Synthetic aperture radar (SAR) automatic target recognition (ATR) has been widely used by scholars around the world and achieved excellent results. However, occluded SAR target recognition is still a very challenging task. In this letter, we propose a center local constraint shadow residual network (ClcsrNet) for occluded SAR target recognition. First, the shadow features of SAR images are extracted to improve the robustness of the network to occlusion. Then, the shadow features, the target convolutional features, and the residual features are fused to increase the feature diversity of the network. Finally, we combine the center loss and the local constraint loss to optimize the network. The center loss is used to better cluster the targets in the same class. The local constraint loss is used to maintain the local structure of the target, which increases the separability between different classes. Experiments on the moving and stationary target acquisition and recognition (MSTAR) datasets demonstrate that the proposed ClcsrNet can achieve higher accuracy and better robustness than the comparison algorithms in occluded SAR target recognition.

The detection of small floating targets is a challenging problem for maritime surveillance radar. To achieve effective detection within complex sea clutter background, an innovative graph feature detector is proposed in this letter. First, the received radar sequences are converted into graphs to capture the correlation of signals. Then, three graph features weight peak height (WPH), graph complexity (GC), and graph entropy (GE) of weighted difference visibility graph (WDVG) are proposed. The topological properties of the WDVGs constructed from the phase domain of radar echoes is analyzed, which provides insights into the underlying dynamics structures of the observed phenomena. In the detection part, an improved false alarm rate controllable (FAC) concave detector is designed, which is based on the concave hull-learning algorithm. Experiments results based on the real measured IPIX radar datasets confirm that the proposed method has a better performance compared with the existing feature-based methods, especially under shorter observation time (0.128 s).

With the development of satellite constellations and fierce competition for limited spectrum resources, Mutual Terrain Scattered Interference (MTSI) has become an emerging issue for spaceborne SAR systems. Existing mitigation methods mainly focus on strong wideband MTSI that satisfies the low-rank property. However, in most scenarios, MTSI presents as weak wideband or ultra-wideband interference occupying most of the spectrum, violating the low-rank assumption. This paper introduces two mitigation schemes employing the multi-view subspace representation to tackle these challenges. The first scheme divides the spectrum to construct a clean dictionary composed of subspaces with high correlation, which makes it possible to mitigate wideband MTSI by sparse constraint. Further, based on the differences in amplitude statistical characteristics between polluted and clean pulses, the second scheme utilizes histogram normalization to construct a clean dictionary from the polluted spectrum. Therefore, the wideband and ultrawideband MTSI could be mitigated by solving the subspace clustering problem. Experimental results in the simulated and real measured Sentinel-1 and GaoFen-3 data demonstrate superior image quality improvement by the proposed mitigation schemes.

Radio Frequency Interference (RFI) would lead to degradation of image quality for synthetic aperture radar (SAR) systems, resulting in a waste of observation resources. RFI source localization provides crucial prior information for RFI mitigation and spectrum management, and traditional RFI localization methods for multichannel SAR system suffer from localization ambiguity. This paper derives the RFI model for dual-channel SAR and investigates the mechanisms underlying localization ambiguity. A localization framework is proposed based on the varying characteristics of RFI with platform motion and the slant range difference model between dual-channels. This approach achieves localization by implementing mutual constraints among alternative optimization models. Moreover, an RFI filtering scheme is developed to facilitate the mutual cancellation of RFI between the two channels. The performance of the proposed method is validated using simulated RFI and real-measured RFI scenarios of Chinese Lutan-1 mission. The results indicate that the proposed method effectively mitigates the localization ambiguity, demonstrating high-precision localization performance. Moreover, it can reduce echo amplitude distortion and preserve degrees of freedom while removing RFI effectively.

This work introduces the ongoing initiatives by the "RFI in Remote Sensing Working Group" within the IEEE Standards Association. The Working Group is taking the lead in standardizing the evaluation of radio frequency interference (RFI) impact on spaceborne microwave remote sensing. This standardization effort aims to enhance the monitoring of RFI and improve the effectiveness of sharing information related to it. It is developing a standard titled "P4006 Standard for Remote Sensing Frequency Band Radio Frequency Interference (RFI) Impact Assessment". This paper presents these efforts and highlights the recent activities undertaken by this working group.

