In a multipath environment, the fully polarized reflection coefficients significantly impact the polarimetric measurement and calibration of a target, due to the depolarization effects of its underlying surface. In this letter, a technique for estimating the fully polarized reflection coefficients of underlying rough surfaces by means of a switchable double-antenna polarimetric active radar calibrator (SIDAPARC) is proposed. Calibrated polarimetric measurements in multipath environments with different underlying surfaces are conducted to validate the usefulness of the proposed technique. Experimental results demonstrate the effective extraction of fully polarimetric radar returns directly from the target, with noticeable suppression of multipath components from the underlying surface reflections.

Polarimetric active radar calibrators (PARCs) are getting more and more applications in calibration of polarimetric radar systems because of their flexible operational modes and adjustable radar cross section (RCS). In this article, a new programmable radio frequency (RF) switched double-antenna PARC (SIDAPARC) is designed and implemented, which consists of a transponder using two dual-polarized antennas operated through the control of two single-pole double-throw (SPDT) RF switches. Standard reference polarimetric scattering matrices (PSMs) can be obtained by switching the polarization states of the receive and transmit channels using the two program-controlled SPDT switches. Considering the distortion of calibrator, a modified polarimetric measurement signal model for SIDAPARC is developed. A polarimetric calibration procedure based on the pre-calibration technique for SIDAPARC is proposed. The polarization crosstalk factors of calibrator can be estimated through the pre-calibration. During the calibration of a polarimetric radar system, compensating for these factors enhances the estimates of radar system distortion parameters, thus improving polarization calibration performance. Both indoor and outdoor experiments are conducted to demonstrate the usefulness of the proposed calibration procedure with more than 16-dB improvement of polarization isolation for fully polarimetric imagery.

Compressed sensing (CS) based imaging technique is considered to be an effective solution for high-resolution radar imaging due to the sparse distribution of the scatterers. Nevertheless, the unoptimized CS based synthetic aperture radar (SAR) or inverse SAR (ISAR) imaging approach may suffer from the computationally intensive problem when apply it to wideband radar signatures of electrical large-scale targets. In this paper, a two-dimensional (2-D) superresolution imaging technique based on peak search and compressed sensing (PS-CS) is presented. A peak search strategy is first developed to solve the problem of high computational complexity of CS based method in scattering parameter estimation. Superresolution imaging result is then achieved by extrapolating the estimated parameters of scattering along with the observing angle dimension and frequency dimension. The numerical and measurement data acquired from different man-made targets are presented to demonstrate the feasibility and usefulness of the proposed technique for supperresolution radar imaging.

It is known that the challenge for detection of small targets on the sea surface is the low signal-to-clutter ratio (SCR). In particular, for low grazing angles and high sea states, wave shading and sea spikes make the small target detection even more difficult. This letter proposed a novel phase-feature detector of small targets in sea clutter. Three phase features, which correspond to different radar scattering mechanisms between the target and sea surface, are extracted in the phase domain of radar echoes, namely, the number of phase crossing zero points, the maximum value of the phase difference probability density function, and the decorrelation time of the phase difference. A detector with a controllable false alarm rate (FAR) based on phase features is constructed using the fast convex hull learning algorithm. Experimental results on measured databases demonstrate that the proposed phase-feature detector attains better performance than the existing tri-feature detectors.

The 3-D inverse synthetic aperture radar (ISAR) tomography is an enabling technique for applications such as the exact diagnosis of scattering mechanisms for complex targets. Nevertheless, current ISAR tomography solutions still suffer from problems such as the great computational complexity and optimal utilization of the signatures acquired from limited baselines. In this work, we propose a fast ISAR tomography technique for fully polarimetric 3-D imaging of man-made targets. A stack of 2-D complex-valued images with different baselines and polarizations is first obtained through a phase error calibration (PEC) process and graphic processing unit accelerated polarimetric filtered backprojection. A polarimetric state-space decomposition (P-SSD) algorithm is then developed which could provide joint 3-D reconstruction results with low computational complexity. Examples from both numerical multibaseline data for the Sandia laboratories implementation of cylinders (SLICY) benchmark model and the outdoor range dataset collected by the Georgia Tech Research Institute (GTRI) are presented to demonstrate the superior performance and the usefulness of the proposed technique.

