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.

The RO (Ring-Oscillator)-based sensor is one of easily-implementable variation sensors, but for decomposing the observed variability into multiple unique device-parameter variations, a large number of ROs with different structures and sensitivities to device-parameters is required. This paper proposes an area efficient device parameter estimation method with sensitivity-configurable ring oscillator (RO). This sensitivity-configurable RO has a number of configurations and the proposed method exploits this property for reducing sensor area and/or improving estimation accuracy. The proposed method selects multiple sets of sensitivity configurations, obtains multiple estimates and computes the average of them for accuracy improvement exploiting an averaging effect. Experimental results with a 32-nm predictive technology model show that the proposed averaging with multiple estimates can reduce the estimation error by 49% or reduce the sensor area by 75% while keeping the accuracy. Compared to previous work with iterative estimation, 23% accuracy improvement is achieved.

This paper deals with the inefficient channel utilization of wireless LANs that use rate adaptation. Recently, wireless LANs are being utilized in various environments. However, inefficient channel utilization is still a serious problem. The effective solutions include to decrease the frequency of packet loss and to transmit packets at a higher rate. While the backoff algorithm in IEEE 802.11 avoids only the packet loss caused by collision, other previous works tackle the packet loss caused by channel fading by means of transmission at a lower rate. This approach is called rate adaptation and a simple rate adaptation scheme is widely diffused in commercial 802.11 wireless LAN devices. However, utilizing lower transmission rate degrades transmission efficiency because the channel is occupied for a longer time. In this paper, decreasing transmission rate is avoided with novel transmission scheduling. Specifically, the proposed scheduling interrupts packet transmission to receiver stations under fading channel condition until the condition improves. Instead, other packets to other stations are transmitted in advance. To implement this proposed scheduling, only access points (APs) need to be modified. Hence, legacy wireless stations can benefit from higher communication bandwidth simply by introducing the modified APs. Moreover, although wireless stations must also be modified, an extended RTS/CTS handshake is also proposed to quickly detect the improvement of channel condition and to minimize the wasted time even if fading loss occurs. Here, wireless stations must also be modified to adopt the extended RTS/CTS handshake but further bandwidth increase is achievable. Evaluation results demonstrate that network throughput is improved without degrading the throughput fairness among receiver stations and packet transfer delay of interrupted stations.

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.

Gentle AdaBoost is widely used in object detection and pattern recognition due to its efficiency and stability. To focus on instances with small margins, Gentle AdaBoost assigns larger weights to these instances during the training. However, misclassification of small-margin instances can still occur, which will cause the weights of these instances to become larger and larger. Eventually, several large-weight instances might dominate the whole data distribution, encouraging Gentle AdaBoost to choose weak hypotheses that fit only these instances in the late training phase. This phenomenon, known as “classifier distortion”, degrades the generalization error and can easily lead to overfitting since the deviation of all selected weak hypotheses is increased by the late-selected ones. To solve this problem, we propose a new variant which we call “Penalized AdaBoost”. In each iteration, our approach not only penalizes the misclassification of instances with small margins but also restrains the weight increase for instances with minimal margins. Our method performs better than Gentle AdaBoost because it avoids the “classifier distortion” effectively. Experiments show that our method achieves far lower generalization errors and a similar training speed compared with Gentle AdaBoost.

This study improves the compression efficiency of Lee's colorization-based coding framework by introducing a novel colorization matrix construction and an adaptive color conversion. Colorization-based coding methods reconstruct color components in the decoder by colorization, which adds color to a base component (a grayscale image) using scant color information. The colorization process can be expressed as a linear combination of a few column vectors of a colorization matrix. Thus it is important for colorization-based coding to make a colorization matrix whose column vectors effectively approximate color components. To make a colorization matrix, Lee's colorization-based coding framework first obtains a base and color components by RGB-YCbCr color conversion, and then performs a segmentation method on the base component. Finally, the entries of a colorization matrix are created using the segmentation results. To improve compression efficiency on this framework, we construct a colorization matrix based on a correlation of base-color components. Furthermore, we embed an edge-preserving smoothing filtering process into the colorization matrix to reduce artifacts. To achieve more improvement, our method uses adaptive color conversion instead of RGB-YCbCr color conversion. Our proposed color conversion maximizes the sum of the local variance of a base component, which resulted in increment of the difference of intensities at region boundaries. Since segmentation methods partition images based on the difference, our adaptive color conversion leads to better segmentation results. Experiments showed that our method has higher compression efficiency compared with the conventional 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.

