Statistical ISAR Imagery for Low-Altitude and Small-Size UAV Based on Sparse Bayesian Learning

In this paper, we propose and investigate a model based on asymptotic methods, designed to compute the radar cross section (RCS) of complex targets. A combined method of physical optics, geometrical optics, and the equivalent current method is applied to establish an effective backscattering analysis procedure. The scattering model includes shadowing effects, multiple bounce, and diffraction by edges. This model is used for RCS estimation and to generate inverse synthetic aperture radar (ISAR) raw data for imaging applications. We review the theoretical aspects and describe the proposed model and experimental setup in detail. Numerical results are provided and compared with the experimental results obtained in the anechoic chamber of ENSTA Bretagne to validate the approach through the computation of the RCS for canonical objects and a generic boat. Finally, a parallelepiped representation of the RCS is presented to be used for ISAR imagery, and the first results concerning the ISAR imaging of a generic boat are provided.

For the Inverse Synthetic Aperture Radar (ISAR) imaging, the ISAR image obtained by the Range-Doppler (RD) or time-frequency analysis methods can not display the target's real shape due to its azimuth relating to the target Doppler frequency, thus the cross-range scaling is required for ISAR image. In this paper, a fast cross-range scaling method for ISAR is proposed to estimate the Rotational Angular Velocity (RAV). Firstly, the proposed method utilizes efficient Pseudo Polar Fast Fourier Transform (PPFFT) to transform the rotational motion of two ISAR images from two different instant time into translation in the polar angle direction. Then, a new cost function called integrated correction is defined to obtain the RAV coarse estimation. Finally, the optimal RAV can be estimated using the Bisection method to realize the cross-range scaling. Compared with the available algorithms, the proposed method avoids the problems of precision loss and high computational complexity caused by interpolation operation. The results of computer simulation and real data experiments are provided to demonstrate the validity of the proposed method.

Spectrum cartography is the process of estimating radio map over a given geographical region. The key problem of model-based cartography is multiple transmitters localization from limited geographical signal power measurements. Existing methods assume that unknown transmitters are exactly on a subset of known candidate locations. In this paper, we avoid this strong assumption via the variational Bayesian expectation maximization (VBEM) algorithm. In the VBEM algorithm, the candidate locations are viewed as an unknown parameter that will be refined and pruned dynamically during the iterations. Compared to the existing methods, simulation results show that our proposed method can significantly reduce the estimation error of spectrum cartography.

Radar cross-section (RCS) is an important study parameter for defence applications specially dealing with airborne weapon system. The RCS parameter guides the detection range for a target and is therefore studied to understand the effectiveness of a weapon system. It is not only important to understand the RCS characteristics of a target but also to look into the diagnostic mode of study where factors contributing to a particular RCS values are studied. This further opens up subject like RCS suppression and stealth. The paper discusses the RCS principle, control, and need of measurements. Classification of RCS in terms of popular usage is explained with detailed theory of RF imaging and inverse synthetic aperture radar (ISAR). The various types of RCS measurement ranges are explained with brief discussion on outdoor RCS measurement range. The RCS calibration plays a critical role in referencing the measurement to absolute values and has been described.The RCS facility at Reseach Centre Imarat, Hyderabad, is explained with some details of different activities that are carried out including RAM evaluation, scale model testing, and diagnostic imaging.Defence Science Journal, 2010, 60(2), pp.204-212, DOI:http://dx.doi.org/10.14429/dsj.60.341

In this paper, a new joint symbol detection and channel estimation algorithm is proposed for MIMO-OFDM systems over frequency-selective fading channels using the variational Bayesian expectation-maximization (VBEM) algorithm. Since the VBEM algorithm can provide distribution-estimates of the parameters, the statistical information about the channel uncertainty is exploited to improve the evaluation of the extrinsic information in the soft-input soft-output detector which identifies the significant symbol combinations via list sphere decoder. In addition, two channel estimators are derived based on the posterior distributions of the transmitted symbols which are obtained from the space-time detection. The VBEM iterations are embedded in the turbo-processing of the receiver. Simulation results demonstrate that the proposed VBEM algorithm has more robust performance over the conventional EM techniques.