The numerical simulation of Doppler spectrum for 2-D sea surfaces at high microwave bands requires a huge number of sample points for the sea surface, which leads to a huge amount of computation and requires a large computer memory. As a result, the Doppler simulation for 2-D sea surfaces at high microwave bands is considered to be a great challenge. In this paper, the Fourier spectrum of 2-D sea surface is decomposed into multiple blocks, and the sea surface corresponding to the complete Fourier spectrum is generated block by block. Then, the weighted curvature approximation method is adopted and the electromagnetic (EM) scattering fields of the 2-D time-evolving sea surfaces are obtained after calculating the scattering contributions of the blocks separately. In this way, the Doppler spectra of 2-D sea surfaces up to the X-band and Ku-band are simulated. To illustrate the behavior of Doppler spectrum for 2-D sea surfaces, Doppler spectra for 2-D linear and choppy wave model sea surfaces are simulated and analyzed under different radar and environment parameters. Conclusion for the evolution of Doppler spectra with incident angle, wind direction, and radar frequency is obtained. Moreover, the numerically simulated Doppler characteristics are compared with the results of analytical prediction and the results for 1-D sea surfaces. Although similar behaviors are observed in the comparisons, the details are different.

It is known that the major difficulty of sea clutter simulation is the controlled generation of a continuous correlated non-Gaussian random process. In particular, for sea clutter at low grazing angles and over a long time scale, the spiky and nonstationary nature makes the simulation more difficult. This paper proposes a novel procedure for the simulation of continuous correlated low-grazing-angle sea clutter with prescribed statistic and correlation characteristics. The Pareto distribution is utilized to describe the statistics of the sea clutter intensity. In addition, different correlation characteristics for sea clutter over both short and long time scales are considered in this paper. First, the memoryless nonlinear transform is adopted to simulate the intensities of the correlated sea clutter. Second, the Doppler spectra of different range bins are generated separately. In particular, for the sea clutter over long time scales, successive time-varying Doppler spectra are modeled to characterize the nonstationarity of the sea clutter series. Then, the phases of the sea clutter are retrieved by constraining to the desired Doppler spectra and given magnitudes. Different methods are adopted for the phase retrieval of sea clutter on short and long time scales. Finally, simulation results show that the proposed procedure can generate continuous low-grazing-angle sea clutter with prescribed statistic and correlation characteristics. In particular, for the sea clutter over a long time scale, the simulated sea clutter series enjoys the time-varying Doppler spectrum while maintaining continuity. In addition, the proposed method is also suitable for other models, such as the K-distribution, the Weibull model, and so on.

Based on estimation of signal parameter via rotational in variance techniques (ESPRIT), a super-resolution direction of arrival (DOA) estimation approach is developed for partly calibrated or noncoherent sparse array. Numerical results demonstrate that compared with the conventional multiple signal classification (MUSIC) based approaches, the proposed approach performs better under lower signal-to-noise (SNR) and with fewer snapshots, yet with less computational complexity.

Two-dimensional linear and second-order Creamer [Creamer (2)] nonlinear sea-surface models are combined with the second-order small-slope approximation method to comparatively study the electromagnetic scattering and the Doppler spectral characteristics from sea surfaces. Due to nonlinear hydrodynamics, the bistatic normalized radar cross section (NRCS) calculated from Creamer (2) surfaces is slightly larger than its linear surface counterpart for scattering angles departing from the specular direction, and the Creamer (2) surface backscattering coefficient increases as well for wind direction angles around upwind and downwind, whereas, for the crosswind direction, it is interesting to note that the observations are contrary. However, as was pointed out by Toporkov in a 1-D surface case, the effect of the nonlinear surface model on the average NRCS is minute. In particular, the Doppler spectra of the backscattered echoes are compared for the linear and Creamer (2) surfaces at various incident angles. It is seen, as expected, that the Doppler shifts and spectral widths of 2-D Creamer (2) surfaces exhibit different features compared with those of the linear surfaces, agreeing with the 1-D cases. However, for larger incident angles, the Doppler spectral broadening for Creamer (2) surfaces is not as severe as was reported in previous 1-D studies that used full Creamer model. The reason for this discrepancy appears to be the reduced nature of the Creamer (2) approximation. Moreover, studies of the Doppler characteristics for different wind directions demonstrate that nonlinear effects become weaker as the wind direction varies from upwind to crosswind.

