Locality of high frequency electromagnetic scattering phenomena is embodied and imported to the Method of Moments (MoM) to reduce computational load. The proposed method solves currents on small areas only around inner and edge stationary phase points (SPPs) on the scatterer surfaces. The range of MoM area is explicitly specified in terms of Fresnel zone number as a function of frequency, source and observer positions. Based upon this criterion, scatterer of arbitrary size and shape can be solved with almost frequency independent number of unknowns. In some special cases like focusing systems, locality disappears and the method reduces to the standard MoM. The hybrid method called PO-MoM is complementarily introduced to cope with these cases, where Fresnel zone number with analogous but different definition is used. The selective use of Local-MoM and PO-MoM provides frequency insensitive number of unknowns for general combination of source and observation points. Numerical examples of RCS calculation for two dimensional flat and curved surfaces are presented to demonstrate the accuracy and reduction of unknowns of this method. The Fresnel zone, introduced in the scattering analysis for the first time, is a useful indicator of the locality or the boundary for MoM areas.

ICA (Independent Component Analysis) has a remarkable capability of separating mixtures of stochastic random signals. However, we often face problems of separating mixtures of deterministic signals, especially sinusoidal signals, in some applications such as radar systems and communication systems. One may ask if ICA is effective for deterministic signals. In this paper, we analyze the basic performance of ICA in separating mixtures of complex sinusoidal signals, which utilizes the fourth order cumulant as a criterion of independency of signals. We theoretically show that ICA can separate mixtures of deterministic sinusoidal signals. Then, we conduct computer simulations and radio experiments with a linear array antenna to confirm the theoretical result. We will show that ICA is successful in separating mixtures of sinusoidal signals with frequency difference less than FFT resolution and with DOA (Direction of Arrival) difference less than Rayleigh criterion.

In this paper, the inherent problem of the Hough transform when applied to search radars is considered. This problem makes the detection probability of a target depend on the length of the target line in the data space in addition to the received SNR from it. It is shown that this problem results in a non-uniform distribution of noise power in the parameter space. In other words, noise power in some regions of the parameter space is greater than in others. Therefore, the detection probability of the targets covered by these regions will decrease. Our solution is to modify the Hough detector to remove the problem. This modification uses non-uniform quantization in the parameter space based on the Maximum Entropy Quantization method. The details of implementing the modified Hough detector in a search radar are presented according to this quantization method. Then, it is shown that by using this method the detection performance of the target will not depend on its length in the data space. The performance of the modified Hough detector is also compared with the standard Hough detector by considering their probability of detection and probability of false alarm. This comparison shows the performance improvement of the modified detector.

A hot clutter mitigation algorithm based on Subbanding and Space Fast-time Adaptive Processing (Fast-time STAP) for Multi-channel Synthetic Aperture Radar (MSAR) is analyzed, and is compared with the method based on just fast-time STAP. Simulation results demonstrate that the method based on subbanding and fast-time STAP performs better than the method based on just fast-time STAP in hot clutter mitigation for MSAR.

The importance of respiratory monitoring systems during sleep have increased due to early diagnosis of sleep apnea syndrome (SAS) in the home. This paper presents a simple respiratory monitoring system suitable for home use having 3D ranging of targets. The range resolution and azimuth resolution are obtained by a stepped frequency transmitting signal and MIMO arrays with preferred pair M-sequence codes doubly modulating in transmission and reception, respectively. Due to the use of these codes, Gold sequence codes corresponding to all the antenna combinations are equivalently modulated in receiver. The signal to interchannel interference ratio of the reconstructed image is evaluated by numerical simulations. The results of experiments on a developed prototype 3D-MIMO radar system show that this system can extract only the motion of respiration of a human subject 2 m apart from a metallic rotatable reflector. Moreover, it is found that this system can successfully measure the respiration information of sleeping human subjects for 96.6 percent of the whole measurement time except for instances of large posture change.