In this letter, a new subband adaptive filter (SAF) which is robust against impulsive noise in system identification is presented. To address the vulnerability of adaptive filters based on the L2-norm optimization criterion to impulsive noise, the robust SAF (R-SAF) comes from the L1-norm optimization criterion with a constraint on the energy of the weight update. Minimizing L1-norm of the a posteriori error in each subband with a constraint on minimum disturbance gives rise to robustness against impulsive noise and the capable convergence performance. Simulation results clearly demonstrate that the proposal, R-SAF, outperforms the classical adaptive filtering algorithms when impulsive noise as well as background noise exist.

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.

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.

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.

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.

This paper proposes a rate adaptation scheme (RAS) for a wireless local area network (WLAN) station powered with microwave power transmission (MPT). A WLAN station attempting to transmit data frames when exposed to microwave radiation for MPT, experiences a reduction in the physical (PHY) layer data rate because frames are lost even when the carrier sense mechanism is used. The key idea of the proposed scheme is to utilize the output of the rectenna used for receiving microwave power. Using rectenna output, a WLAN station based on the proposed scheme assesses whether the station is exposed to microwave radiation for MPT. Then, using historical data corresponding to the assessment result, the station selects an appropriate PHY data rate. The historical data are obtained from previous transmission results, e.g., historical data pertaining to the data frame loss ratio. The proposed scheme was implemented and verified through an experiment. Experimental results showed that the proposed scheme prevents the reduction in the PHY data rate, which is caused by the use of historical data stored in a single memory. Thus, the proposed scheme leads to an improvement in the WLAN throughput.

We propose simple but efficient encapsulation architecture. In the architecture, clients can better decode Extensible Markup Language (XML) based service information for TV contents with schema digest. Our experimental results show the superiority of the proposed architecture by comparing the compression ratios and decoding times of the proposed architecture and the existing architectures.

This paper proposes the concept of adaptive multi-rate (AMR), which jointly employs switching between two links and adaptive rate on each link, for hybrid free-space optical/radio-frequency (FSO/RF) systems. Moreover, we present the cross-layer design of AMR switching, which is based on both the physical and link layers with an automatic-repeat request (ARQ) scheme. We develop an analytical framework based on a Markov chain model for system performance analysis. System performance metrics, including frame-error rate, goodput and link switching probability, are analytically studied over fading channels. Numerical results quantitatively show how the proposal significantly outperforms conventional ones with physical layer-based design and/or fixed-rate switching operation.

This paper investigates active period selection for cluster-based WSNs employing traffic adaptive IEEE 802.15.4 beacon enabled medium access control (MAC) under spatial non-uniform traffic and cluster mobility environments, and proposes a simple autonomous distributed superframe duration (SD) selection scheme to enhance network performance under such environments. The proposed scheme autonomously selects an active SD at each cluster head (CH) by using beacon reception power monitoring (conducted in distributed control manner) and also introduces a beacon status notice from sensor nodes (SNs) to their parent CHs in order to prevent unnecessary SD selection at CHs. Moreover, SD reuse mechanism and joint operation with previously proposed distributed backoff mechanism are proposed for the proposed SD selection scheme to further enhance the network performance. The results of computer simulation show that the proposed scheme can improve both the transmission and power efficiency performance of cluster-based WSNs under spatial non-uniform traffic and cluster mobility environments.

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.

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.