Ultra-wideband (UWB) pulse radars have a definite advantage in high-range resolution imaging, and are suitable for short-range measurements, particularly at disaster sites or security scenes where optical sensors are rarely suitable because of dust or strong backlighting. Although we have already proposed an accurate imaging algorithm called Range Points Migration (RPM), its reconstructible area is too small to identify the shape of an object if it is far from the radar and the size of the aperture is inadequate. To resolve this problem, this paper proposes a novel image expansion method based on ellipse extrapolation; it enhances extrapolation accuracy by deriving direct estimates of the observed range points distributed in the data space. Numerical validation shows that the proposed method accurately extrapolates part of the target boundary, even if an extremely small region of the target boundary is obtained by RPM.

The applicability in harsh optical environments, such as dark smog, or strong backlight of ultra-wide band (UWB) pulse radar has a definite advantage over optical ranging techniques. We have already proposed the extended Synthetic Aperture Radar (SAR) algorithm employing double scattered waves, which aimed at enhancing the reconstructible region of the target boundary including shadow region. However, it still suffers from the shadow area for the target that has a sharp inclination or deep concave boundary, because it assumes a mono-static model, whose real aperture size is, in general, small. To resolve this issue, this study proposes an extension algorithm of the double scattered SAR based on a multi-static configuration. While this extension is quite simple, the effectiveness of the proposed method is nontrivial with regard to the expansion of the imaging range. The results from numerical simulations verify that our method significantly enhances the visible range of the target surfaces without a priori knowledge of the target shapes or any preliminary observation of its surroundings.

This letter proposes a novel technique for detecting a target signal buried in clutter using principal component analysis (PCA) for pulse-Doppler radar systems. The conventional detection algorithm is based on the fast Fourier transform-constant false alarm rate (FFT-CFAR) approaches. However, the detection task becomes extremely difficult when the Doppler spectrum of the target is completely buried in the spectrum of clutter. To enhance the detection probability in the above situations, the proposed method employs the PCA algorithm, which decomposes the target and clutter signals into uncorrelated components. The performances of the proposed method and the conventional FFT-CFAR based detection method are evaluated in terms of the receiver operating characteristics (ROC) for various signal-to-clutter ratio (SCR) cases. The results of numerical simulations show that the proposed method significantly enhances the detection probability compared with that obtained using the conventional FFT-CFAR method, especially for lower SCR situations.

Ultra-wideband millimeter wave radars significantly enhance the capabilities of three-dimensional (3D) imaging sensors, making them suitable for short-range surveillance and security purposes. For such applications, developed the range point migration (RPM) method, which achieves highly accurate surface extraction by using a range-point focusing scheme. However, this method is inaccurate and incurs great computation cost for complicated-shape targets with many reflection points, such as the human body. As an essential solution to this problem, we introduce herein a range-point clustering algorithm that exploits, the RPM feature. Results from numerical simulations assuming 140-GHz millimeter wavelength radar verify that the proposed method achieves remarkably accurate 3D imaging without sacrificing computational efficiency.

A multi-beam airborne pulsed-Doppler radar (MBR) system is presented and its clutter rejection performance compared with conventional phased array radar (PAR)'s by PRF tuning is discussed. The pulsed-Doppler radar equations taking account of the multi-beam operation are introduced and some kinds of computer simulations for seeking the conditions to get maximum signal to clutter ratio are carried out. As a results of this, it is cleared that same order of signal to clutter ratio improvement gotten in high PRF operation by conventional PAR can be realized at lower PRF operation by MBR on clutter free area, and higher clutter rejection effect, which is proportional to beam numbers, is obtained under affection of both of mainlobe and sidelobe clutters with order of beam numbers. This also means observable numbers of range bin are increased in MBR operation.

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.