Convolutional neural networks (CNNs) have been widely employed in synthetic aperture radar (SAR) target recognition due to their powerful feature extraction capability. However, the performance of CNN-based SAR target recognition algorithms is often affected by imbalanced datasets, in which some classes own plenty of samples and some classes own few samples. To address this issue, this letter proposes a large-margin Gaussian mixture with a ring loss network (LGM-RNet). To improve CNN’s recognition performance for classes with few samples, the algorithm clusters features of each class in the feature space and makes all the data to be equally distributed on a circle. Furthermore, to mitigate the impact of speckle noise in SAR images on target recognition, a denoising method based on Euclidean loss and the total variation loss is introduced. The proposed algorithm aims to improve the accuracy and robustness of imbalanced SAR image target recognition. Experimental results have verified the effectiveness of the proposed algorithm.

Range ambiguity is a common issue for spaceborne synthetic aperture radar (SAR) systems, leading to image quality degradation and subsequent interpretation accuracy. Existing range ambiguity suppression methods mainly rely on raw echo domain processing with specific requirements on prior knowledge. However, most users can only obtain single-look-complex(SLC) products instead of raw echo products. Raw echo obtained from SLC through an inverse focusing process will waste additional time and space resources. This paper proposes a novel scheme to detect and suppress range ambiguity in SLC products to deal with this deficiency. The proposed method designs a detector to extract and suppress the focused range ambiguity based on the characteristic difference between the desired signal and range ambiguity in the fractional transform domain of the SLC image. Experimental results on real measured L-band spaceborne interferometry SAR system verify the detection performance of the proposed method, which is beneficial for subsequent land monitoring applications.

Synthetic Aperture Radar (SAR) is a type of active microwave remote sensing imaging radar that can generate two-dimensional high-resolution images. Its ability to operate in all weather conditions and at all times has led to its widespread use. As a multi-parameter and multi-channel extension of SAR, polarimetric SAR (PolSAR) provides a wealth of scattering information for various applications, including topographic mapping, ocean exploration, polar observation, and target identification. Compared with single-polarization SAR, multi-polarization SAR enhances the information potential of the data by expanding its one-dimensional information, however, this potential cannot be fully realized without a clean SAR echo signal. The electromagnetic environment is becoming increasingly congested with radio frequency interference (RFI) signals, presenting a significant challenge for the subsequent tasks of PolSAR. Although there have been many related studies based on polarization information to carry out the aforementioned applications, there is a lack of research on the joint suppression of interference by using multi-polarization information, and single-polarization data alone is insufficient in effectively mitigating interference. To address these challenges, this paper presents a framework combining multi-polarization data to improve performance of low-rank based methods. Based on the proposed framework, experiments are conducted on real PolSAR data to assess the feasibility of the proposed framework in interference suppression. The results demonstrate that the framework significantly enhances the suppression performance of various low-rank based methods with clearer scene details being recovered.

Satellite imagery plays a critical role in target detection. However, the quality and usability of optical remote sensing images can be severely compromised by atmospheric conditions, particularly haze, which significantly reduces the recognition accuracy of target detection algorithms such as ships. On the other hand, paired training data, i.e., the remote sensing data with or without fog at the same place, are difficult to obtain in real-world scenarios, leading to the failure of many existing dehazing methods. To deal with these issues, this article proposes a Transformer-Guide CycleGAN framework generative adversarial networks (Dehaze-TGGAN) incorporating an extra attention mechanism from the frequency domain. First, an SSA mechanism is proposed by using a 2-D fast Fourier transform (2D FFT) in the spatial domain, which enables the model to understand the relationships within the three-channel frequency domain information and to recover the spectral features of the hazy image through the spectrum encoder block. Then, a pre-training approach using semi-transparent masks (STM), which can effectively simulate hazy conditions by adjusting the transparency of masks, is presented as a key strategy to accelerate the convergence rate. Finally, the applicability of the transformer architecture is extended by incorporating total variation loss (TV Loss). The results of simulated and measured optical remote sensing data show that the recognition accuracy and the efficiency of the proposed algorithm are greatly improved.

