The transient scattering characteristics of millimeter waves from a cylindrical object near a flat boundary were measured by the 50 GHz scatterometer to evaluate the multiple interactions of scattered waves with the objects and the boundary. Both perfectly conducting and dielectric cylinders are considered as a scattering object. The pulse intensities including waves scattered first from the object and then from the flat boundary or vice versa are shown to be significantly influenced by the distance from the object to the boundary, depending on the refractive index of the object. The observed higher order responses including the multiple scattering between the object and the boundary are also discussed. A preliminary comparison of the measured and calculated pulse responses for the perfectly conducting object is presented at slightly oblique incidence on a flat boundary.

Our goal in this paper is to provide a complete detection analysis for the OS processor along with OSGO and OSSO modified versions, for M postdetection integrated pulses when the operating environment is nonideal. Analytical results of performance are presented in both multiple-target situations and in regions of clutter power transitions. The primary and the secondary interfering targets are assumed to be fluctuating in accordance with the Swerling II target fluctuation model. As the number of noncoherently integrated pulses increases, lower threshold values and consequently better detection performances are obtained in both homogeneous and multiple target background models. However, the false alarm rate performance of OSSO-CFAR scheme at clutter edges is worsen with increasing the postdetection integrated pulses. As predicted, the OSGO-CFAR detector accommodates the presence of spurious targets in the reference window, given that their number is within its allowable range in each local window, and controls the rate of false alarm when the contents of the reference cells have clutter boundaries. The OSSO-CFAR scheme is useful in the situation where there is a cluster of radar targets amongst the estimation cells.

In this paper we propose a stepped-FM array radar system that can precisely estimate the target position by combining S- and T-MUSIC and adaptive beamforming. By adopting the adaptive beamformer as a preprocessor of T-MUSIC, the proposed system can uniquely determine the direction and distance of targets. In addition, the distance estimation precision is improved by introducing beamformer.

This paper attempts to use the polarimetric correlation coefficient for extraction of the polarimetric features of the urban areas and the natural distributed areas from Polarimetric Synthetic Aperture Radar (POLSAR) data. There is a possibility that the polarimetric correlation coefficient can reveal various scattering mechanisms of terrains based on the reflection symmetry property. In order to verify the capability of polarimetric correlation coefficient, we examined the behavior of this coefficient of the urban areas and the natural distributed areas with respect to the several polarimetric scattering models in the linear and circular polarization bases, and the difference of the polarimetric scattering characteristics between these two areas was derived. It was confirmed that the polarimetric correlation coefficient is useful to extract the polarimetric features from the actual L-band and X-band POLSAR data.

Various types of radars have been developed and used until now--such as Pulse, FM-CW, and Spread Spectrum. Additionally, we have proposed another type of radar which measures distances by using standing wave. We have named it as "Standing Wave Radar." It has a shorter minimum detectable range and higher accuracy compared to other types. However, the radar can not measure distances down to zero meters like other types of radars. Minimum detectable range of the standing wave radar depends on a usable frequency range. A wider frequency range is required if we need to measure shorter distances. Consequently, we propose a new method for measuring distances down to zero meters without expanding the frequency range. We use an analytic signal, which is a complex sinusoidal signal. The signal is obtained by observing the standing wave with multiple detectors. We calculate distances by Fourier transform of the analytic signal. Moreover, we verify the validity of our method by simulations based on numerical calculation. The results show that it is possible to measure distances down to zero meters. In our method, measurement errors are caused by deviations of position and gain of the detectors. They are around 10cm at the largest if the gain deviations are up to 1% and the position deviations are up to 6% of the spacing between the detectors. Prevalent radars still have a common defect that they can not measure distances from zero to several meters. We expect that the defect will be eliminated by putting our method into practical use.

A method for detecting shallowly buried landmines using sequential ground penetrating radar (GPR) data is presented. After removing a dominant coherent component arising from the ground surface reflection from the GPR data, three kinds of target features related to wave correlation, energy ratio, and signal arrival time are extracted. Since the detection problem treated here is reduced to a binary hypothesis test, an approach based on a likelihood ratio test is employed as a detection algorithm. In order to check the detection performance, a Monte Carlo simulation is carried out for data generated by a two-dimensional finite-difference time domain (FDTD) method. Results given in the form of receiver operating characteristic (ROC) curves show that good detection performance is obtained even for landmines buried at shallow depths under rough ground surfaces, where the responses from the landmines and that from the ground surface overlap in time.

