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The combination of a single-photon avalanche diode detector with a high-sensitivity and photon-efficient reconstruction algorithm can realize the reconstruction of target range image from weak light signal conditions. The limited spatial resolution of the detector and the substantial background noise remain significant challenges in the actual detection process, hindering the accuracy of 3D reconstruction techniques. To address this challenge, this paper proposes a denoising super-resolution reconstruction network based on generative adversarial network (GAN) design. Soft thresholding is incorporated into the deep architecture as a nonlinear transformation layer to effectively filter out noise. Moreover, the Unet-based discriminator is introduced to complete the high-precision detail reconstruction. The experimental results show that the proposed network can achieve high-quality super-resolution range imaging. This approach has the potential to enhance the accuracy and quality of long-range imaging in weak light signal conditions, with broad applications in fields such as robotics, autonomous vehicles, and biomedical imaging.

There exists the contradiction between imaging efficiency and imaging quality for Fourier single-pixel imaging (FSI). Although the deep learning approaches have solved this problem to some extent, the reconstruction quality at low sampling rate is still not enough to meet the practical requirements. To solve this problem, inspired by the idea of super-resolution, this paper proposes the paralleled fusing of the U-net and attention mechanism to improve the quality of FSI reconstruction at a low sampling rate. This paper builds a generative adversarial network structure to achieve recovery of high-resolution target images from low-resolution FSI reconstruction results under low sampling rate conditions. Compared with conventional FSI and other deep learning methods based on FSI, the proposed method can get better quality and higher resolution results at low sampling rates in simulation and experiments. This approach is particularly important to high-speed Fourier single pixel imaging applications.

Ghost imaging is widely used in underwater active optical imaging because of its simple structure, long distance, and non-local imaging. However, the complexity of the underwater environment will greatly reduce the imaging quality of ghost imaging. To solve this problem, an underwater ghost imaging method based on the generative adversarial networks is proposed in the study. The generator of the proposed network adopts U-Net with the double skip connections and the attention module to improve the reconstruction quality. In the network training process, the total loss function is the sum of the weighted adversarial loss, perceptual loss, and pixel loss. The experiment and simulation results show that the proposed method effectively improves the target reconstruction performance of underwater ghost imaging. The proposed method promotes the further development of active optical imaging of underwater targets based on ghost imaging technology.

A single photon avalanche diode (SPAD) is a high sensitivity detector that can work under weak echo signal conditions (≤1 photon per pixel). The measured digital signals can be used to invert the range and reflectivity images of the target with photon-efficient imaging reconstruction algorithm. However, the existing photon-efficient imaging reconstruction algorithms are susceptible to noise, which leads to poor quality of the reconstructed range and reflectivity images of target. In this paper, a non-local sparse attention encoder (NLSA-Encoder) neural network is proposed to extract the 3D information to reconstruct both the range and reflectivity images of target. The proposed network model can effectively reduce the influence of noise in feature extraction and maintain the capability of long-range correlation feature extraction. In addition, the network is optimized for reconstruction speed to achieve faster reconstruction without performance degradation, compared with other existing deep learning photon-efficient imaging reconstruction methods. The imaging performance is verified through numerical simulation, near-field indoor and far-field outdoor experiments with a 64 × 64 SPAD array. The experimental results show that the proposed network model can achieve better results in terms of the reconstruction quality of range and reflectivity images, as well as reconstruction speed.

The conventional convolutional neural network (CNN) has proven to be effective for synthetic aperture radar (SAR) target recognition. However, the relationship between different convolutional kerne...

Simultaneous location and parameter estimation of vital sign signal captured with a MIMO-FMCW radar sensor system is presented. The suggested system is able to deal with safety inspection emergency counter-terrorism and fire rescue. Micro-Doppler information, estimated from received signal, is used to classify heart and breath frequency of human. The Capon BF measurement algorithm is used to estimate accurate angles. The received signal synthesize precise angle information to get position of target. Measured data collected from MIMO-FMCW radar received encouraging results after these algorithms. Results of human object with deferent range or angle show effectiveness to achieve great estimation performance.