In this paper, we describe the attempt of the Frequency Allocation in Remote Sensing Technical Committee (FARS-TC) from the IEEE Geoscience and Remote Sensing Society (GRSS) to standardize the impact assessment of radio frequency interference (RFI) especially in the field of spaceborne remote sensing. Multiple radio services have been sharing a common radio band with remote sensing satellites, resulting in inevitable interferences between each other. In other cases, some services accidentally emit their signal to nearby frequency band. Detecting interference and locating its source is a necessary task to guarantee data integrity. Given that remote sensing satellites carry unique payloads, the effect that RFI have depends on each sensor. For this reason, the RFI impact assessments have been usually developed with one mission or application in mind.Standardizing the impact assessment of RFI will benefit to make a public identification system so that we can monitor RFI effectively. In this paper, we introduce the working group in the IEEE Standards Association named "P4006 Standard for Remote Sensing Frequency Band Radio Frequency Interference (RFI) Impact Assessment" which is handled by the IEEE GRSS FARS-TC and its recent activities.

Synthetic aperture radar (SAR) is a critical imaging technique that is widely used for civil and military tasks, as it is featured with an excellent ability for high resolution imaging. However, due to the severe shortage of SAR images, the performance of automatic target recognition (ATR) is greatly sabotaged. Generative adversarial network (GAN) is often applied for data augmentation of small-sized dataset. In this paper, based on auxiliary classifier GAN (ACGAN) and top-k training technique, we propose double top-k training, which implements a modification during training without any further adjustment on model architecture. The proposed method is to enforce generator to only optimize on generated images that perform well in both discriminator and auxiliary classifier, and discard images of poor performance. We evaluate the generated images via recognition on the moving and stationary target acquisition and recognition (MSTAR) dataset. Recognition accuracy and Fréchet inception distance (FID) score indicate better generation results of the proposed method compared with original ACGAN.

Maritime target detection is one of the most complicated problems in the radar signal processing field. Since traditional constant false alarm rate detection methods rely on the clutter distribution model, the mismatch of the sea clutter model leads to a decrease in the target detection performance. In this paper, the amplitude statistical entropy (ASE) of sea clutter sequence is extracted as a feature to describe the degree of aggregation of the sea clutter amplitude statistical histogram. Then a novel target detection algorithm based on ASE is proposed, which is not affected by the degree of the model matching between sea clutter datasets and statistical model. Finally, the experiment result based on the Canadian IPIX radar datasets confirms the effectiveness of this method.

In this paper, a method using fractional signatures for small target detection is proposed based on fusion of features extracted from both the time-frequency domain and fractional domain by using principal component analysis (PCA) to get the key characteristics for redundancy reduction. The process of reducing feature dimensions is visualized by the t-distributed stochastic neighbor embedding (t-SNE) network, also the simulation based on real dataset offers better performance in small target detection under sea clutter environment.

Space-based radar has the characteristics of all-weather operation, and can accurately provide important data for understanding global environmental changes. On the other hand, with the rapid development of radio technology, space-based radar is facing more and more interference, such as terrestrial interference and inter-satellite interference, which greatly distort the measurements and degrade the image quality. In this paper, a novel interference mitigation method based on multi-temporal coupling analysis is proposed. The temporal-spatial coupling between time-series images could be modeled as low rank, while the interference follows the sparsity constraints due to the time-varying property. The interference extraction and mitigation on remote sensing images is realized by optimization by joint low-rank and sparsity regularization. The experimental results of Sentinel-1A data show that the method can achieve the separation of interference and restore clear remote sensing images with little distortion.

Fractal theory has improved the target detection performance under sea clutter background. However, the traditional fractal methods in time domain or Fourier domain cannot accurately characterize the complex sea clutter properties, which leads to the degradation of the target detection performance under low signal-clutter-ratio (SCR) conditions. This article investigates the multifractal correlation property of sea clutter in the autoregressive (AR) spectrum domain for detection performance improvement. In this work, the traditional target detection problem is converted to a binary classification problem of sea clutter and targets. The refined fractal characteristics in various singularity scale interval and range bins are analyzed, and the AR singularity intensity correlation function width together with the accumulation area of AR multifractal correlation spectrum is extracted as intrinsic features. Then, a simple and efficient fully connected network is developed to realize the classification. Experimental results on real measured marine radar datasets demonstrate that the proposed method can increase the detection probability by about 15% than the state-of-art fractal-based methods under a low SCR condition.