This paper proposes a new angular measurement system to a moving target in the presence of clutter. We apply MUSIC (MUltiple SIgnal Classification) to the outputs of a Doppler filter bank consisting of quadrature mirror filter (QMF). The comparison between QMF and the short time Fourier transform (STFT) as a preprocessor of MUSIC is also discussed. DOA estimation performance by QMF-MUSIC is nearly equal to that of STFT-MUSIC. On the other hand, QMF-MUSIC overcomes STFT-MUSIC in the aspect of computational cost. In a specific example in this paper, the proposal QMF bank by Daubechies (4th order) wavelet requires 80% fewer the number of multiplications and 25% fewer the number of additions than the FFT-based STFT filter bank.

This paper describes a polarimetric feature extraction method from urban areas using the POLSAR (Polarimetric Synthetic Aperture Radar) data. The scattering characteristic of urban areas is different from that of natural distributed areas. The main point of difference is polarimetric correlation coefficient, because urban areas do not satisfy property of azimuth symmetry, Shh = Shv = 0. The decomposition technique based on azimuth symmetry can not be applied to urban areas. We propose a new model fit suitable for urban areas. The proposed model fit consists of odd-bounce, even-bounce and cross scattering models. These scattering models can represent the polarimetric backscatter from urban areas, and satisfy Shh 0 and Shv 0. In addition, the combination with the proposed model fit and the three component scattering model suited for natural distributed areas is examined. It is possible to apply the combined technique to POLSAR data which includes both urban areas and natural distributed areas. The combined technique is used for feature extraction of actual X-band POLSAR data acquired by Pi-SAR. It is shown that the proposed model fit is useful to extract polarimetric features from urban areas.

Inverse synthetic aperture radar (ISAR) is useful for automatic target recognition (ATR) because it can reconstruct a high resolution image of an observed target. In ISAR imaging, 3-dimensional reflectivity distribution of a target is projected to the plane defined by range axis and cross range axis. In order to recognize the observed target by using pattern matching, reference images of candidate targets must be adequately generated. However, that is difficult because the cross range axis, which depends on the target's unknown rotational motion, can not be determined precisely. In this paper, we propose a new algorithm to generate reference ISAR images of ship targets. In this algorithm, tracking data, Doppler width and the slope of the centerline of an ISAR target image are used to specify the cross range axis. The effectiveness of the proposed algorithm was evaluated by using simulated targets.

JERS-1 L-band SAR data can be, especially over urban areas affected by ground radar interferences. For most of the applications of the data the interferences should be suppressed. Notch filtering during image correlation process is one of the straightforward ways to do this. However, lower the threshold is, more signals from earth surface is eliminated. In this paper, a probability density function (PDF's) model of the ground radar interference signal is worked out from experimental data, and used for the suppression of interferences and the preservation of backscattered signals. The validity of the model is confirmed against real SAR data, and a general filter threshold--applicable to all JERS-1 SAR data--without any conditions is proposed.

The design, manufacture, and test results are presented for a 90polarization-rotating Van Atta array reflector with suppressed scattered field for the 1.27-GHz band. The reflector consists of 48 element antennas, half for horizontal polarization and half for vertical polarization. It receives a horizontally or vertically polarized wave and retransmits a vertically or horizontally polarized wave, respectively. The measured cross-polarized radar cross section of the reflector was 15.8 dBm2 on average, which agreed well with a theoretical prediction. Although the suppression of the scattered field was limited to about -20 dB relative to the retransmitted field, we could suppress more the scattered field by accurate positioning and careful characteristics adjustment of element antennas. Theoretical calculations showed that total phase errors of the element antennas including positioning errors and impedance characteristics errors have to be within 7.5to suppress the scattered field by less than -30 dB.

Imaging techniques for robots are important and meaningful in the near future. Pulse radar systems have a great potential for shape estimation and locationing of targets. They have an advantage that they can be used even in critical situations where optical techniques cannot be used. It is thus required to develop high-resolution imaging algorithms for pulse radar systems. High-resolution imaging algorithms utilize the carrier phase of received signals. However, their estimation accuracy suffers degradation due to phase rotation of the received signal because the phase depends on the shape of the target. In this paper, we propose a phase compensation algorithm for high-resolution pulse radar systems. The proposed algorithm works well with SEABED algorithm, which is a non-parametric algorithm of estimating target shapes based on a reversible transform. The theory is presented first and numerical simulation results follow. We show the estimation accuracy is remarkably improved without sacrificing the resolution using the proposed algorithm.