Usually, in traditional Inverse Synthetic Aperture Radar (ISAR) systems design and mode selection for space satellite targets, coherent integration gain in azimuth direction hardly can be analyzed, which depends on target’s motion. In this study, we combine the target orbit parameters to determine its motion relative to radar and deduce coherent integration equation in ISAR imaging to realize the selection of imaging intervals based on coherent integration, which can ensure the resolution in azimuth direction. Meanwhile, we analyze the influence of target orbit altitude to echo power and imaging Signal-to-Noise Ratio (SNR) that provides a new indicator for space observation ISAR systems design. The result of simulation experiment illustrates that with target orbit altitude increasing, coherent integration gain in azimuth direction of large-angular observation offsets the decreasing of imaging SNR in a degree, which provides a brand-new perspective for space observation ISAR systems and signal processing design.

An extensive 3D radar cross-section (RCS) data set has recently been acquired at the HARC Microwave Imaging Facility. This data set includes diversity in azimuth and elevation through antenna placement as well as frequency diversity. The addition of elevation diversity to standard Inverse Synthetic Aperture Radar (ISAR) imaging allows 3D object reconstruction, as opposed to the 2D defocused projections obtained by conventional ISAR. The method, based on spherical collection and a paracentric approximation for the target space, relates the reconstruction to the acquired data by a 3D Fourier transform. Resulting images are presented for full- and quasi-3D reconstructions, including 2D tomographic slices of the 3D target and a focused projection of the 3D target data onto a single horizontal plane. >

Inverse synthetic aperture radar (ISAR) imaging on a turntable-tower test range permits convenient generation of high resolution 2- and 3-D images of radar targets under controlled conditions, typically for characterization of the radar cross section of targets or for testing SAR image processing and automatic target recognition algorithms. However, turntable ISAR images suffer geometric distortions and zero-Doppler clutter (ZDC) artifacts not found in airborne SAR images. In this paper, ISAR images formed at Georgia Tech's Electromagnetic Test Facility are used to demonstrate and compare selected members of one family of 2- D ISAR imaging algorithms, from a simple but distortion- prone 2D discrete Fourier transform to a computationally- intensive matched filter solution. A simple algorithm for correcting range curvature using image domain resampling is described. We then demonstrate two signal processing techniques to suppress zero-Doppler clutter while minimizing effects on the target signature. The first removes ZDC components in the frequency domain, whereas the second performs cancellation in the image domain.

The application of radar absorbing materials on the surface of metal aircraft can effectively reduce the radar cross section (RCS). In this paper, the physical optics (PO) method is used to calculate the RCS of electrically large coated targets. First, we calculate the RCS of the original X29 aircraft and the X29 aircraft coated with a broadband two-layer radar absorbing coating. In addition, the backscattered electric field data of coated and uncoated X29 aircraft are obtained by using the PO method, respectively. Finally, the two-dimensional Inverse Fourier Transform (IFT) of the collected data is taken to form the Inverse Synthetic Aperture Radar (ISAR) image.

An essential characteristic of any radar target is the measure of its ability to reflect energy to the receiving antenna. The parameter used to describe this ability is the radar cross section (RCS) of the target. Stealth techniques and technologies, which affect the efficiency of early warning radar systems currently in operation, are described in ref [1]. The problem is to find countermeasures that fully integrate new requirement capabilities into the current long-range radar net and infrastructure, keeping research & development, and maintenance costs, low. The netcentric approach recently proposed in ref [2] using VHF-based multi-band radar systems is a new concept for Recognized Air Picture production based on long-range radars, which offers a powerful solution for the surveillance of large areas at a reasonable cost. Target RCS behaviour, fluctuations due to aspect angle and frequency are of particular interest in these systems, because the antenna beam positioning of the "VHF", "L" (or "S") and "X" band radars should be synchronised in azimuth and elevation. Targets could change orientation, course and kinematical characteristics during the time required for beam positioning. Advance knowledge and accurate prediction of target RCS is therefore desirable in order to design and develop efficient (robust) discrimination algorithms for the optimisation of target detection, tracking and non-cooperative target recognition. This paper reviews the target RCS aspects in current radar systems of interest, and examines RCS behaviour for use in future multi-band radar system applications.