Wide-band interference (WBI) may severely affect the imaging quality of synthetic aperture radar (SAR) systems. Since it highly overlaps with useful signals in the 1-dimensional (1-D) time or frequency domain, the existing WBI mitigation methods usually transform 1-D echoes into a 2-D transform domain. However, they usually suffer from a model mismatch, which results in the loss of the useful signal. To tackle this problem, a novel algorithm combining time-frequency-pulse (TFP) joint characteristics and robust principal component analysis (RPCA) is proposed for WBI mitigation. The TFP joint feature of SAR echo is introduced for interference mitigation for the first time. We first transform the SAR echoes into the time-frequency domain, and construct a new TFP matrix by reshaping the STFT matrices between adjacent pulses. In terms of the WBI-occupied SAR echoes, the short-time Fourier transformation (STFT) in adjacent pulses can be modeled as a combination of a low-rank part (i.e. useful SAR echoes) and a sparse counterpart (i.e. WBIs), which well fits the assumption of RPCA. Then, the TFP matrix is decomposed into the useful signal TFP matrix and the WBI TFP part by taking full advantage of the low-rank and sparse properties. Finally, the WBIs can be reconstructed and subtracted from the echoes to realize interference mitigation. Experimental results on both simulated and measured datasets show that the proposed algorithm not only suppresses WBIs effectively but also preserves useful information as much as possible.

Radio frequency interference (RFI) is a significant threat to accurate microwave remote sensing. The RFI signals manifest themselves in unpredictable locations and patterns in the image, which will cause measurement distortion, image degradation, or even lead to wrong retrievals of the geophysical parameters. Accurate detection of RFI artifacts is a prerequisite step to preserve the overall quality of remote sensing quality. In this paper, a semantic cognitive enhancement network for RFI signature detection is proposed. It employs an encoder-decoder architecture, which incorporates the atrous spatial pyramid pooling, Depthwise convolution, and self-attentional mechanism. Rather than detecting the existence of RFI artifacts for an entire image, the proposed scheme can realize RFI recognition in a pixel-wise manner without setting predefined thresholds. Extensive experimental results on diverse scenarios in Sentinel-1 images with various RFI types are provided, which demonstrates robust detection performance for both strong and weak interference without requiring a large number of training samples.

Radio frequency interference (RFI) is a critical issue for accurate remote sensing by synthetic aperture radar (SAR). Existing literature mainly detects and mitigates RFI in the raw data domain, which is generally not accessible to the end-user. In this article, a novel RFI extraction and mitigation scheme in the image domain is proposed using multitemporal analysis of SAR images. By exploiting the coupling correlation and complementary information among the time-series images, the background landscape could be modeled as relatively stationary with the low-rank property. Meanwhile, the radiometric artifacts corresponding to RFI could be well extracted and characterized by the sparse components. Extraction and mitigation of RFI signatures could be achieved simultaneously via a joint iterative optimization process. Experimental results on typical real-measured Sentinel-1 datasets acquired in different regional areas with various RFI types demonstrate the validity of the proposed method.

Remote sensing image dehazing is of great scientific interest and application value in both military and civil fields. In this article, we propose an enhanced attention-guide generative adversarial network (GAN) network, Dehaze-AGGAN, to solve the remote sensing images dehazing problem, which does not require paired training data. Since haze images have a great influence on remote sensing object detection, the dehazing of remote sensing images has become significantly important. Typical image dehazing methods require a hazy input image and its ground truth in a paired manner, while paired training data are usually not available in the field of remote sensing. To solve this problem, we propose the Dehaze-AGGAN network and train it by feeding unpaired clean and hazy images into the model. We present a novel total variation loss combined with the cycle consistency loss to eliminate wave noise and improve the target edge quality in the test dataset. Moreover, we present a new dehazing dataset called remote sensing dehazing dataset (RSD), which contains 7000 simulate and real hazy images including 3500 warship images and 3500 civilian ship images, and evaluate our method in the dataset. We conduct experiments on RSD. Extensive experiments demonstrate that the proposed Dehaze-AGGAN is effective and has strong robustness and adaptability in different settings.

Slow-moving target detection in strong clutter background is a critical issue for the ground-based radar system. To detect the slow-moving target effectively, a novel multi-scan joint target detection method via principal component analysis (RPCA) is proposed. For radar echoes, there are two useful properties: 1) Stationary ground clutters have low-rank property, since the clutters in adjacent scan intervals are almost similar; 2) Moving targets have the sparse characteristic, due to their variation of position and sparsely distributed. Thanks to these two properties, moving targets can be separated from the stationary clutters via RPCA. Compared with the moving target indicator (MTI) method, the experimental results demonstrate that the proposed algorithm not only can suppress clutters effectively, but also preserve the moving target as much as possible.