Identification of targets using sequential high range-resolution (HRR) radar signatures is studied. Classifiers are designed by using hidden Markov models (HMMs) to characterize the sequential information in multi-aspect HRR signatures. The higher-order moments together with the target dimension and the number of dominant wavefronts are used as features of the transient HRR waveforms. Classification results are presented for the ten-target MSTAR data set. The example results show that good classification performance and robustness are obtained, although the target features used here are very simple and compact compared with the complex HRR signatures.

Subcircuits designed for integrated silicon DCS1800/ PCS1900 base station receivers have been reconfigured into hybrid and single-chip Doppler radar transceivers. Radar chips have been fully integrated in 0.25 µm silicon CMOS and BiCMOS processes. These chips have been used to monitor heart and respiration activity without contact, and they have successfully detected heartbeat and respiration rate up to 1 m from the subject. This monitoring device may be useful in home monitoring, continuous monitoring, and physiological research.

Matching Pursuits (MP), a technique for signal decomposition using a dictionary of functions, is applied to ground penetrating radar (GPR) signals in order to remove noise and clutter included in the signals and to extract target responses. A wave-based dictionary composed of wavefronts and resonances is employed. Noise reduction performance and the removal of ground-surface reflection are evaluated through numerical simulations. The results show that the MP approach performs well and offers an effective method for feature extraction from GPR signals.

Modern spaceborne precision radar altimeters transmit radio signals of a spectrum bandwidth up to 300 MHz, but the bandwidth should be still increased for precise estimation of the roughness of the sea surface. In this research, the influence of the ionosphere on wideband radar signals is investigated and then it is shown that the signals are strongly influenced by the dispersive distortions in the atmosphere of the Earth even in Ku-band. Finally, the allowable bandwidth of a space borne precision radar altimeter signals is estimated, at which we could ignore the presence of these distortions.

Radars utilizing ultra-wide-band (UWB) pulses are attractive as an environment measurement method for various applications including household robots. Suitable filtering is essential for accurate ranging, which requires an accurate waveform estimation. This paper presents a high-resolution algorithm of estimating target location and scattered waveforms, whose accuracies are interdependent. The technique relies on iterative improvements of estimated waveforms. Description of the algorithm is followed by statistical simulation examples. The performance of the algorithm is contrasted with conventional ones and statistical bounds. Results indicate that our proposed algorithm has a remarkable performance, which is close to the theoretical limit. Next, we clarify the problem of applying HCT to multiple targets. HCT for multiple targets can not be used as an estimated waveform because of interference waves from other targets. We propose an interference suppression algorithm based on a neural network, and show an application example of the algorithm.

Environment measurement is an important issue for various applications including household robots. Pulse radars are promising candidates in a near future. Estimating target shapes using waveform data, which we obtain by scanning an omni-directional antenna, is known as one of ill-posed inverse problems. Parametric methods such as Model-fitting method have problems concerning calculation time and stability. We propose a non-parametric algorithm for high-resolution estimation of target shapes in order to solve the problems of parametric algorithms.

This paper presents an imaging technique using the MUSIC algorithm to localize cylindrical reflectors in cross-borehole radar arrangements. Tomographic measurement, in which a transmitting and a receiving antenna are individually moved in separate boreholes, can be considered as a combination of a transmitting and a receiving array. A decorrelation technique with the transmitting array, which has been proposed for imaging point reflectors, is applied for imaging cylindrical reflectors using the MUSIC algorithm. Simulated and experimental results are shown to verify the validity of this algorithm for cylindrical targets. We analyze the evaluation error caused by the increase in the radius of the cylinder.

In this letter, the concept of cross-entropy is introduced for unsupervised polarimetric synthetic aperture radar (SAR) image classification. The difference between two scatterers is decomposed into three parts, i.e., the difference of average scattering characteristic, the difference of scattering randomness and the difference of scattering matrix span. All these three parts are expressed in cross-entropy formats. The minimum cross-entropy principle is adopted to make classification decision. It works well in unsupervised terrain classification with a NASA/JPL AIRSAR image.