The results of an investigation into processing techniques for reducing target-support contamination in inverse synthetic aperture radar (ISAR) and radar cross-section (RCS) measurements are presented. A three-stage processing approach is described, and its feasibility is successfully demonstrated on actual experimental data. The improvement in RCS accuracy after processing was very good. This is illustrated by two-dimensional displays of target cross-section versus angle over 360/spl deg/ and frequency from 0.5 to 2.0 GHz on a 30 dB scale (max = -3.4 dBsm). >

In this paper, we propose a deep learning-based model to generate high-resolution inverse synthetic aperture radar (ISAR) images under a limited radar bandwidth. While traditional ISAR systems achieve a higher resolution with a wider bandwidth, this comes at the cost of increased system resource consumption. To address this issue, we propose a method to generate high-resolution images from low-resolution ISAR images using deep learning. The training data used in this study consist of ISAR image pairs generated by calculating the scattered field using physical optics. The images generated by the proposed model can achieve average performances of 0.08, 8.94, and 0.19 in terms of learned perceptual image patch similarity, natural image quality evaluator, and deep image structure and texture similarity, respectively, compared to the original images.

Translational motion compensation for maneuvering targets is a challenging step in inverse synthetic aperture radar (ISAR) imaging. On one hand, nonuniform translational motion and rotational motion of maneuvering targets result in nonlinear bending of the range profile and create high-order slow time phases. On the other hand, radar cross section (RCS) fluctuation of targets may cause a sparse aperture and, therefore, sharply weakens the correlation between the pulses. Under this condition, traditional methods of translational motion compensation are sometimes difficult to get ideal results. Therefore, we propose an effective and novel method to achieve translational motion compensation for maneuvering targets with sparse aperture. In this method, the translational and rotational motions are modeled as cubic polynomial and quadratic polynomial, respectively. Based on this model, we use the echo to construct a 1-D optimization to estimate all translational parameters simultaneously and use the root-mean-square prop-momentum gradient descent (RMSprop-MGD) algorithm to solve the optimization to maintain the accuracy of parameter estimation. Finally, we use those translational parameters to compensate for the translational motion. Since this method converts the multiparameter optimization into a 1-D optimization, we named it dimension compressed optimization (DCO). Experimental results of the measured dataset prove that the proposed method is effective for full-aperture echoes, random sparse echoes, and block sparse echoes. Moreover, this method is robust under a low pulse sampling rate and low signal-to-noise ratio (SNR) environment.

A brief overview of radar cross section (RCS) measurement techniques is presented. Parameters to be measured include: RCS versus target aspect angle; RCS versus frequency; high-resolution range profiles (one-dimensional images); and high-resolution two-dimensional inverse synthetic aperture radar (ISAR) images. In this review, the measurement radar is limited to ground-based radar systems. Targets include: operational full-scale ships and aircraft; full-scale aircraft mounted on pylons; scale model aircraft mounted on pylons; and scale model ships in water. The review includes compact ranges in anechoic chambers.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The paper presents a new inverse synthetic aperture radar (ISAR) algorithm intended for radar cross-section (RCS) imaging and measurement from scattered fields. The method, based on a spherical-wave near-field illumination of the target, overcomes the requirement for an expensive compact range facility to produce a plane wave illumination. The formulation and the implementation of the algorithm are described. Some experimental results obtained in an anechoic chamber are presented to show RCS results similar to the conventional plane wave methods.