To address the defocusing problem faced by random stepped frequency (RSF) radars, this paper presents a complementary code cancellation (CCC) method to estimate the target velocity and achieve motion compensation. The proposed CCC method is capable of eliminating the coupling effect between range profile and target velocity. In this paper, we first give a block diagram of RSF radar modulated by the M-sequence generated pseudorandom code, and introduce the baseband sampling echo model of moving target. Then, the velocity estimation accuracy is derived to show the sensitivity of high-resolution range profile to target velocity. Subsequently, the CCC method is proposed and also investigated in the application of multi-target scenario. Performance analyses demonstrate that the proposed method can satisfy the estimation accuracy requirement and improve the signal to noise ratio by the velocity accumulation. Finally, simulations show that the method is effective and more computationally efficient than the existing popular methods.

A novel method to estimate the object velocity for random stepped frequency radar by the waveform design is proposed. The method utilises a so-called ‘complementary random code’ to modulate the transmitted waveform. Theoretical analyses show that the proposed method achieves a high precision in velocity estimation, which could meet the requirements of motion compensation. Moreover, its computational complexity can be significantly reduced, compared with the classical estimation methods. Finally, the simulations validate the effectiveness of the present method.

In the optical region, the global scattering center (SC) model is important for target recognition and data compression. However, the existing methods are difficult to be accomplished because of the requirement for large storage or time consumption. To solve this problem, a new easy-to-implement method is proposed in this paper. By employing a modified random sample consensus method, all candidate positions for the global SC model are extracted and discriminated efficiently. The clean step is unnecessary in the proposed method, which makes the procedure more efficient. Experimental results on the high-frequency-electromagnetic data demonstrate the effectiveness of the proposed method.

Nowadays, object detection methods based on deep learning are applied more and more to the interpretation of optical remote sensing images. However, the complex background and the wide range of object sizes in remote sensing images increase the difficulty of object detection. In this paper, we improve the detection performance by combining the attention information, and generate adaptive anchor boxes based on the attention map. Specifically, the attention mechanism is introduced into the proposed method to enhance the features of the object regions while reducing the influence of the background. The generated attention map is then used to obtain diverse and adaptable anchor boxes using the guided anchoring method. The generated anchor boxes can match better with the scene and the objects, compared with the traditional proposal boxes. Finally, the modulated feature adaptation module is applied to transform the feature maps to adapt to the diverse anchor boxes. Comprehensive evaluations on the DIOR dataset demonstrate the superiority of the proposed method over the state-of-the-art methods, such as RetinaNet, FCOS and CornerNet. The mean average precision of the proposed method is 4.5% higher than the feature pyramid network. In addition, the ablation experiments are also implemented to further analyze the respective influence of different blocks on the performance improvement.

Background: To investigate the value of the nursing cooperation workflow for immediate breast reconstruction with deep inferior epigastric perforator (DIEP) flap after breast cancer resection. Methods: The clinical data of 29 patients who had undergone immediate breast reconstruction with DIEP flap after breast cancer resection in our center from January 2016 to December 2017 were retrospectively analyzed. In particular, the nursing cooperation workflow was reviewed. Results: All the 29 patients were emotionally stable before surgery and were able to cooperate well with the surgery. The surgery was smooth. In 27 patients, the flaps survived after surgery and primary healing was achieved at the wounds. The remaining two cases presented with venous vascular crisis within 24 h after the surgery, and the flaps survived after active rescue. The patients were followed up for 4 months to 3 years. Neither complication such as local tumor recurrence, incision infection, flap necrosis, or upper limb lymphedema in the surgical area nor complication such as abdominal wall bulging, abdominal wall hernia, or fat liquefaction of incision in the donor area was reported. The shape of the reconstructed breasts was natural and satisfactory. Conclusions: Immediate breast reconstruction with DIEP flap after breast cancer resection involves two disciplines: tumor resection and plastic repair. It is time-consuming and difficult to perform. Before the surgery, nurses in the operating room should carefully assess the patient's disease condition, communicate well with the operator, fully understand and be familiar with the surgical procedure and its special requirements, and formulate the surgical cooperation plan. During the surgery, the nurses should strictly implement cancer-free technique and be ready to assist for every next step, so as to effectively shorten the operative time, prevent local tumor recurrence, and thus pave the way for a successful surgery.

