Synthetic aperture radar (SAR) imagery is significantly deteriorated by the random phase noises which are generated by the frequency jitter of the transmit signal and atmospheric turbulence. In this paper, we recast the SAR imaging problem via the phase-corrupted data as for a special case of quadratic compressed sensing (QCS). Although the quadratic measurement model has potential to mitigate the effects of the phase noises, it also leads to a nonconvex and quartic optimization problem. In order to overcome these challenges and increase reconstruction robustness to the phase noises, we proposed a QCS-based SAR imaging algorithm by greedy local search to exploit the spatial sparsity of scatterers. Our proposed imaging algorithm can not only avoid the process of precise random phase noise estimation but also acquire a sparse representation of the SAR target with high accuracy from the phase-corrupted data. Experiments are conducted by the synthetic scene and the moving and stationary target recognition Sandia laboratories implementation of cylinders (MSTAR SLICY) target. Simulation results are provided to demonstrate the effectiveness and robustness of our proposed SAR imaging algorithm.

The MIMO technique can improve system performance of not only communication system but also of radar systems. In this paper, we apply the MIMO radar with enhanced angular resolution to the indoor location estimation of humans. The Khatri-Rao (KR) matrix product is also adopted for further angular resolution enhancement. We show that the MIMO radar with the KR matrix product processing can increase the number of virtual elements effectively with suitable element arrangement, hence higher angular resolution can be realized. In general, the KR matrix product processing is not suitable for coherent radar because of signal correlation. However, when targets signals have enough Doppler frequency differential against each other, this approach works well because the signals are decorrelated. In addition, Doppler filtering is introduced to remove unwanted responses of stationary objects which make human detection difficult with conventional methods. Computer simulation and experimental results are provided to show performance of the proposed method.

Microwave systems have a number of promising applications in surveillance and monitoring systems. The main advantage of microwave systems is their ability to detect targets at distance under adverse conditions such as dim, smoky, and humid environments. Specifically, the wide bandwidth of ultra-wideband radar enables high range resolution. In a previous study, we proposed an accurate shape estimation algorithm for multiple targets using multiple ultra-wideband Doppler interferometers. However, this algorithm produces false image artifacts under conditions with severe interference. The present paper proposes a technique to suppress such false images by detecting inconsistent combinations of the radial velocity and time derivative of image positions. We study the performance of the proposed method through numerical simulations of a two-dimensional section of a moving human body, and demonstrate the remarkable performance of the proposed method in suppressing false image artifacts in many scenarios.

With extensive use of automotive radars, mutual interference between radars has become a crucial issue, since it increases the noise floor in the frequency domain triggering frequent false alarms and unsafe decision. This paper introduces a mathematical model for a frequency-modulated continuous-wave (FMCW) radar in interfering environments. In addition, this paper proposes a time-domain interference suppression method to provide anti-interference capability regardless of the signal-to-interference ratio. Numerical results are presented to verify the performance of a 77GHz FMCW radar systme with the proposed method in interference-rich environments.

Ultra-wideband radar exhibits high range resolution, and excellent capability for penetrating dielectric media, especially when using lower frequency microwaves. Thus, it has a great potential for innovative non-destructive testing of aging roads or bridges or for non-invasive medical imaging applications. In this context, we have already proposed an accurate dielectric constant estimation method for a homogeneous dielectric medium, based on a geometrical optics (GO) approximation, where the dielectric boundary points and their normal vectors are directly reproduced using the range point migration (RPM) method. In addition, to compensate for the estimation error incurred by the GO approximation, a waveform compensation scheme employing the finite-difference time domain (FDTD) method was incorporated. This paper shows the experimental validation of this method, where a new approach for suppressing the creeping wave along the dielectric boundary is also introduced. The results from real observation data validate the effectiveness of the proposed method in terms of highly accurate dielectric constant estimation and embedded object boundary reconstruction.

The characteristic basis function method using improved primary characteristic basis functions (IP-CBFM) has been proposed as a technique for high-precision analysis of monostatic radar cross section (RCS) of a scattering field in a specific coordinate plane. IP-CBFM is a method which reduces the number of CBF necessary to express a current distribution by combining secondary CBF calculated for each block of the scatterer with the primary CBF to form a single improved primary CBF (IP-CBF). When the proposed technique was evaluated by calculating the monostatic RCS of a perfect electric conductor plate and cylinder, it was found that solutions corresponding well with analysis results from conventional CBFM can be obtained from small-scale matrix equations.

The RCS of a radar target is an important factor related with the radar performance such as detection, tracking and classification. When dealing with the design of 26/79GHz automotive surveillance radar system, it is essential to know individual RCS of typical vehicles and pedestrian. However, there are few papers related to the RCS measurement at 26 and 79GHz. In this letter, the RCS measurements of typical vehicles and pedestrian were performed in a large-scale anechoic chamber room and the characteristics are discussed.