Radio frequency interference (RFI) has been a pervasive and critical issue for space-borne synthetic aperture radar (SAR). The presence of RFI could lead to incorrect image interpretation and biased parameter retrieval, making the RFI detection a necessity to preserve overall data quality. In this paper, we propose an approach for detecting RFI signals in SAR raw data using time-frequency semantic analysis. Employing the U-Net convolutional neural network enables identification of target echoes and RFI signatures in 2D time-frequency representation with high probability. The detection process is realized without setting predefined thresholds, and could achieve superior performance without requiring large number of training samples.

As the radio spectrum available to spaceborne synthetic aperture radar (SAR) is restricted to certain limited frequency intervals, there are many different spaceborne SAR systems sharing common frequency bands. Due to this reason, it is reported that two spaceborne SARs at orbit cross positions can potentially cause severe mutual interference. Specifically, the transmitting signal of an SAR, typically linear frequency modulated (LFM), can be directly received by the side or back lobes of another SAR’s antenna, causing radiometric artifacts in the focused image. This article tries to model and characterize the artifacts and study efficient methods for mitigating them. To this end, we formulate an analytical model for describing the artifact, which reveals that the mutual interference can introduce a 2-D LFM radiometric artifact in image domain with a limited spatial extent. We show that the artifact is low-rank based on a range–azimuth decoupling analysis and 2-D high-order Taylor expansion. Based on the low-rank model, we show that two methods, i.e., principal component analysis and its robust variant, can be adopted to efficiently mitigate the artifact via processing in the image domain. The former method has the advantage of fast processing speed, for example, a subswath of Sentinel-1 interferometric wide swath image can be processed within 70 s via blockwise processing, whereas the latter provides improved accuracy for sparse pointlike scatterers. Experiment results demonstrate that the radiometric artifacts caused by mutual interference in Sentinel-1 level-1 images can be efficiently mitigated via the proposed methods.

Wide-band interference (WBI) is a critical issue for synthetic aperture radar (SAR), which may severely affect the imaging quality of SAR systems. To suppress WBI effectively, a novel interference suppression algorithm based on robust principal component analysis (RPCA) in time-frequency-pulse (TF-P) domain is proposed. For SAR echoes in TF-P domain, there are two useful properties: 1) The TF characteristic of useful signal in adjacent pulse are similar, indicating that useful signal has low-rank property; 2) Due to its variation of position and sparsely distrusted in TF-P domain, WBI has sparse characteristic. According to these properties, RPCA method is applied to decompose the TF-P matrix into a low-rank matrix (i.e. useful signal) and a sparse matrix (i.e. WBI). Finally, the WBIs can be reconstructed and subtracted from the echoes to realize the interference suppression. The experimental results of simulated data demonstrate that the proposed algorithm not only can suppress interference effectively, but also preserve the useful information as much as possible.

In synthetic aperture radar (SAR) system, the separation and reconstruction of useful signal from Narrow-band interference (NBI) and Wide-band interference (WBI) components is a challenging problem. In this paper, a novel time-varying interference suppression algorithm is proposed based on the signal synthesis from joint time-frequency (TF) distribution. This algorithm makes full use of two TF representations: Wigner distribution (WD) and cross WD (CWD). After cross-terms elimination, these two TF representations are equal or close to the sum of WDs or CWDs of individual signal components, respectively. Based on this property, interferences can be separated and reconstructed by matrix rearrangement and eigenvalue decomposition (EVD). Compared with the traditional SSM (TSSM), the proposed algorithm has two advantages: 1) it is more accurate, since it avoids the approximate interpolation to WD; 2) it is quite time-saving, due to its matrix obtained by fast Fourier transform (FFT) and matrix rearrangement instead of the discrete Fourier transform (DFT). Experimental results demonstrate the effectiveness of the proposed approach in terms of accuracy and computational complexity.

The contest against electromagnetic spectrum are making the electromagnetic environment more and more congested. Synthetic aperture radar (SAR) requires larger bandwidth to obtain finer resolution, and thus inevitably affected by radio emitters sharing the same frequency band. Most of the radio frequency interference (RFI) originated from the terrestrial emitters, while there are also rare cases with interfering signals from space-borne satellites. In this paper, we analyzed the mechanism of terrain scattered interference (TSI) from space-borne RFI sources, and provide a case study of the interference signatures in Sentinel-1 data.

