Many previous works state that a multiple Sidelobe canceller (MSLC) with two auxiliary antennas is successful in suppressing two interference signals received simultaneously by sidelobes of a main antenna. In this paper, we show that the MSLC does not always guarantee such capability in three dimensional applications where the incident direction of interference signals is defined by two angles (elevation and azimuth). We show the singularity of the autocorrelation matrix for the auxiliary channel signals induces the degradation of the capability by analyzing characteristics of MSLC's in three dimensional applications from the view point of the eigenvalue problem. To overcome this singularity, we propose a novel MSLC controlling the placement of auxiliary antennas by means of switching over three antennas arranged triangularly. Some simulations are conducted to show the effectiveness of the proposed MSLC.

Ultra wideband radar is one of the most promising techniques for non-invasive imaging in a dielectric medium, which is suitable for both medical screening and non-destructive testing applications. A novel imaging method for such an application is proposed in this brief paper, which has been extended from the advanced range points migration method to a multi-static observation model with circular arrays. One notable feature of this method is that it is applicable to either arbitrary dielectric or internal object shapes, and it can also expand the reconstructible image region compared with that obtained using the mono-static model by employing received signals after penetrating various propagation paths in dielectric medium. Numerical results for the investigation of an elliptical object, surrounded by a random dielectric surface, show the remarkable advantages of the proposed method with respect to image expansion.

Microwave imaging techniques, in particular synthetic aperture radar (SAR), are able to obtain useful images even in adverse weather or darkness, which makes them suitable for target position or feature estimation. However, typical SAR imagery is not informative for the operator, because it is synthesized using complex radio signals with greater than 1.0 m wavelength. To deal with the target identification issue for imaging radar, various automatic target recognition (ATR) techniques have been developed. One of the most promising ATR approaches is based on neural network classification. However, in the case of SAR images heavily contaminated by random or speckle noises, the classification accuracy is severely degraded because it only compares the outputs of neurons in the final layer. To overcome this problem, this paper proposes a self organized map (SOM) based ATR method, where the binary SAR image is classified using the unified distance matrix (U-matrix) metric given by the SOM. Our numerical analyses and experiments on 5 types of civilian airplanes, demonstrate that the proposed method remarkably enhances the classification accuracy, particular in lower S/N situations, and holds a significant robustness to the angular variations of the observation.

There is a strong demand for super-resolution time of arrival (TOA) estimation techniques for radar applications that can that can exceed the theoretical limits on range resolution set by frequency bandwidth. One of the most promising solutions is the use of compressed sensing (CS) algorithms, which assume only the sparseness of the target distribution but can achieve super-resolution. To preserve the reconstruction accuracy of CS under highly correlated and noisy conditions, we introduce a random resampling approach to process the received signal and thus reduce the coherent index, where the frequency-domain-based CS algorithm is used as noise reduction preprocessing. Numerical simulations demonstrate that our proposed method can achieve super-resolution TOA estimation performance not possible with conventional CS methods.

We present a method of extracting the digital inphase (I) and quadrature (Q) components from oversampled bandpass signals using narrow-band bandpass Hilbert transformers. Down-conversion of the digitized IF signals to baseband and reduction of the quantization noise are accomplished by the multistage decimator with the complex coefficient bandpass digital filters (BPFs), which construct the bandpass Hilbert transformers. Most of the complex coefficient BPFs in the multistage decimator can be replaced with the lowpass filters (LPFs) under some conditions, which reduces computational burden. We evaluate the signal to quantization noise ratio of the I and Q components for the sinusoidal input by computer simulation. Simulation results show that the equivalent amplitude resolution of the I and Q components can be increased by 3 bits in comparison with non-oversampling case.

UWB (Ultra Wideband) radar offers great promise for advanced near field sensors due to its high range resolution. In particular, it is suitable for rescue or resource exploration robots, which need to identify a target in low visibility or acoustically harsh environments. Recently, radar algorithms that actively coordinate multiple scattered components have been developed to enhance the imaging range beyond what can be achieved by synthesizing a single scattered component. Although we previously developed an accurate algorithm for imaging shadow regions with low computational complexity using derivatives of observed ranges for double scattered signals, the algorithm yields inaccurate images under the severe interference situations that occur with complex-shaped or multiple objects or in noisy environments. This is because small range fluctuations arising from interference or random noises can produce non-negligible image degradation owing to inaccuracy in the range derivative calculation. As a solution to this difficulty, this paper proposes a novel imaging algorithm that does not use the range derivatives of doubly scattered signals, and instead extracts a feature of expansive distributions of the observed ranges, using a unique property inherent to the doubly scattering mechanism. Numerical simulation examples of complex-shaped or multiple targets are presented to demonstrate the distinct advantage of the proposed algorithm which creates more accurate images even for complicated objects or in noisy situations.

Microwave imaging techniques, in particular, synthetic aperture radar (SAR), are promising tools for terrain surface measurement, irrespective of weather conditions. The coherent change detection (CCD) method is being widely applied to detect surface changes by comparing multiple complex SAR images captured from the same scanning orbit. However, in the case of a general damage assessment after a natural disaster such as an earthquake or mudslide, additional about surface change, such as surface height change, is strongly required. Given this background, the current study proposes a novel height change estimation method using a CCD model based on the Pauli decomposition of fully polarimetric SAR images. The notable feature of this method is that it can offer accurate height change beyond the assumed wavelength, by introducing the frequency band-divided approach, and so is significantly better than InSAR based approaches. Experiments in an anechoic chamber on a 1/100 scaled model of the X-band SAR system, show that our proposed method outputs more accurate height change estimates than a similar method that uses single polarimetric data, even if the height change amount is over the assumed wavelength.

As a consequence of recent developments in communications, the parameters of communication signals, such as the modulation parameter values, are becoming unstable because of time-varying SNR under electromagnetic conditions. In general, it is difficult to classify target signals that have time-varying parameters using traditional signal recognition methods. To overcome this problem, this study proposes a novel recognition method that works well even for such time-dependent communication signals. This method is mainly composed of feature extraction and classification processes. In the feature extraction stage, we adopt Shannon entropy and index entropy to obtain the stable features of modulated signals. In the classification stage, the interval gray relation theory is employed as suitable for signals with time-varying parameter spaces. The advantage of our method is that it can deal with time-varying SNR situations, which cannot be handled by existing methods. The results from numerical simulation show that the proposed feature extraction algorithm, based on entropy characteristics in time-varying SNR situations,offers accurate clustering performance, and the classifier, based on interval gray relation theory, can achieve a recognition rate of up to 82.9%, even when the SNR varies from -10 to -6 dB.

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.