A new method of segmentation for Synthetic Aperture Radar (SAR) images using the skewness wavelet energy has been presented. The skewness is the third order cumulant which measures the local texture along the region-based active contour. Nonlinearity in intensity inhomogeneities often occur in SAR images due to the speckle noise. In this paper we propose a region-based active contour model that is able to use the intensity information in local regions and to cope with the speckle noise and nonlinear intensity inhomogeneity of SAR images. We use a wavelet coefficients energy distribution to analyze the SAR image texture in each sub-band. A fitting energy called skewness wavelet energy is defined in terms of a contour and a functional so that, the regions and their interfaces will be modeled by level set functions. A functional relationship has been calculated on these level sets in terms of the third order cumulant, from which an energy minimization is derived. Minimizing the calculated functions derives the optimal segmentation based on the texture definitions. The results of the implemented algorithm on the test images from the Radarsat SAR images of agricultural and urban regions show a desirable performance of the proposed method.

Future high-resolution short-range automotive radar will have a higher false alarm probability than the conventional low-resolution radar has. In a high-resolution radar, the reception signal becomes sensitive to the difference between intended and unintended objects. However, automotive radars must distinguish targets from background objects that are the same order of size; it leads to an increase in the false alarm probability. In this paper, a CFAR circuit for obtaining the target mean power, as well as the background mean power, is proposed to reduce the false alarm probability for high-resolution radars working in automotive environments. The proposed method is analytically evaluated with use of the characteristic function method. Spatial correlation is also considered in the evaluation, because the sizes of the both target and background objects approach the dimension of several range cells. Result showed the proposed CFAR with use of two alongside range cells could reduce the ratio of 6.4 dB for an example of an automotive situation.

The Hough transform is known to be an effective technique for target detection and track initiation in search radars. However, most papers have focused on the simplistic applications of this technique which consider a 2-D data space for the Hough transform. In this paper, a new method based on xthe Hough transform is introduced for detecting targets in a 3-D data space. The data space is constructed from returned surveillance radar signal using the range and bearing information of several successive scans. This information is mapped into a 3-D x-y-t Cartesian data space. Targets are modeled with four parameters in this data space. The proposed 3-D Hough detector is then used to detect the existent targets in the 3-D surveillance space by mapping the returned signal of the radar from the data space to the parameter space. This detector, which is constructed of two detection stages, integrates the returned data of each target non-coherently along its 3-D trajectory in one parameter space cell related to this target. Hence, the detection performance will improve. The effectiveness of the new 3-D Hough detector is demonstrated through deriving the detection statistics analytically and comparing the results with those of several comprehensive simulations. The performance improvement of this detector is shown by comparing its detection range with the conventional detector. The proposed detector is also evaluated with real radar data and its efficiency is confirmed.

A method for reducing ground clutter contribution from ground penetrating radar (GPR) data is proposed for discrimination of landmines located in shallow depth. The algorithm of this method is based on the Matching Pursuit (MP) that is a technique for non-orthogonal signal decomposition using dictionary of functions. As the dictionary of function, a wave-based dictionary constructed by taking account of scattering mechanisms of electromagnetic (EM) wave by rough surfaces is employed. Through numerical simulations, performance of ground clutter reduction is evaluated. The results show that the proposed method has good performance and is effective for GPR data preprocessing for discrimination of shallowly buried landmines.

The final goal of the present project is to develop a ship detection and identification system by integrating spaceborne synthetic aperture radar (SAR), ground-based maritime radar and automatic identification system (AIS); and this article presents the results of the first phase experiments and current status toward achieving this goal. The data acquired by the Phased Array L-band SAR (PALSAR) on board of the Advanced Land Observing Satellite (ALOS) were used as SAR data, and X-band maritime radar including AIS were used as a ground-based system. The work is divided into two experimental phases. The first phase is to examine the ability of PALSAR to detect ships whose sizes are comparable with the SAR resolution cells, and the second is to incorporate the PALSAR data with those acquired by the ground-based radar with AIS. For the experiments in the first phase, we deployed three small fishing boats whose lengths ranged from approximately 8 m to 15 m in the Tosa Bay in Kochi, Japan in 2006. The experiments were carried out for four observation PALSAR modes: FBS (Fine Beam Single) 34.3, FBS 21.5, FBD (Fine Beam Double) 41.5, and PLR (PoLaRimetric) 20.5, where the numbers in each modes represent the off-nadir angles. For extracting the boats from the PALSAR images, five algorithms were considered, including amplitude-based, CFAR (Constant False Alarm Rate), MLCC (Multi-Look Cross-Correlation), CCF (Cross-Correlation Function) of HH- and HV-polarization amplitudes, and polarimetric analyses. This paper summarizes the results of the first phase experiments; the summary of the integrated system in the second phase will be reported in the near future.

