Over the past decade structural signal processing is an emerging field, which gained researchers' intensive attentions in various areas including the applied mathematics, physics, information theory, signal processing, and so on. The structural signal processing is a paradigm of making the revolutionary refresh on theories and methods in the nutshell of traditional signal processing based on the well-known Nyquist-Shannon theory, which will render us a new perspective on the adaptive data acquisition in the task-driven manner. Basically, the structural signal processing includes four research contents (MAMA): (a) Measures for the structural signal, (b) Algorithms for reconstructing the structural signal at the low-complexity computational cost, (c) Methods for smart data acquisition at the low hardware cost and system complexity, and (d) Applications of structural signal processing in applied fields. This paper reviews on the recent progress on the theory and algorithms for structural signal processing, which will provides hopefully useful guide for readers of interest.

FOliage PENetration Synthetic Aperture Radar (FOPEN SAR) is used in low Signal-to-Clutter Ratio (SCR) conditions to detect targets hidden in forests, which introduces difficulties in target detection. In this study, an enhanced imaging method based on the scattering aspect variability of the target is proposed, which improves the SCR of the formed images of hidden targets while maintaining high spatial resolution. In the case of a vehicle target, the dihedral is formed by its main side and the ground. The echo is strongest when the incident electromagnetic wave is along the normal direction of the dihedral. A high-resolution image corresponding to this aspect angle is formed by the proposed enhanced imaging method to increase the SCR of the target and improve the detection performance. Airborne FOPEN SAR data were used to validate the efficiency of the proposed method.

A novel concept for suppressing the problem of multipath ghosts in Ground Penetrating Radar (GPR) and Through-Wall Imaging (TWI) is presented. Ghosts (i.e., false targets) mainly arise from the use of the Born or single-scattering approximations that lead to linearized imaging algorithms; however, these approximations neglect the effect of multiple scattering (or multipath) between the electromagnetic wavefield and the object under investigation. In contrast to existing methods of suppressing multipath ghosts, the proposed method models for the first time the reflectivity of the probed objects as a probability function up to a normalized factor and introduces the concept of random subaperture by randomly picking up measurement locations from the entire aperture. Thus, the final radar image is a joint probability distribution that corresponds to radar images derived from multiple random subapertures. Finally, numerical experiments are used to demonstrate the performance of the proposed methodology in GPR and TWI imaging.

Lunar Penetrating Radar (LPR), which is one of the most important science payloads onboard the Chang'E-3 (CE-3) rover, is used to obtain electromagnetic image less than 100 m beneath the lunar surface. This paper describes the system composition and working mechanism of the LPR and presents a detailed analysis of its data. We investigated special signal-processing methods and present the result of channel-1 data. The result shows that the effective echo occurs at depths greater than 100 m. Moreover, an unusual reflection exists at depth of 40 m, which may be the boundary of two geological units beneath the lunar surface.

This paper presents the design of a handheld pseudo random coded Ultra-WideBand (UWB) radar for human sensing. The main tasks of the radar are to track the moving human object and extract the human respiratory frequency. In order to achieve perfect penetrability and good range resolution, m sequence with a carrier of 800 MHz is chosen as the transmitting signal. The modulated m-sequence can be generated directly by the high-speed DAC and FPGA to reduce the size of the radar system, and the mean power of the transmitting signal is 5 dBm. The receiver has two receiving channels based on hybrid sampling, the first receiving channel is to sample the reference signal and the second receiving channel is to obtain the radar echo. The real-time pulse compression is computed in parallel with a group of on-chip DSP48E slices in FPGA to improve the scanning rate of the radar system. Additionally, the algorithms of moving target tracking and life detection are implemented using Intel’s micro-processor, and the detection results are sent to the micro displayer fixed on the helmet. The experimental results show that the moving target located at less than 16 m far away from the wall can be tracked, and the respiratory frequency of the static human at less than 14 m far away from the wall can be extracted.

In this study, we investigated the use of a half-ellipse dipole with distributed resistor-loading. By improving the structure of the antenna arms and using distributed resistor-loading technology, the current reflection at the end of dipole is significantly reduced, the input impedance is improved, and the operation bandwidth is widened. We decreased the backward radiation of the antenna with a cubic metal-reflective cavity and also improved the ground penetration ability. The proposed antenna was simulated and designed with electromagnetic computing software; on the basis of the design results, we fabricated the antenna sample. Measurement results of the return loss and radiation characteristics of the proposed antenna confirm the validity of the simulation. We applied the proposed antenna in a GPR system as an underground target detection experiment; on the basis of the experimental results, we conclude that the antenna is able to meet the needs of GPR systems.

To separate the target group siganl in midcourse, a method based on time-frequency filtering is proposed in this paper. Firstly, the micro-motion period of one target is estimated based on the auto correlation method. Then, the signal is divided to several segments based on the estimated period. Also, the strong energy area in time-frequency domain for each segment signal is obtained by S-transform. The intersection of strong energy areas for different period can be seem as the support area of target. So, the signal attribute to one target can be obtained by time-frequency filtering based on the estimated support area. Simulation results verify the effectiveness of the proposed algorithm.

