (RCS) analysis and prediction for simple target shapes such as circular flat plate, triangular flat plate, ellipsoid, sphere, ellipsoid, truncated cone and cylinder. This paper also discusses the accuracy of physical optics (PO) approximation in measuring the far field monostatic radar cross section of different simple objects varying in size using the Matlab Programming to simulate the output graphs, and the results were compared to a more exact data obtained by CST Microwave Studio Simulation software which uses asymptotic solver in calculating RCS for these objects.

The ability of a threat system to quickly detect, locate, identify, and accurately track an armored helicopter has a significant influence upon the safety of the target. RCS (radar cross section) reduction is a major consideration in the design of a stealthy armed helicopter. This paper is to provide a systematic method for qualitatively assessing the RCS reduction benefits on the helicopter detection probability. Considering the helicopter radar scatter signal of mirror reflection and edge diffraction at high radar frequency, the RCS computation methods of PO (physical optics) and ILDC (increment length diffraction coefficient) are firstly introduced. Then probability theory is employed to predict the radar detection probability. Moreover, two hypothesis armed helicopters, in which one is conventionally designed and another is stealthily designed with radar absorbent material, are used as an example to illustrate the feasibility of the proposed method. Analysis shows that the benefits of radar signature reduction (i.e., radar absorbent material) include a decrease in the radar detection probability and an increase in the effectiveness of any radar countermeasures.

In this contribution a model based on asymptotic methods is proposed to compute the scattered fleld from complex objects on a sea surface. The scattering model combines the geometrical optics, the physical optics and the method of equivalent currents. It includes the shadowing efiects and multiple-bounce up to order 3. This model is used, in the following, for Radar Cross Section (RCS) estimation and to generate Synthetic Aperture Radar (SAR) raw data for imaging applications. The theoretical aspects are reviewed in this paper and the proposed model is detailed. Numerical results are provided to validate the approach through the computation of RCS for canonical objects and complex scenes. Both the bistatic and the monostatic conflgurations are studied in this work. Finally some flrst results dealing with SAR imaging of objects on a sea surface are provided. These images are constructed from the simulated raw data thanks to a chirp scaling-based algorithm.

In this paper, an efficient algorithm is proposed to the angular-diversity radar recognition of ships with noise effects taken into consideration. The goal is to identify the similarity between the unknown target ship and known ships. The content of this paper is divided into two parts. In the first part, the angular-diversity radar recognition of ships is given by transformation based approaches, i.e., the principal components analysis (PCA), with noise effects taken into consideration. The goal is to identify the similarity between the unknown target ship and known ships. In the second part, the linear discriminant algorithm (LDA) is utilized to increase the recognition rate. Initially, the angular-diversity radar cross sections (RCS) from a ship are collected to constitute RCS vectors (usually large-dimensional). By changing the elevation angle or the ship type, different RCS vectors are obtained to produce a high-rank covariance matrix. By choosing some of the largest eigenvalues and their corresponding eigenvectors, all the RCS vectors are projected onto the eigenspace (usually small-dimensional). Similarity between the unknown target ship and known ships can be identified in the eigenspace with high recognition rate. This will reduce the complexity for radar recognition of RCS characteristics from ships. However, the separating ability for such an elementary recognition is usually poor. This poor separation of radar target recognition will make the prediction results unreliable. The PCA gives the major features for the projected data of the two classes. While the LDA gives the best separation for the projected data of the two classes. To enhance the separating ability of radar target recognition, the projection features on the PCA space are further projected onto the LDA space and the recognition is performed on the LDA space. Our simulation shows that the separating ability for RCS based recognition of targets is greatly increased by using the LDA in the radar recognition process. In addition, the use of LDA in the recognition process increases the ability to tolerate noise effects. This study will be helpful in many applications of radar target recognition.