The optimization of polarimetric contrast enhancement (OPCE) is a widely used method for maximizing the received power ratio of a desired target versus an undesired target (clutter). In this letter, a new model of the OPCE is proposed based on the Fisher criterion. By introducing the well known two-class problem of linear discriminant analysis (LDA), the proposed model is to enlarge the normalized distance of mean value between the target and the clutter. In addition, a cross-iterative numerical method is proposed for solving the optimization with a quadratic constraint. Experimental results with the polarimetric SAR (POLSAR) data demonstrate the effectiveness of the proposed method.

An inversion method of bridge height over water by polarimetric synthetic aperture radar (SAR) is developed. A geometric ray description to illustrate scattering mechanism of a bridge over water surface is identified by polarimetric image analysis. Using the mapping and projecting algorithm, a polarimetric SAR image of a bridge model is first simulated and shows that scattering from a bridge over water can be identified by three strip lines corresponding to single-, double-, and triple-order scattering, respectively. A set of polarimetric parameters based on the de-orientation theory is applied to analysis of three types scattering, and the thinning-clustering algorithm and Hough transform are then employed to locate the image positions of these strip lines. These lines are used to invert the bridge height. Fully polarimetric image data of airborne Pi-SAR at X-band are applied to inversion of the height and width of the Naruto Bridge in Japan. Based on the same principle, this approach is also applicable to spaceborne ALOSPALSAR single-polarization data of the Eastern Ocean Bridge in China. The results show good feasibility to realize the bridge height inversion.

A composite patch array antenna with built-in polarizer has been developed to reduce road clutter noise by 20 dB for 76 GHz automotive radars. A polarizer is placed in front of Tx and Rx patch arrays within their reactive near-field region to suppress cross-polarized sidelobe radiation from their feeding lines while maintaining a low-profile characteristic with 4 mm thickness. Additional metal-lined absorbers within the composite antenna structure, while terminating cross-polarized waves undesirably excited by the patch arrays, also serve as miniature clutter plates to further reduce sidelobes toward the road surface. The resultant composite antenna achieved sidelobe levels of -45 dB, a 20 dB improvement over standard patch arrays, at elevation angles close to 90.

External interferences can severely degrade the performance of an Over-the-horizon radar (OTHR), so suppression of external interferences in strong clutter environment is the prerequisite for the target detection. The traditional suppression solutions usually began with clutter suppression in either time or frequency domain, followed by the interference detection and suppression. Based on this traditional solution, this paper proposes a method characterized by joint clutter suppression and interference detection: by analyzing eigenvalues in a short-time moving window centered at different time position, clutter is suppressed by discarding the maximum three eigenvalues at every time position and meanwhile detection is achieved by analyzing the remained eigenvalues at different position. Then, restoration is achieved by forward-backward linear prediction using interference-free data surrounding the interference position. In the numeric computation, the eigenvalue decomposition (EVD) is replaced by singular values decomposition (SVD) based on the equivalence of these two processing. Data processing and experimental results show its efficiency of noise floor falling down about 10-20 dB.

Conventional radar imaging systems use Fourier transform for image formation, but due to the target's complicated motion the Doppler spectrum is time-varying and thus the reconstructed image becomes blurred even after applying standard motion compensation algorithms. Therefore, sophisticated algorithms such as polar reformatting are usually employed to produce clear images. Alternatively, Joint Time-Frequency (JTF) analysis can be used for image formation which produces clear image without using polar reformatting algorithm. In this paper, a new JTF-based method is proposed for image formation in inverse synthetic aperture radars (ISAR). This method uses minimum entropy criterion for optimum parameter adjustment of JTF algorithms. Short Time Fourier Transform (STFT) and Fractional Fourier Transform (FrFT) are applied as JTF for time-varying Doppler spectrum analysis. Both the width of Gaussian window of STFT and the order of FrFT, α, are adjusted using minimum entropy as local and total measures. Furthermore, a new statistical parameter, called normalized correlation, is defined for comparison of images reconstructed by different methods. Simulation results show that α-order FrFT with local adjustment has much better performance than the other methods in this category even in low SNR.