To overcome the shortcomings of fractal analysis in the time domain and Fourier transform domain, this letter mainly studies the joint fractal property of sea clutter of autoregressive (AR) spectrum and its application on weak target detection. Since the box-counting dimension is the most popular parameter to describe a fractal set and simply to calculate, we combined the box-counting dimension with AR spectrum estimate theory, which considers the correlation property of sea clutter series. Moreover, the intercept is regarded as an auxiliary feature for target detection. Then the box-counting dimension and intercept are used as a 2-D feature to analyze the joint fractal characteristic of AR spectrum, and a novel weak target detection algorithm is proposed based on the joint fractal characteristic of AR spectrum. In fact, radar target detection can be regarded as a binary-classification question, and the support vector machine (SVM) is applied to target detection. Finally, real S-band sea clutter data sets are analyzed. Compared to the traditional CFAR method and existing fractal methods, the proposed method improves the detection performance without complex computations.

In this paper, a frequency diverse array-based directional modulation with artificial noise synthesis scheme is proposed to enhance the physical layer security of wireless communications. We aim to optimize the secrecy performance by jointly optimizing the frequency offsets, the beamforming vector, and the artificial-noise projection matrix (ANPM). Specifically, we address the physical layer security problems for known locations of proximal eavesdropper (Eve) and legitimate user (LU). The beamforming vector and frequency offsets are designed to preserve the signal power at LU. The ANPM is calculated to minimize the effect of AN on LU. Furthermore, we extend our approach to the case of multi-LUs with unknown Eve locations. Being different from the case of a single LU, the frequency offsets across array antennas are optimized to equally allocate transmitted power to each LU. The numerical results show that the proposed method can provide a higher secrecy performance than conventional DM methods. In the case of multi-LUs with unknown Eve locations, the proposed method can provide a high secrecy capacity while achieving almost equal achievable capacity to each LU.

In this paper, we proposed a ship detection method based on deep convolutional neural networks for PolSAR images. The proposed ship detector firstly segments PolSAR images into sub-samples using a sliding window of fixed size to effectively extract translational-invariant spatial features. Further, the modified faster region based convolutional neural network (Faster-RCNN) method is utilized to realize ship detection for ships with different sizes and fusion the detection result. Finally, the proposed method was validated using real measured NASAlJPL AIRSAR datasets by comparing the performance with the modified constant false alarm rate (CFAR) detector. The comparison results demonstrate the validity and generality of the proposed detection algorithm.

Based on the fuzzy suppressed c-means clustering algorithm, a new method is developed for extracting auroral oval regions from images acquired by the Ultraviolet Imager aboard the POLAR satellite. Compared with different variations of fuzzy c-means clustering methods, suppressed fuzzy c-means clustering was proposed with the intention of improving convergence rate by modifying membership values, which is more suitable for studying auroral behavior over time with considering a series of images. However, traditional suppressed c-means clustering algorithms employ the same suppressed parameter for modifying fuzzy membership degrees of all pixels, ignoring the fact that image characteristics varies from one auroral oval images to another. In this paper, the technique parameters which is set beforehand will be automatically selected according to the intrinsic characteristic of each auroral oval image. Moreover, corresponding operations are devised for modifying membership values of different pixels according to their real needs, which makes it clear to decide whether to proceed with further determination or just make decision on the basis of already obtained analysis results. Experimental results on auroral oval images acquired from an online database collected by NASA Polar satellite's Ultraviolet Imager indicate that the proposed method extracts more accurate auroral oval regions than traditional suppressed c-means clustering method in most cases.

Narrow-band interference (NBI) and Wide-band interference (WBI) are common jamming signals against synthetic aperture radar (SAR), in which the imaging quality can be degraded severely. To effectively suppress NBI and WBI, a novel time-frequency iteration decomposition method is proposed based on ambiguity function iteration decomposition. In this algorithm, echoes contaminated by interferences are identified in the radon ambiguity function (RAF) domain. After that, the masked method and signal synthesis method are utilized to extract and recovery interferences from the ambiguity function. Finally, the reconstructed interferences are subtracted from the echoes, and the well-focused SAR imagery is obtained by conventional imaging methods. The simulation and measured data results demonstrates that the proposed algorithm not only suppresses interference efficiently but also preserves the useful information as much as possible.