The article focuses on developing a predictive and optimization model for surface roughness in ultra-precision turning (UPT) using a diamond cutter on the spherical surface of Al6061 material. An experimental model with 30 experiments was established, considering three input parameters: spindle speed, feed rate, and depth of cut. The results from this model were collected to create an input dataset for an artificial neural network (ANN) to build a surface roughness prediction model. The ANN structure 3-5-10-1 provided the best prediction results, with Coefficient of Determination ( ) is 0.98, Mean Absolute Percent Error ( ) is 13.36%, Mean Square Error ( ) is 0.68, and Root Mean Square Error ( ) is 0.82. Additionally, the artificial bee colony (ABC) algorithm was employed to determine the optimal cutting parameters that minimize surface roughness. The results indicated that the minimum roughness value achieved was 0.76 nm with the cutting parameters: spindle speed of 823 rev/min, feed rate of 13 mm/min, and depth of cut of 1 µm. Moreover, the effects of different cutting parameter combinations on surface roughness were analyzed and evaluated. The integration of the ANN model with the ABC algorithm enables a reliable prediction model for surface roughness and demonstrates high efficiency in optimizing the objective function. This research contributes valuable insights into surface roughness prediction and optimization in ultra-precision turning of Al6061 material. Furthermore, the proposed modeling and optimization approach can be extended to other materials and the processing of aspherical and diffractive surfaces.

In this paper, a method based on machine learning and physical optics (PO) is proposed to accurately predict the monostatic radar cross section (RCS) of a polygonal metal plate. We use the PO method to calculate the RCS of the target, and then use the neural network to correct the result. By comparing the RCS calculated by the method of moments (MOM), our algorithm can accurately obtain the RCS of the target at different frequencies and different angles.

To study the electromagnetic scattering characteristics of a V-shaped deformation quadrotor, a calculation approach is presented to determine the dynamic radar cross section (RCS). The rotation of the single/multi-rotor, the opening of the support arm and the deformation of the fuselage are dynamically simulated. Physical optics and physical theory of diffraction are used to calculate the target RCS. The results show that when undeformed, the presence of strong scattering sources such as support arms, rotor towers, and hubs allows for an instantaneous peak of 13.08 dBm 2 under given forward azimuth, and this peak indicator will decrease to 2.714 dBm 2 as the elevation angle increases. Under given conditions, the RCS mean index increases from 4.9693 dBm 2 to 8.7069 dBm 2 as the support arm angle increases from 10° to 50°. When the fuselage rotation angle increased from 20° to 80°, and the RCS mean decreased from 7.7414 dBm 2 to 5.1235 dBm 2 under given observation conditions. It is feasible for the presented method to study the RCS of the V-shaped deformed quadrotor.

[1] Two orbital synthetic aperture radars (SARs), the Chandrayaan-1 Mini-SAR (13 cm wavelength) and the Lunar Reconnaissance Orbiter (LRO) Mini-RF (13 and 4.2 cm wavelengths), have been imaging the lunar surface searching for ice deposits in the polar permanently shadowed areas. To understand the radar signatures of lunar polar ices, an empirical two-component model with parametric variations of the specular and diffuse components was developed and validated. This model estimates scattering differences associated with slopes, surface roughness, thin regolith over ice, and patches of ice. Lunar radar backscatter cross sections for the average surface for the Chandrayaan-1 and LRO instruments are estimated from the radar cross sections from the Moon at 3.8, 23, and 68 cm wavelengths measured in the 1960s at the Massachusetts Institute of Technology. This modeling predicts that enhanced diffuse scattering from near-surface ice can be separated from rocks if the scattering is characterized by both the high reflectivity and circular polarization ratios (CPRs) like those observed on Mercury, Mars, and the Galilean satellites. Scattering from near-surface ices covered by a thin regolith can be separated from rocks if the enhancement is twice the average or more. If, however, the lunar ice is dispersed throughout the regolith as ice-filling pores, then scattering differences might be too small to detect. Preliminary validation using LRO radar data for a few polar and midlatitude craters indicate that the observed CPRs are consistent with our models for different regolith ice and roughness conditions.

A measurement method and system based on conventional radars has been proposed to address the problem of radar cross-section (RCS) measurement of ship targets in open seas. Calibration errors for RCS measurement on ground plane range (GPR) are analysed, and the error elimination method and accurate RCS calculation formula are given. An RCS measurement system is designed by improving conventional radars. These systems measure two Luneburg lens reflectors of different sizes on the lake. The average RCS of the big Luneburg lens reflector is obtained, and the error is calculated to be 2.73% compared with its actual value, which verifies the rationality of the proposed measurement method and system. The system measures the RCS of a ship in the open sea and obtains its circumferential RCS distribution. The effect of the error in the ship's position calculation on the RCS measurement results is analysed, and the uncertainty range of the error is obtained.