Recently, it has become important to rapidly detect human subjects buried under collapsed houses, rubble and soil due to earthquakes and avalanches to reduce the casualties in a disaster. Such detection systems have already been developed as one kind of microwave displacement sensors that are based on phase difference generated by the motion of the subject's breast. Because almost all the systems consist of single transmitter and receiver pair, it is difficult to rapidly scan a wide area. In this paper, we propose a single-frequency multistatic radar system to detect breathing human subjects which exist in the area surrounded by the transmitting and receiving array. The vibrating targets can be localized by the MUSIC algorithm with the complex amplitude in the Doppler frequency. This algorithm is validated by the simulated signals synthesized with a rigorous solution of a dielectric spherical target model. We show experimental 3D localization results using a developed multistatic Doppler radar system around 250 MHz.

We have developed a high-temperature superconducting (HTS) filter with narrow bandwidth characteristic for receiver of weather radar in order to reduce interference between adjacent radar channels. To realize a filter with which a narrow bandwidth and low insertion loss are compatible, resonators with high unloaded Q (Qu) value are required. Hairpin microstrip resonators with 1.5 times wavelength were adopted to suppress the radiation loss and achieve a high Qu value. The developed HTS filter has 8-pole quasi-elliptic function response for sharp skirt characteristic. The measured frequency response of the developed filter shows center frequency of 5370 MHz, insertion loss of 2.04 dB and maximum return loss of 15 dB, which agrees with the designed responses.

The purpose of this study is the real-time estimation of Doppler spectral moments for precipitation in the presence of ground clutter overlap. The proposed method is a frequency domain approach that uses a Gaussian model both to remove clutter spectrum and to estimate weather spectrum. The main advantage of this method is that it does not use processes like several fitting procedures and enables to estimate profiles of precipitation in a short processing time. Therefore this method is efficient for real-time radar observation with high range and time resolution. The performance of this method is evaluated based on simulation data and the observation data acquired by the Ku-band broad band radar (BBR) [1].

The complex-valued self-organizing map (CSOM) realizes an adaptive distinction between plastic landmines and other objects in landmine visualization systems. However, when the spatial resolution in electromagnetic-wave measurement is not sufficiently high, the distinction sometimes fails. To solve this problem, in this paper, we propose two techniques to enhance the visualization ability. One is the utilization of SOM-space topology in the CSOM adaptive classification. The other is a novel feature extraction method paying attention to local correlation in the frequency domain. In experimental results, we find that these two techniques significantly improve the visualization performance. The local-correlation method contributes also to the reduction of the number of tuning parameters in the CSOM classification.

Ultra wideband (UWB) radar cross sections (RCSs) of several targets have been measured using various combinations of transmitting and receiving linear polarizations (V-V, H-H, and +45- -45) with a view to obtaining information on the design of vehicular short-range radars. This paper reports the UWB RCSs (σLR and σLL) of a typical passenger automobile using two circular polarization combinations (L and R denote left and right circular polarizations). The wideband measurements were carried out with use of a vector network analyzer by sweeping the frequency from 24.5 to 28.8 GHz in a radio anechoic chamber. The UWB RCSs were derived by integrating the received power in the frequency domain. Similar to the linear polarization results, fluctuations of the RCSs were smaller in the UWB than in narrowband for both L-R and L-L, because the ultra-wide bandwidth cancels out RCS plunges caused by narrowband interference among reflected waves from various facets of the target. The median of (σLR- σLL) was 2 dB, while the median of (σHH - σ+45 - -45) or (σVV-σ+45- -45) was 6 dB. This is because the body of the automobile comprises a number of smaller scattering objects yielding σ LL, either similar to the corner reflectors or asymmetrical to the radar boresight. Frequency-domain responses showed a number of notches caused by the interference between numerous reflecting waves having power levels of a similar order and different round-trip path lengths. Some representative reflective parts of the automobile were identified through analyses of time-domain responses.