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, 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 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.

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.

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.

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.

Weak target detection is a key problem in passive bistatic radar (PBR). Track-before-detect (TBD) is an effective solution which has drawn much attention recently. However, TBD has not been fully developed in PBR. In this paper, the transition function and the selection of parameters in dynamic programming are analyzed in PBR. Then a novel processing scheme of dynamic programming based TBD is proposed to reduce the computation complexity without severely decreasing the detection performance. Discussions including complexity, detection performance, threshold determination, selection of parameters and detection of multitarget, are presented in detail. The new method can provide fast implementation with only a slight performance penalty. In addition, good multitarget detection performance can be achieved by using this method. Simulations are carried out to present the performance of the proposed processing scheme.

In automotive frequency modulated continuous wave (FMCW) radar based on multiple ramps with different slope, an effective pairing algorithm is required to simultaneously detect the target range and velocity. That is, as finding beat-frequencies intersecting at a single point of the range-Doppler map, we extract the range and velocity of a target. Unlike the ideal case, however, in a real radar system, even though multiple beat frequencies are originated from the same target, these beat frequencies have many different intersection values, resulting in mismatch pairing during the pairing step. Moreover, this problem also reduces the detection accuracy and the radar detection performance. In this study, we found that mismatch pairing is caused by the round-off errors of the range-beat frequency and Doppler frequency, as well as their various combinations in the discrete frequency domain. We also investigated the effect of mismatch pairing on detection performance, and proposed a new approach to minimize this problem. First, we propose integer and half-integer frequency position-based pairing method during extraction of the range and Doppler frequencies in each ramp to increase detection accuracy. Second, we propose a window-based pairing method to identify the same target from range-Doppler frequencies extracted in the first step. We also find the appropriate window size to overcome pairing mismatch. Finally, we propose the method to obtain a higher accuracy of range and velocity by weighting the values determined in one window. To verify the detection performance of the proposed method by comparison with the typical method, simulations were conducted. Then, in a real field test using the developed radar prototype, the detection probability of the proposed algorithm showed more than 60% improvement in comparison with the conventional method.

We focus on forward-looking radar network systems for automotive usages. By using multiple radars, the radar network systems will achieve reliable detection and wide observation area. The forward-looking systems by cameras are famous. In order to realize more reliable safety, the cameras had better be used with other sensing devices such as the radar network. In the radar network, processing of the data, which is derived from the multiple receivers, is important because the processing decides the estimation performance. In this paper, we will introduce our estimation algorithm which focuses on target existence probability and virtual receivers. The performance will be evaluated by simulated targets which are both single point model and 3D target model.

A monolithic frequency synthesizer with wide tuning range, low phase noise and spurs was realized in 0.13,$mu$m CMOS technology. It consists of an analog PLL, a harmonic-rejection mixer and injection-locked frequency doublers to cover the whole 6--18,GHz frequency range. To achieve a low phase noise performance, a sub-sampling PLL with non-dividers was employed. The synthesizer can achieve phase noise $-$113.7,dBc/Hz@100,kHz in the best case and the reference spur is below $-$60,dBc. The core of the synthesizer consumes about 110,mA*1.2,V.

In recent years, there are many collision accidents between vehicles due to human errors. As one of countermeasures against the collision accidents, automotive radar systems have been supporting vehicle drivers. By the automotive radar mounted on the vehicle, it is possible to recognize the situation around the vehicle. The ranging with automotive infrared laser radar is very accurate, and able to understand the object existence in the observation around the vehicle. However, in order to grasp the situation around the vehicle, it is necessary to be aware of the attribute of the detected object. The information obtained by the automotive radar vehicle is only the direction and the distance of the object. Thus, the recognition of the attribute of the detected object is very difficult. In this paper, we propose a novel vehicle information acquisition method by using 2D reflector code. Through experiments, we show that the proposed method is able to detect 2D reflector code and is effective for vehicle information acquisition.

Radar systems using ultra-wideband (UWB) signals have definitive advantages in high range resolution. These are suitable for accurate 3-dimensional (3-D) sensing by rescue robots operating in disaster zone settings, where optical sensing is not applicable because of thick smog or high-density gas. For such applications, where no a priori information of target shape and position is given, an accurate method for 3-D imaging and motion estimation is strongly required for effective target recognition. In addressing this issue, we have already proposed a non-parametric 2-dimensional (2-D) imaging method for a target with arbitrary target shape and motion including rotation and translation being tracked using a multi-static radar system. This is based on matching target boundary points obtained using the range points migration (RPM) method extended to the multi-static radar system. Whereas this method accomplishes accurate imaging and motion estimation for single targets, accuracy is degraded severely for multiple targets, due to interference effects. For a solution of this difficulty, this paper proposes a method based on a novel matching scheme using not only target points but also normal vectors on the target boundary estimated by the Envelope method; interference effects are effectively suppressed when incorporating the RPM approach. Results from numerical simulations for both 2-D and 3-D models show that the proposed method simultaneously achieves accurate target imaging and motion tracking, even for multiple moving targets.