Three-dimensional (3-D) reconstruction techniques employed by airborne radars are essential for object recognition in scenarios where optically vision is blurry, and are required for the monitoring of disasters and coast-guard patrols. There have been reports on 3-D reconstruction methods that exploit the layover appearing in inverse synthetic aperture radar (ISAR) imagery, which are suitable for the recognition of artificial targets such as buildings, aircraft or ships. However, existing methods assume only a point target or the aggregate of point targets, and most require the tracking of the multiple points over sequential ISAR images. In the case of a solid object with a continuous boundary, such as a wire or polyhedral structure, the positioning accuracy of such methods is severely degraded owing to scattering centers continuously shifting on the target surface with changes in the rotation angle. To overcome this difficulty, this paper extends the original Range Points Migration (RPM) method to the ISAR observation model, where a double mono-static model with two transmitting and receiving antennas is introduced to suppress cross-range ambiguity. The results of numerical simulation and experimental validation demonstrate that the extended RPM method has a distinct advantage for accurate 3-D imaging, even for non-point targets.

Blind source separation (BSS) techniques are required for various signal decomposing issues. Independent component analysis (ICA), assuming only a statistical independence among stochastic source signals, is one of the most useful BSS tools because it does not need a priori information on each source. However, there are many requirements for decomposing multiple deterministic signals such as complex sinusoidal signals with different frequencies. These requirements may include pulse compression or clutter rejection. It has been theoretically shown that an ICA algorithm based on maximizing non-Gaussianity successfully decomposes such deterministic signals. However, this ICA algorithm does not maintain a sufficient separation performance when the frequency difference of the sinusoidal waves becomes less than a nominal frequency resolution. To solve this problem, this paper proposes a super-resolution algorithm for complex sinusoidal signals by extending the maximum likelihood ICA, where the probability density function (PDF) of a complex sinusoidal signal is exploited as a priori knowledge, in which the PDF of the signal amplitude is approximated as a Gaussian distribution with an extremely small standard deviation. Furthermore, we introduce an optimization process for this standard deviation to avoid divergence in updating the reconstruction matrix. Numerical simulations verify that our proposed algorithm remarkably enhances the separation performance compared to the conventional one, and accomplishes a super-resolution separation even in noisy situations.

Doppler lidar (LIght Detection And Ranging) can provide accurate wind velocity vector estimates by processing the time delay and Doppler spectrum of received signals. This system is essential for real-time wind monitoring to assist aircraft taking off and landing. Considering the difficulty of calibration and cost, a single Doppler lidar model is more attractive and practical than a multiple lidar model. In general, it is impossible to estimate two or three dimensional wind vectors from a single lidar model without any prior information, because lidar directly observes only a 1-dimensional (radial direction) velocity component of wind. Although the conventional VAD (Velocity Azimuth Display) and VVP (Velocity Volume Processing) methods have been developed for single lidar model, both of them are inaccurate in the presence of local air turbulence. This paper proposes an accurate wind velocity estimation method based on a parametric approach using typical turbulence models such as tornado, micro-burst and gust front. The results from numerical simulation demonstrate that the proposed method remarkably enhances the accuracy for wind velocity estimation in the assumed modeled turbulence cases, compared with that obtained by the VAD or other conventional method.

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.

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.

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.

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.

Ultra-wide band (UWB) pulse radar with high range resolution and dielectric permeability is promising as an internal imaging technique for non-destructive testing or breast cancer detection. Various imaging algorithms for buried objects within a dielectric medium have been proposed, such as aperture synthesis, the time reversal approach and the space-time beamforming algorithm. However, these algorithms mostly require a priori knowledge of the dielectric medium boundary in image focusing, and often suffer from inadequate accuracy to identify the detailed structure of buried targets, such as an edge or specular surface owing to employing the waveform focusing scheme. To overcome these difficulties, this paper proposes an accurate and non-parametric (i.e. using an arbitrary shape without target modeling) imaging algorithm for targets buried in a homogeneous dielectric medium by advancing the RPM (Range Points Migration) algorithm to internal imaging issues, which has been demonstrated to provide an accurate image even for complex-shaped objects in free-space measurement. Numerical simulations, including those for two-dimensional (2-D) and three-dimensional (3-D) cases, verify that the proposed algorithm enhances the imaging accuracy by less than 1/10 of the wavelength and significantly reduces the computational cost by specifying boundary extraction compared with the conventional SAR-based algorithm.