For the random hopping frequency signal, the modulated frequencies are randomly distributed over given bandwidth. The randomness of modulated frequency not only improves the electronic counter countermeasure capability for radar systems, but also determines its performance of range compression. In this paper, the range ambiguity function of RHF signal is firstly derived. Then, a design method of frequency hopping pattern based on stationary phase principle to improve the peak to side-lobe ratio is proposed. Finally, the simulated experiments show a good effectiveness of the presented design method.

Target tracking using non-threshold raw data with low signal-to-noise ratio is a very difficult task, and the model uncertainty introduced by target’s maneuver makes it even more challenging. In this work, a multiple-model based method was proposed to tackle such issues. The method was developed in the framework of Bernoulli filter by integrating the model probability parameter and implemented via sequential Monte Carlo (particle) technique. Target detection was accomplished through the estimation of target’s existence probability, and the estimate of target state was obtained by combining the outputs of modeldependent filtering. The simulation results show that the proposed method performs better than the TBD method implemented by the conventional multiple-model particle filter.

Phased array radar based simultaneous multi-beams can be used in many fields for activities for change detection and platform location. This paper proposes a method for multi-beam antenna pattern design based on the phased array radar principle. In the proposed method, the relationships between the array structure, the direction and the width of the beams are deduced. Based on analysis of echo characters, the limiting conditions in various applications are obtained to ensure that the spectrums from different beams are not aliasing. Using the designed antenna pattern and parameters, echoes from different beams are received and separated by filtering the signal using a bank of filters corresponding to each beam. After separation, an azimuth nonlinear chirp scaling method is applied to the echoes of each beam for imaging and the imaging performance is analyzed. Simulation results show that the mode is efficient. The results also demonstrate that (1) when the platform velocity is perpendicular to the array normal line and the squint angle is not sufficiently large, the azimuth resolution is kept constant; (2) when the platform velocity is parallel to the normal line, the azimuth resolution declines sharply if the beam direction gets close to the normal line. However, if the beam direction deviates from the line, the effects of the antenna fill time becomes serious; therefore, an appropriate beam direction should be chosen.

Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio (SNR). A modified multi-target track-before-detect (TBD) method was proposed to tackle this issue using a nonstandard point observation model. The method was developed from sequential Monte Carlo (SMC)-based probability hypothesis density (PHD) filter, and it was implemented by modifying the original calculation in update weights of the particles and by adopting an adaptive particle sampling strategy. To efficiently execute the SMC-PHD based TBD method, a fast implementation approach was also presented by partitioning the particles into multiple subsets according to their position coordinates in 2D resolution cells of the sensor. Simulation results show the effectiveness of the proposed method for time-varying multi-target tracking using raw observation data.

Recent breakthroughs in algorithms related to deep convolutional neural networks (DCNN) have stimulated the development of various of signal processing approaches, where the specific application of Automatic Target Recognition (ATR) using Synthetic Aperture Radar (SAR) data has spurred widely attention. Inspired by the more efficient distributed training such as inception architecture and residual network, a new feature fusion structure which jointly exploits all the merits of each version is proposed to reduce the data dimensions and the complexity of computation. The detailed procedure presented in this paper consists of the fused features, which make the representation of SAR images more distinguishable after the extraction of a set of features from DCNN, followed by a trainable classifier. In particular, the obtained results on the 10-class benchmark data set demonstrate that the presented architecture can achieve remarkable classification performance to the current state-of-the-art methods.

In stepped-frequency (SF) radar systems, the high-order phase error due to target motion in each burst can blur the range profile seriously. The methods based on motion parameter estimation perform well in most cases. However, the motion parameter estimation is time consuming, which challenges the real-time imaging of a radar target. To solve this problem, a new easy-to-implement variant of SF signal format is proposed in this paper. By employing the new signal, the high-order phase error can be simply eliminated, and the motion parameter estimation is unnecessary, which makes the imaging efficient. The effectiveness of the proposed method is illustrated and analyzed with both simulations and real data.