The combined linear frequency modulation continuous wave (LFMCW) and inverse synthetic aperture radar (ISAR) can be used for imaging long-distance targets because of its long-distance and high resolution imaging abilities. In this paper, we find and study the dechirp distortion phenomenon (DDP) for imaging long-distance targets by a dechirp-on-receive LFMCW radar. If the targets are very far from the radar, the maximum delay-time is not much smaller than a single sweep duration, and the dechirp distortion is triggered since the distance of the target is unknown in a LFMCW-ISAR system. DDP cannot be ignored in long-distance imaging because double images of a target appear in the frequency domain, which reduces resolution and degrades image quality. A novel LFMCW-ISAR signal model is established to analyze DDP and its negative effects on long-distance target imaging. Using the proportionately distributed energy of double images, the authors propose a method to correct dechirp distortion. In addition, the applicable scope of the proposed method is also discussed. Simulation results validate the theoretical analysis and the effectiveness of the proposed method.

Soil moisture retrieval from polarimetric synthetic aperture radar (SAR) imagery over forested terrain is quite a challenging problem, because the radar backscatter is affected by not only the moisture content, but also by large vegetation structures such as the trunks and branches. Although a large number of algorithms which exploit radar backscatter to infer soil moisture have been developed, most of them are limited to the case of bare soil or little vegetation cover that an incident wave can easily reach the soil surface without serious disturbance. However, natural land surfaces are rarely free from vegetation, and the disturbance in radar backscatter must be properly compensated to achieve accurate soil moisture measurement in a diversity of terrain surfaces. In this paper, a simple polarimetric parameter, co-polarized backscattering ratio, is shown to be a criterion to infer moisture content of forested terrain, from both a theoretical forest scattering simulation and an appropriate experimental validation under well-controlled condition. Though modeling of forested terrain requires a number of scattering mechanisms to be taken into account, it is essential to isolate them one by one to better understand how soil moisture affects a specific and principal scattering component. For this purpose, we consider a simplified microwave scattering model for forested terrain, which consists of a cloud of dielectric cylinders as a representative of trunks, vertically stood on a flat dielectric soil surface. This simplified model can be considered a simple boreal forest model, and it is revealed that the co-polarization ratio in the ground-trunk double-bounce backscattering can be an useful index to monitor the relative variation in the moisture content of the boreal forest.

Recently, radar resource management of multifunction radar is a challenging issue in electronically scanned array radar technology. This paper deals with radar beam scheduling, which is a core issue of radar resource management. This paper proposed stochastic scheduler algorithm using Simulated Annealing (SA) and Hybrid scheduler algorithm which automatically selects two different types of schedulers according to the radar load: Rule based scheduler using modified Butler algorithm for underload situations and SA based scheduler for overload situations. The proposed algorithms are evaluated in terms of scheduling latency, the number of scheduled tasks, and time complexity. The simulation results show that the performance of rule based scheduler is seriously degraded in overload situation. However, SA based scheduler and Hybrid scheduler have graceful performance degradation in overload situation. Compared with rule based scheduler, SA based scheduler and Hybrid scheduler can schedule many more tasks on time for the same operation duration in the overload situation. Even though their time complex is relatively high, it can be applied to real applications if the parameters are properly controlled. Especially, Hybrid scheduler has an advantage of low time complexity with good performance.

Ultra-wide band (UWB) radar has a great advantage for range resolution, and is suitable for 3-dimensional (3-D) imaging sensor, such as for rescue robots or surveillance systems, where an accurate 3-dimensional measurement, impervious to optical environments, is indispensable. However, in indoor sensing situations, an available aperture size is severely limited by obstacles such as collapsed furniture or rubles. Thus, an estimated region of target image often becomes too small to identify whether it is a human body or other object. To address this issue, we previously proposed the image expansion method based on the ellipse extrapolation, where the fitting space is converted from real space to data space defined by range points to enhance the extrapolation accuracy. Although this method achieves an accurate image expansion for some cases, by exploiting the feature of the efficient imaging method as range points migration (RPM), there are still many cases, where it cannot maintain sufficient extrapolation accuracy because it only employs the single scattered component for imaging. For more accurate extrapolation, this paper extends the above image expansion method by exploiting double-scattered signals between the target and the wall in an indoor environment. The results from numerical simulation validate that the proposed method significantly expands the extrapolated region for multiple elliptical objects, compared with that obtained using only single scattered signal.