We propose an effective technique for estimation of targets by ground penetrating radar (GPR) using model-based compressive sensing (CS). We demonstrate the technique's performance by applying it to detection of buried landmines. The conventional CS algorithm enables the reconstruction of sparse subsurface images using much reduced measurement by exploiting its sparsity. However, for landmine detection purposes, CS faces some challenges because the landmine is not exactly a point target and also faces high level clutter from the propagation in the medium. By exploiting the physical characteristics of the landmine using model-based CS, the probability of landmine detection can be increased. Using a small pixel size, the landmine reflection in the image is represented by several pixels grouped in a three dimensional plane. This block structure can be used in the model based CS processing for imaging the buried landmine. The evaluation using laboratory data and datasets obtained from an actual mine field in Cambodia shows that the model-based CS gives better reconstruction of landmine images than conventional CS.

Satellite-borne or aircraft-borne synthetic aperture radar (SAR) is useful for high resolution imaging analysis for terrain surface monitoring or surveillance, particularly in optically harsh environments. For surveillance application, there are various approaches for automatic target recognition (ATR) of SAR images aiming at monitoring unidentified ships or aircraft. In addition, various types of analyses for full polarimetric data have been developed recently because it can provide significant information to identify structure of targets, such as vegetation, urban, sea surface areas. ATR generally consists of two processes, one is target feature extraction including target area determination, and the other is classification. In this paper, we propose novel methods for these two processes that suit full polarimetric exploitation. As the target area extraction method, we introduce a peak signal-to noise ratio (PSNR) based synthesis with full polarimetric SAR images. As the classification method, the circular polarization basis conversion is adopted to improve the robustness especially to variation of target rotation angles. Experiments on a 1/100 scale model of X-band SAR, demonstrate that our proposed method significantly improves the accuracy of target area extraction and classification, even in noisy or target rotating situations.

Microwave ultra-wideband (UWB) radar systems are advantageous for their high-range resolution and ability to penetrate dielectric objects. Internal imaging of dielectric objects by UWB radar is a promising nondestructive method of testing aging roads and bridges and a noninvasive technique for human body examination. For these applications, we have already developed an accurate internal imaging approach based on the range points migration (RPM) method, combined with a method that efficiently estimates the dielectric constant. Although this approach accurately extracts the internal boundary, it is applicable only to highly conductive targets immersed in homogeneous dielectric media. It is not suitable for multi-layered dielectric structures such as human tissues or concrete objects. To remedy this limitation, we here propose a novel dielectric constant and boundary extraction method for double-layered materials. This new approach, which simply extends the Envelope method to boundary extraction of the inner layer, is evaluated in finite difference time domain (FDTD)-based simulations and laboratory experiments, assuming a double-layered concrete cylinder. These tests demonstrate that our proposed method accurately and simultaneously estimates the dielectric constants of both media and the layer boundaries.

In this paper, a real-valued reweighted l1 norm minimization method based on data reconstruction in monostatic multiple-input multiple-output (MIMO) radar is proposed. Exploiting the special structure of the received data, and through the received data reconstruction approach and unitary transformation technique, a one-dimensional real-valued received data matrix can be obtained for recovering the sparse signal. Then a weight matrix based on real-valued MUSIC spectrum is designed for reweighting l1 norm minimization to enhance the sparsity of solution. Finally, the DOA can be estimated by finding the non-zero rows in the recovered matrix. Compared with traditional l1 norm-based minimization methods, the proposed method provides better angle estimation performance. Simulation results are presented to verify the effectiveness and advantage of the proposed method.

In this paper, a novel approach for central angle estimation of coherently distributed targets that utilizes electric vector sensors in bistatic MIMO radar is proposed. First, the coherently distributed targets signal model in bistatic MIMO radar that equipped with electric vector sensors is reconstructed. The Hadamard product rotation invariance property of the coherently distributed targets' steering vectors is found to get the initial estimation of direction of departure (DOD). 1-D MUSIC is then used to estimate the accurate central angles of direction of arrival (DOA) and DOD. The proposed method can estimate the central angles of DOA and DOD efficiently and accurately without pairing even in the situation where the angular signal distribution functions are unknown. Our method has better performance than Guo's algorithm. Numerical results verify the improvement and performance of the proposed algorithm.

In this paper, we first propose ten new discrimination features of SAR images in the moving and stationary target acquisition and recognition (MSTAR) database. The Ada_MCBoost algorithm is then proposed to classify multiclass SAR targets. In the new algorithm, we introduce a novel large-margin loss function to design a multiclass classifier directly instead of decomposing the multiclass problem into a set of binary ones through the error-correcting output codes (ECOC) method. Finally, experiments show that the new features are helpful for SAR targets discrimination; the new algorithm had better recognition performance than three other contrast methods.

Instrumental variable (IV) filters designed for range sidelobe suppression in multiple-input multiple-output (MIMO) radar suffer from Doppler mismatch. This mismatch causes losses in peak response and increases sidelobe levels, which affect the performance of MIMO radar. In this paper, a novel method using the component-code processing prior to the IV filter design for MIMO radar is proposed. It not only compensates for the Doppler effects in the design of IV filter, but also offers more virtual sensors resulting in narrower beams with lower sidelobes. Simulation results are presented to verify the effectiveness of the method.