In optical region, the scattering center model is very useful in scattering analysis, target recognition and data compression. The method based on Hough transformation performs well in most cases. However, the algorithm extracts the scattering centers one by one via a clean method, which is time consuming. To solve this problem, a novel method is proposed in this paper to extract the scattering centers. By searching the estimated 1D scattering centers, the candidate positions for 3D scattering centers are extracted. Then the candidates are discriminated by a clustering based procedure. By employing the new algorithm, the 3D scattering centers can be extracted simply and the clean step is unnecessary, which makes the procedure e-cient. The experiment results of the high-frequency-electro- magnetic data demonstrate the performance of the proposed method.

The modulation frequencies of random stepped frequency radars are randomly distributed over the expected specific bandwidth. The characteristics of random modulation frequencies make it difficult to synthesize the high-resolution range profile fleetly. A zero-padding inverse discrete Fourier transform method is proposed to address the problem. Theoretical analysis shows that the proposed method has the same signal-to-noise ratio improvement performance as the classical methods, but relatively lower computational complexity. The experimental results validate the effectiveness and efficiency of the proposed method.

Non-rigid point matching has received more and more attention. Recently, many works have been developed to discover global relationships in the point set which is treated as an instance of a joint distribution. However, the local relationship among neighboring points is more effective under non-rigid transformations. Thus, a new algorithm taking advantage of shape context and relaxation labeling technique, called SC-RL, is proposed for non-rigid point matching. It is a strategy that joints estimation for correspondences as well as the transformation. In this work, correspondence assignment is treated as a soft-assign process in which the matching probability is updated by relaxation labeling technique with a newly defined compatibility coefficient. The compatibility coefficient is one or zero depending on whether neighboring points preserving their relative position in a local coordinate system. The comparative analysis has been performed against four state-of-the-art algorithms including SC, ICP, TPS-RPM and RPM-LNS, and the results denote that SC-RL performs better in the presence of deformations, outliers and noise.

The electromagnetic scattering mechanism of radar targets in the high-frequency domain can be characterized exactly by geometrical theory of difiraction (GTD) model. In this paper, we propose a novel parameter estimation method for GTD model based on compressed sensing. The sparse characteristic of radar echoes is analyzed, and the parameter estimation problem is converted to one of sparse signal reconstruction. Furthermore, clustering and linear least-minimum-squares algorithms are utilized to improve the accuracy of the result. Compared with several modern spectral estimation techniques, the proposed method gives a more precise estimation of the GTD model parameters, especially the scattering centers. Simulations with synthetic and measured data in an anechoic chamber conflrm the efiectiveness of the method.

The di-culty of focusing high-resolution highly squinted SAR data comes from the serious azimuth-range coupling, which needs to be compensated in the procedure of imaging. Generally, the linear range walk correction (LRWC) can reduce the coupling efiectively, however, it also induces the problem of azimuth-dependence of residual range cell migration (RCM) and quadratic phase. A novel algorithm is proposed to solve this problem in this paper. In this algorithm, the azimuth nonlinear chirp scaling (ANCS) operation is used, which can not only eliminate the azimuth space variation of residual RCM and frequency modulation (FM) rate but also remove the azimuth misregistration. In addition, the range chirp scaling operation is applied to correct the range-dependent RCM. After implementing the unifled RCM correction, range compression and azimuth compression sequentially, the focused SAR image is acquired flnally. The experimental results with simulated data demonstrate that the proposed algorithm outperforms the existing algorithms.

This paper deals with the problem of constant false alarm rate (CFAR) target detection in high-resolution ground synthetic aperture radar (SAR) images based on KK distribution. For the parameter estimation of KK distribution, the semi-experiential algorithm is analyzed flrstly. Then a new estimation algorithm based on the particle swarm optimization (PSO) is proposed, which takes the discrepancies between the histogram of the clutter data and probability density function (PDF) of KK distribution at some selected points as the cost function to search for the optimal parameter values using PSO algorithm. The performance of the two algorithms is compared using Monte-Carlo simulation using the simulated data sets generated under difierent conditions; and the estimation results validate the better performance of the new algorithm. Then the KK distribution, which is proposed for spiky sea clutter originally, is applied to model the real ground SAR clutter data. The goodness-of-flt test clearly show that the KK distribution is able to model the ground SAR clutter much better than some common used model, such as standard K- distribution and Gamma, etc. On this basis, a global CFAR target detection algorithm is presented. The detection threshold is calculated numerically through the cumulative density function (CDF) of KK distribution. Comparing the amplitude of every SAR image pixel with this threshold, the potential targets in ground SAR images can be located efiectively. Then target clustering is implemented to eliminate the false alarm and obtain more accurate target regions. The detection results of the proposed algorithm in a typical ground SAR image show that it has better performance than the detector based on G 0 distribution.

