In order to execute coherent Doppler processing in a high range-rate scenario, whether it is for detection, estimation or imaging, range walk embedded in target return should be compensated first. In case of a bistatic radar geometry where a transmitter, a receiver and a target can be all moving, the extent of range walk depends on their relative positions and velocities. This paper presents a coherent Doppler processing algorithm to achieve target detection and Doppler frequency estimation of a target under a bistatic radar geometry. This algorithm is based on the assumption that a target has constant Doppler frequency during a coherent processing interval (CPI). Thus, we first show under what condition the assumption could be valid. We next develop an algorithm, along with its implementation procedures where the region of range walk, called a window, is manipulated. Finally, the performance of a proposed algorithm is examined through simulations.

Ultra-wideband pulse radar is a promising technology for the imaging sensors of rescue robots operating in disaster scenarios, where optical sensors are not applicable because of thick smog or high-density gas. For the above application, while one promising ultra-wideband radar imaging algorithm for a target with arbitrary motion has already been proposed with a compact observation model, it is based on an ellipsoidal approximation of the target boundary, and is difficult to apply to complex target shapes. To tackle the above problem, this paper proposes a non-parametric and robust imaging algorithm for a target with arbitrary motion including rotation and translation being observed by multi-static radar, which is based on the matching of target boundary points obtained by the range points migration (RPM) algorithm extended to the multi-static radar model. To enhance the imaging accuracy in situations having lower signal-to-noise ratios, the proposed method also adopts an integration scheme for the obtained range points, the antenna location part of which is correctly compensated for the estimated target motion. Results from numerical simulations show that the proposed method accurately extracts the surface of a moving target, and estimates the motion of the target, without any target or motion model.

The optimization of polarimetric contract enhancement (OPCE) is one of the important problems in radar polarimetry since it provides a substantial benefit for target enhancement. Considering different scattering mechanisms between the desired targets and the undesired targets, Yang et al. extended the OPCE model to the generalized OPCE (GOPCE) problem. Based on a modified GOPCE model and the linear discriminant analysis, a ship detector is proposed in this paper to improve the detection performance for polarimetric Synthetic Aperture Radar (SAR) imagery. In the proposed method, we modify the combination form of the three polarimetric parameters (i.e., the plane scattering similarity parameter, the diplane scattering similarity parameter and the Cloude entropy), then use an optimization function resembling the classical Fisher criterion to optimize the optimal polarization states corresponding to the radar received power and the fusion vector corresponding to the polarimetric parameters. The principle of the optimization detailed in this paper lies in maximizing the difference between the desired targets and sea clutter, and minimizing the clutter variance at the same time. RADARSAT-2 polarimetric SAR data acquired over Tanggu Port (Tianjin, China) on June 23, 2011 are used for validation. The experimental results show that the proposed method improves the contrast of the targets and sea clutter and meanwhile reduces the clutter variance. In comparison to another GOPCE based ship detector and the classical polarimetric whitening filter (PWF), the proposed method shows a better performance for weak targets. In addition, we also use the RADARSAT-2 data acquired over San-Francisco on April 9, 2008 to further demonstrate the improvement of this method for target contrast.

We propose a new fine Doppler frequency estimator using two fast Fourier transform (FFT) samples for pulse Doppler radar that offers highly sensitive detection and a high resolution of velocity. The procedure of fine Doppler frequency estimation is completed through coarse frequency estimation (CFE) and fine frequency estimation (FFE) steps. During the CFE step, the integer part of the Doppler frequency is obtained by processing the FFT, after which, during the FFE step, the fractional part is estimated using the relationship between the FFT peak and its nearest resultant value. Our simulation results show that the proposed estimator has better accuracy than Candan's estimator in terms of bias. The root mean square error (RMSE) of the proposed estimator has more than 1.4 time better accuracy than Candan's estimator under a 1,024-point FFT and a signal-to-noise ratio (SNR) of 10 dB. In addition, when the FFT size is increased from 512 to 2,048, the RMSE characteristics of the proposed estimator improve by more than two-fold.

Ultra-wideband (UWB) pulse radar has high range resolution and permeability in a dielectric medium, and has great potential for the non-destructive inspection or early-stage detection of breast cancer. As an accurate and high-resolution imaging method for targets embedded in a dielectric medium, extended range points migration (RPM) has been developed. Although this method offers an accurate internal target image in a homogeneous media, it assumes the permittivity of the dielectric medium is given, which is not practical for general applications. Although there are various permittivity estimation methods, they have essential problems that are not suitable for clear, dielectric boundaries like walls, or is not applicable to an unknown and arbitrary shape of dielectric medium. To overcome the above drawbacks, we newly propose a permittivity estimation method suitable for various shapes of dielectric media with a clear boundary, where the dielectric boundary points and their normal vectors are accurately determined by the original RPM method. In addition, our method iteratively compensates for the scattered waveform deformation using a finite-difference time domain (FDTD) method to enhance the accuracy of the permittivity estimation. Results from a numerical simulation demonstrate that our method achieves accurate permittivity estimation even for a dielectric medium of wavelength size.

A method for the specification of weighting functions for a spaceborne/airborne interferometric synthetic aperture radar (SAR) sensor for Earth observation and environment monitoring is introduced. This method is based on designing an optimum mismatched filter which minimizes the total power in sidelobes located out of a specified range region around the peak value point of the system point-target response, i.e. impulse response function under the constraint imposed on the peak value. It is shown that this method allows achieving appreciable improvement in accuracy performance without degradation in the range resolution.