Owing to the Doppler frequency migration of the return signal of maneuvering targets and finite training samples, it is difficult to detect maneuvering targets by conventional Adaptive Matched Filter (AMF) detectors. To solve this problem, a new method is proposed. First, to minimize sample size impairments, the diagonal loading technique was adopted to decrease the degrees of freedom of the sample space. Second, the Doppler frequency migration was compensated by the estimated acceleration which was estimated by the cubic phase transform, so as to reduce the dimension of matched searching and degrade the heavy calculation load. Finally, accumulation detection was conducted. The simulation results suggest that the proposed method can efficiently detect maneuvering target in finite sample situations with simple computation and constant false alarm rate detection.

In this paper, we present a Modified Two-Scale Microwave (MTSM) scattering model to describe the scattering coefficient of naturally rough surfaces. The surface roughness is assumed to be Gaussian in the proposed model so that the surface height z(x, y) can be split into large- and small-scale components by the wavelet packet transform according to electromagnetic wavelength. We used the Kirchhoff Model(KM) and Small Perturbation Method (SPM) to estimate the backscattering coefficient of large- and small-scale roughness, respectively. The tilting effect caused by the slope of large-scale roughness was corrected when calculating the contribution of backscattering to small-scale roughness. The backscattering coefficient of the MTSM comprised the total backscattering contributions of surfaces with both scales of roughness. The MTSM was tested and validated using the Advanced Integral Equation Model (AIEM) for dielectric randomly rough surfaces. The accuracy of the MTSM showed favorable agreement with AIEM, both when the incident angle was less than 30° (θ_i<30°) and when the surface roughness was small (ks=0.354).

In 2012, the German Aerospace Center (DLR.) proposed a BiDirectional mode that can achieve several seconds of repeated time lags by single star and single flight. Its basic principle includes the generation of a double-beam antenna pattern by electronic beam steering and simultaneous emission of two pulses that irradiate the front and back imaging area. The two pulses, which are simultaneously received will be separated by band-pass filtering in the Doppler domain and imaged, respectively. This paper presents an improved Multi Input Single Output (MISO)-SAR system based on the BiDirectional mode which converts the traditional simultaneous dual beam emitting and receiving into time-division emitting and simultaneous receiving, respectively. This results in an improved emitting antenna pattern owning to the suppression of the Azimuth Ambiguity to Signal Ratio (AASR). The current paper describes the spectrum separation effects, AASR analysis, and the system design process. Therefore, to confirm effectiveness, point target 1-D and 2-D simulation results are compared before and after the improvement. Furthermore, the BiDirectional and other short-term repeated SAR modes are compared.

Aiming at detecting the change regions of high resolution Synthetic Aperture Radar (SAR) images, we propose to use the Dempster-Shafer (D-S) evidence theory to fuse coherent/incoherent features from sensors that form an integral part of the system. First, we use the Simple Linear Iterative Clustering (SLIC) segmentation algorithm to implement multi-scale joint segmentation for multi-temporal SAR images. Second, we extract multiple intensity and coherence difference features on each segment level by SLIC using mean operator to complete the fusion of multi-scale features to get the multi-feature difference mapped by a ratio operator. Finally, we fuse the multi-feature difference maps to get the final change detection result using the D-S evidence theory. The experimental results in our study prove the effectiveness of our proposed computational algorithm.

The linear array Synthetic Aperture Radar (SAR) system is a popular research tool, because it can realize three-dimensional imaging. However, owning to limitations of the aircraft platform and actual conditions, resolution improvement is difficult in cross-track and along-track directions. In this study, a twodimensional fast Estimation of Signal Parameters by Rotational Invariance Technique (ESPRIT) algorithm for linear array SAR imaging is proposed to overcome these limitations. This approach combines the Gerschgorin disks method and the ESPRIT algorithm to estimate the positions of scatterers in cross and along-rack directions. Moreover, the reflectivity of scatterers is obtained by a modified pairing method based on “region growing”, replacing the least-squares method. The simulation results demonstrate the applicability of the algorithm with high resolution, quick calculation, and good real-time response.

The Phase Gradient Autofocus (PGA) algorithm can remove the high order phase error effectively, which is of great significance to get high resolution images in real-time processing. While PGA usually needs iteration, which necessitates long working hours. In addition, the performances of the algorithm are not stable in different scene applications. This severely constrains the application of PGA in real-time processing. Isolated scatter selection and windowing are two important algorithmic steps of Phase Gradient Autofocus Algorithm. Therefore, this paper presents an isolated scatter selection method based on sample mean and a windowing method based on pulse envelope. These two methods are highly adaptable to data, which would make the algorithm obtain better stability and need less iteration. The adaptability of the improved PGA is demonstrated with the experimental results of real radar data.