A band clustering and selection approach based on a hyperspectral measure, spectral information divergence (SID) is presented in this paper. Hyperspectral image data is analyzed for target detection. Hyperspectral image data and spectral signatures of the targets are used to measure the SID. Virtual dimensionality (VD) is used to select optimal number of bands. For endmember extraction, vertex component analysis (VCA) is used. For decision fusion a new approach based on spectral discriminatory entropy (SDE) is proposed. A comparative study is conducted to show the effectiveness of new approach of band clustering and selection. Decision fusion is also compared with full band and individual SID detection schemes.

Resolution enhancement techniques in radar imaging have attracted considerable interest in recent years. In this work, we develop an iterative sidelobe apodization technique and investigate its applications to synthetic aperture radar (SAR) and inverse SAR (ISAR) image processing. A modified noninteger Nyquist spatially variant apodization (SVA) formulation is proposed, which is applicable to direct iterative image sidelobe apodization without using computationally intensive upsampling interpolation. A refined iterative sidelobe apodization procedure is then developed for image-resolution enhancement. Examples using this technique demonstrate enhanced image resolution in various applications, including airborne SAR imaging, image processing for three-dimensional interferometric ISAR imaging, and foliage-penetration ultrawideband SAR image processing.

Multi-dimensional high resolution is one of the most important factors in automatic target recognition (ATR) using the synthetic aperture radar (SAR) or inverse SAR (ISAR) images. In this work, we propose a resolution enhancement technique in three-dimensional (3-D) interferometric SAR/ISAR (IF SAR/ISAR) imaging using an iterative sidelobe apodization procedure.

Cross correlation receiver is one of the most important parts in a random noise radar system. In this paper, the impact of different correlation receiving techniques on the imaging performance of ultra wideband (UWB) random noise radar is studied. Three types of correlation receivers, namely, the ideal analog correlation receiver, the digital-analog correlation receiver, and the fully digital correlation receiver, are discussed.

An airborne low-cost synthetic aperture radar (SAR) is under development at the University of Nebraska-Lincoln. The SAR system is an X-band, stepped-chirp frequency modulation (SCFM) radar system. One of its unique features is that the waveform generation consists of a timing-controlled D/A converter and VCO arrangement to synthesize the SCFM signal, whereby allowing for less design complexity and a much lower overall system cost. In this paper, we present a brief description of the system and some computer simulation results.

Random noise polarimetry is a new radar technique for high-resolution probing of subsurface objects and interfaces. The University of Nebraska has developed a polarimetric random noise radar system based on the heterodyne correlation technique. Simulation studies and performance tests on the system confirm its ability to respond to phase differences in the received signals. In addition to polarimetric processing capability and the simplified system design, random noise radar also possesses other desirable features, such as immunity from radio frequency interference. The paper discusses the theoretical foundations of random noise polarimetry, and presents examples out of the entire data set collected that demonstrate the usefulness of the image processing and Stokes matrix presentation to enhance target detection using the coherent random noise radar.

Two-dimensional (2-D) inverse synthetic aperture radar (ISAR) imaging has been widely used in target scattering diagnosis, modeling and target identification. A major shortcoming is that a 2-D ISAR image cannot provide information on the relative altitude of each scattering center on the target. In this paper, we present an interferometric inverse synthetic aperture radar (IF-ISAR) image processing technique for three-dimensional (3-D) target altitude image formation. The 2-D ISAR images are obtained from the signature data acquired as a function of frequency and azimuthal angle. A 3-D IF-ISAR altitude image can then be derived from two 2-D images reconstructed from the measurements by antennas at different altitudes. 3-D altitude image formation examples from both indoor and outdoor test range data are demonstrated on complex radar targets.