In cognitive radar systems (CRSs), target scattering coefficients (TSC) can be utilized to improve the performance of target identification and classification. This work considers the problem of TSC estimation for temporally correlated target. Multiple receive antennas are adopted to receive the echo waveforms, which are interfered by the signal-dependent clutter. Unlike existing estimation methods in time domain, a novel estimation method based on Kalman filtering (KF) is proposed in frequency domain to exploit the temporal TSC correlation, and reduce the complexity of subsequent waveform optimization. Additionally, to minimize the mean square error of estimated TSC at each KF iteration, in contrary to existing works, we directly model the design process as an optimization problem, which is non-convex and cannot be solved efficiently. Therefore, we propose a novel method, similar in some way to semi-definite programming (SDP), to convert the non-convex problem into a convex one. Simulation results demonstrate that the estimation performance can be significantly improved by the KF estimation with optimized waveform.

This paper presents a method of imaging a two-dimensional section of a walking person using multiple Doppler radar systems. Although each simple radar system consists of only two receivers, different radial speeds allow target positions to be separated and located. The signal received using each antenna is processed employing time-frequency analysis, which separates targets in the time-range-velocity space. This process is followed by a direction-of-arrival estimation employing interferometry. The data obtained using the multiple radar systems are integrated using a clustering algorithm and a target-tracking algorithm. Through realistic simulations, we demonstrate the remarkable performance of the proposed imaging method in generating a clear outline image of a human target in unknown motion.

To overcome the target-aspect sensitivity in radar high resolution range profile (HRRP) recognition, a novel method called Improved Kernel Distance Fuzzy C-means Clustering Method (IKDFCM) is proposed in this paper, which introduces kernel function into fuzzy c-means clustering and relaxes the constraint in the membership matrix. The new method finds the underlying geometric structure information hiding in HRRP target and uses it to overcome the HRRP target-aspect sensitivity. The relaxing of constraint in the membership matrix improves anti-noise performance and robustness of the algorithm. Finally, experiments on three kinds of ground HRRP target under different SNRs and four UCI datasets demonstrate the proposed method not only has better recognition accuracy but also more robust than the other three comparison methods.

A millimeter-wave radar receiver using a z-cut LiNbO3 optical modulator with orthogonal-gap-embedded patch-antennas on a low-k dielectric material is proposed. A millimeter-wave from a reflected radar signal can be received by the patch-antennas and converted directly to a lightwave through electro-optic modulation. A low-k dielectric material is used as a substrate for improving antenna gain. Additionally, an interaction length between millimeter-wave and lightwave electric fields becomes long. As a result, large modulation efficiency can be obtained, which is proportional to sensitivity of the millimeter-wave radar receiver. Optical millimeter-wave radar beam-forming can be obtained using the proposed device with meandering-gaps for controlling interaction between millimeter-wave and lightwave electric fields in electro-optic modulation. Analysis and experimentally demonstration of the proposed device are discussed and reported for 40GHz millimeter-wave bands. Optical millimeter-wave radar beam-forming in 2-D is also discussed.

Microwave imaging techniques, particularly for synthetic aperture radar (SAR), produce high-resolution terrain surface images regardless of the weather conditions. Focusing on a feature of complex SAR images, coherent change detection (CCD) approaches have been developed in recent decades that can detect invisible changes in the same regions by applying phase interferometry to pairs of complex SAR images. On the other hand, various techniques of polarimetric SAR (PolSAR) image analysis have been developed, since fully polarimetric data often include valuable information that cannot be obtained from single polarimetric observations. According to this background, various coherent change detection methods based on fully polarimetric data have been proposed. However, the detection accuracies of these methods often degrade in low signal-to-noise ratio (SNR) situations due to the lower signal levels of cross-polarized components compared with those of co-polarized ones. To overcome the problem mentioned above, this paper proposes a novel CCD method by introducing the Pauli decomposition and the weighting of component with their respective SNR. The experimental data obtained in anechoic chamber show that the proposed method significantly enhances the performance of the receiver operation characteristic (ROC) compared with that obtained by a conventional approach.

Vertical- and horizontal-polarization RCS of a dipole antenna was reduced using a switchable reflector. The switchable reflector can switch reflection level for the vertical-polarization and have absorption for the horizontal-polarization. The reflection level of the reflector for the vertical-polarization can be switched using pin diodes and the reflection for the horizontal-polarization can be reduced using resistor on the surface. The switchable reflector was designed to operate at 9 GHz and fabricated. The vertical-polarized reflection coefficient was switched -28 dB with OFF-state diodes and -0.7 dB with ON-state diodes, and horizontal-polarized one was less than -18 dB at 9 GHz. The reflector with ON-state diodes was applied to an antenna reflector of a dipole antenna and comparable radiation pattern to that with a metal reflector was obtained at 9 GHz. Moreover the reflector with OFF-state diodes was applied to the reflector of the dipole antenna and the RCS of the dipole antenna was reduced 18 dB for the vertical-polarization and 16 dB for the horizontal-polarization. Therefore the designed switchable reflector can contribute to antenna RCS reduction for dual-polarization at the operating frequency without degrading antenna performance.