We propose a compact and high-range resolution 76 GHz millimeter-wave radar system for autonomous unmanned helicopters. The purpose of the radar system is to detect and avoid obstacles that may affect the flight safety. To achieve these objectives, a high range resolution and a long detection range are required for the radar systems with small volume and weight. The radar broadband RF front-end module which employs a simple direct conversion method is proposed. The radar module enables the 6 GHz RF signal transmission as well as the output power of about 8 dBm using commercially available low-cost monolithic microwave integrated circuits. The radar system comprises the broadband RF front-end module, a Ku-band local frequency-modulated continuous wave signal synthesizer, and a very light weight carbon fiber reinforced plastic parabolic reflector antenna. The 5 cm of range resolution is experimentally obtained using the 6 GHz RF signal bandwidth. The results of the power line measurement confirm an about 23 dB signal to noise ratio, which is measured from the reflection of the high-voltage power lines about 150 m ahead. In addition, the results of the radar system on-board test using an unmanned helicopter are evaluated. The real-time radar scope, which is transferred through the wireless connection, confirms the detection of the power lines and the other surrounding objects.

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.

The imaging of humans using radar is promising for surveillance systems. Although conventional radar systems detect the presence or position of intruders, it is difficult to acquire shape and motion details because the resolution is insufficient. This paper presents a high-resolution human imaging algorithm for an ultra-wideband (UWB) Doppler radar. The proposed algorithm estimates three-dimensional human images using interferometry and, using velocity information, rejects false images created by the interference of body parts. Experiments verify that our proposed algorithm achieves adequate pedestrian imaging. In addition, accurate shape and motion parameters are extracted from the estimated images.

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.

A dual-band dual-polarization array antenna with improved bandwidth for an advanced multi-function radio function concept (AMRFC) radar application is proposed. To improve the S-band impedance bandwidth, the proposed antenna uses modified coupling feed patch. The measured bandwidth of the prototype array is 19.8% and 25.7% for the S- and X-band, respectively. The isolation between the two orthogonal polarizations is higher than 15 dB and cross-polarization level is less than -17 dB for both S- and X-bands.

A novel conjugate unitary ESPRIT (CU-ESPRIT) algorithm for the joint direction of departure (DOD), and direction of arrival (DOA), estimation in a bistatic MIMO radar is proposed. A new virtual array is formed by using the properties of noncircular signals, and the properties of the centro-Hermitian matrix are employed to convert the complex-valued data matrix into a real-valued data matrix. Then the real-valued rotational invariance properties of the new virtual array are determined to estimate DODs and DOAs, which are paired automatically. The proposed method provides better angle estimation performance and detects more targets owing to double number of MIMO virtual array elements. Simulation results are presented to verify the effectiveness of the proposed algorithm.

A method of calibration for GPR responses is introduced in order to extract a target response from GPR data. This calibration procedure eliminates undesirable waveform distortion that is caused by antenna characteristics and multiple scattering effects between the antennas and the ground surface. An application result to measured GPR data shows that undesirable late-time responses caused by the antenna characteristics and multiple scattering effects are removed, and that the target response is clearly reconstructed. This result demonstrates that the waveform calibration of GPR data is significant and essential for reliable target identification.

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.

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.

Bistatic synthetic aperture radar (BSAR) with one fixed station (OF-BSAR) can be used in wide area surveillance, ground moving target indication etc. This paper analyzes the spatial variance of OF-BSAR. Analytical expressions of the spatial invariance region in the data space are given. Using these results, we can determine the spatial invariance region in the data set and the imaging area. After that, we give a data blocking scheme for raw data focusing. Numerical simulation verifies the results of this paper.

A low-complexity method for angle estimation in Multiple-input multiple-output radar (MIMO) radar is presented. In this approach, the signal subspace can be spanned by the orthogonal vectors which are obtained by Multi-stage Wiener Filter (MSWF), then the ESPRIT method can be used to estimate direction of departures (DODs) and direction of arrivals (DOAs). Compared with the conventional ESPRIT algorithm, the proposed method does not involve estimation of the covariance matrix and its eigen-decomposition, which alleviates remarkably the computational complexity. Moreover, the proposed method achieves the similar angle estimation performance. Simulation results are presented to verify the efficiency of the proposed method.

Electromagnetic scattering at high-frequencies is computationally heavy. Radar cross section (RCS) of electrically large concave and convex objects are solved by using the hybrid method. For convex and concave surfaces, Modified-Vector Physical Optics (MPO) with enhanced accuracy and Iterative Physical Optics (IPO) taking multiple-reflections into account, are selectively and independently applied for convex and concave parts of the scatterer. The accuracy of RCS by this hybrid method is tested with the MoM based simulator Wipl-D as the reference. The RCS from relatively small scatterers with the dimension of the order of a few wavelengths can be successfully predicted.

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.

The power of laser radar received echoes varies over a large range due to many factors such as target distance, size, reflection ratio, etc, which leads to the difficulty of decoding codes from the received noise buried signals for spectrum code modulated laser radar. Firstly, a pseudo-random noise (PN) code modulated laser radar model is given, and the problem to be addressed is discussed. Then, a novel method based on Inter Symbol Interference (ISI) is proposed for resolving the problem, providing that only Additive White Gaussian Noise (AWGN) exists. The ISI effect is introduced by using a high pass filter (HPF). The results show that ISI improves laser radar receiver decoding ratio, thus the peak of the correlation function of decoded codes and modulation codes. Finally, the effect of proposed method is verified by a simple experiment.