In this paper, a novel forward-looking imaging method based on the compressed sensing is proposed for scanning phased array radar (PAR) in order to improve the azimuth resolution. Firstly, the echo of targets is modeled according to the principle of PAR. Then, it is analyzed why some of the former methods as multi-channel deconvolution are inefiective based on the signal model. Using a widely accepted assumption that dominant scatterers in an interesting area are sparse or compressible, an imaging algorithm based on the compressed sensing is proposed and investigated. This method obtains its high range resolution by transmitting and compressing chirp pulse signal, and improves its azimuth resolution by utilizing the compressed sensing technique. The efiectiveness of the proposed method is illustrated and analyzed with simulations data.

In the condition of high PRF waveforms, several pulse repetition intervals are needed to receive the echoes from a target, which makes the range of the target ambiguous. In this paper, we propose a novel method for range imaging. In our method, the additional phase term produced by range ambiguity is derived, and then considered in establishing the match filter. The high resolution range profiles can be obtained by applying the match filtering step to the echoes. Simulation results demonstrate the validity of this method.

A random distribution over the bandwidth makes the random hopping frequency radar widely use in the synthetic wideband application as stepped-frequency radar. However, the traditional inverse fast Fourier transform is not able to achieve range compression due to the random modulation. In this paper, firstly, the echo of the random hopping frequency radar is derived, and an efficient range imaging method is proposed. Then, the range-velocity ambiguity function of random hopping frequency radar using correlation algorithm is depicted to reveal the coupling interrelationship of the range and Doppler. At last, Monte Carlo simulations are carried on to estimate the relation between side-lobe level and the number of frequencies. At the same time, the theoretical prediction formula provides a solid proof for it.

For the targets whose maneuverability is not too severe, the azimuth echoes in a range bin are modeled as quadratic phase signal after the translational motion compensation. The FrFT algorithm can obtain a focused image by motion parameter estimation and compensation. However, when a range cell contains more than one scatterer, the effects of sidelobes and interference terms will deteriorate the performance of FrFT method. To solve this problem, we add a selecting step for range cells in the imaging scheme. By selecting the dominant-scatterer range cells the estimation performance can be improved obviously. The effectiveness of the proposed method is illustrated and analyzed with simulations data.

In this paper, we focus on the 3D SC model reconstruction from data with wide azimuthal aperture at a single elevation. Since the existing method is difficult to implement for high-frequency signal or large-size target, we propose a novel method to solve this problem. In our approach, the 3D positions of the SCs are estimated from the 1D SCs via a randomized Hough transform (RHT) method. Then the scattering coefficients are refined via a linear least squares algorithm. Comparison with the existing method, the approach has advantages of fast speed, small storage, and high parameter resolution. Experiments demonstrate the effectiveness of the proposed approach.

We introduce a novel high range resolution (HRR) based multi-look automatic target recognition (ATR) method in this paper. First, the scattering center model established offline is used to reduce the data amount that needed in template construction, which makes the method efficient. Then, the correlation among the multiple looks of the same target is considered using the joint sparse representation to update ATR results. Extensive experiments have been carried out on several radar targets predicted by a high-frequency electromagnetic code to evaluate the proposed method compared with several state-of-the-art methods such as mean template least squares (MTLS), and eigen-template-based matched filtering (ETMF). Experimental results demonstrate that the proposed method is effective robust for multi-look ATR.

Fast rotating targets such as gimbaled antennas or propeller blades may cause migration through resolution cells (MTRC) of the high-resolution range profile during the imaging time, which makes the inverse synthetic aperture radar image smeared. To solve this problem, a novel imaging method for fast rotating targets is proposed in this paper. The method is based on the segmental pseudo Keystone transform, which is designed to realize MTRC correction. The fast realization is achieved by employing the discrete match Fourier transform, which makes the algorithm feasible and simple. Experiments with simulated radar data demonstrate the performance of the proposed method.