The proliferation of radio technology increase the likelihood of interference to active remote sensing systems, especially for those high-resolution synthetic aperture radar (SAR) systems with large bandwidth. The presence of radio frequency interference (RFI) in SAR data would affect the image quality and subsequent image interpretation results. Nowadays, radio services have an increasing demand for greater bandwidth, and the contamination bandwidth by RFI are becoming wider. This paper discusses the extreme case that SAR echoes are contaminated by ultra wide-band RFI, i.e., the bandwidth of RFI is relatively larger than the transmitted signal, traditional methods would fail due to large signal loss. In this paper, we proposed a mitigation method using nonnegative matrix factorization with prior constraints like independence and sparsity. The experimental results indicate the effectiveness of the proposed method.

Narrow-band interference (NBI) is a common jamming signal against synthetic aperture radar (SAR), in which the imaging quality can be degraded severely. To suppress NBI effectively, a novel interference suppression algorithm using robust principal component analysis (RPCA) based time-frequency signal separation is proposed. The RPCA algorithm is introduced for time-frequency signal separation for the first time. The experimental results of simulated and measured data demonstrate that the proposed algorithm not only can suppress interference effectively, but also preserve the useful information as much as possible.

Multiple informative polarimetric descriptors can be computed from direct measurements of polarimetric covariance matrix and target decomposition theorems. Under the tensor algebra framework, each pixel is modeled as a third-order tensor object by combining multi-features and incorporating neighborhood spatial information together. Typically, the tensor object is of high correlation and redundancy in both the spatial and feature dimensions. In this paper, we propose a feature extraction method using the multilinear principal component analysis to facilitate the classification process. Experimental results in comparison with principal component analysis, independent component analysis and linear discriminate analysis demonstrate that the classification accuracy is significantly improved since the extracted features by the proposed method are more discriminative.

This paper presents an automatic ship detection algorithm for polarimetric synthetic aperture radar (PolSAR) data. Based on the non-Gaussian K-Wishart distribution model for complex backscattering coefficients, the PolSAR image is clustered automatically by a modified expectation maximization algorithm. A goodness-of-fit test is incorporated to improve the model fitness of the cluster iteratively. Then, the SPAN of ship cluster center is used to detect ships. Finally, the experimental results of a real measured UAVSAR dataset show that the proposed algorithm could improve the ability of weak target detection while reduces the rate of false alarm and miss detections.

Radio frequency interference is a major issue for synthetic aperture radar (SAR) imaging. Especially with the presence of wideband interference (WBI), the signal-to-interference ratio (SIR) of the measurements is greatly degraded, thus making it difficult to produce a high-quality SAR image. Compared with narrow-band interference (NBI), WBI occupies a larger bandwidth and is more complicated to deal with. This paper addresses the detection and mitigation of WBI in high-resolution airborne SAR data. First, a WBI-corrupted echo is characterized in the time-frequency representation by utilizing the short-time Fourier transform. In this way, the original range-spectrum WBI mitigation problem can be simplified into a series of instantaneous-spectrum NBI mitigation problems. For each instantaneous spectrum, the existence of interference signal can be identified according to the negentropy-based statistical test. Furthermore, the interference signal is mitigated by notch filtering or eigensubspace filtering. The experimental results of the simulated data, as well as real measured data sets, show that the proposed scheme is effective in suppressing the interference signal and in obtaining a high-quality image.

Traditionally, the factorization method is applied to reconstruct the 3D geometry of a target from its sequential inverse synthetic aperture radar images. However, this method requires performing cross-range scaling to all the sub-images and thus has a large computational burden. To tackle this problem, this paper proposes a novel method for joint cross-range scaling and 3D geometry reconstruction of steadily moving targets. In this method, we model the equivalent rotational angular velocity (RAV) by a linear polynomial with time, and set its coefficients randomly to perform sub-image cross-range scaling. Then, we generate the initial trajectory matrix of the scattering centers, and solve the 3D geometry and projection vectors by the factorization method with relaxed constraints. After that, the coefficients of the polynomial are estimated from the projection vectors to obtain the RAV. Finally, the trajectory matrix is re-scaled using the estimated rotational angle, and accurate 3D geometry is reconstructed. The two major steps, i.e., the cross-range scaling and the factorization, are performed repeatedly to achieve precise 3D geometry reconstruction. Simulation results have proved the effectiveness and robustness of the proposed method.

In this paper, we make theoretical analysis on cross-tier handover in Small Cell Networks (SCNs). The cross-tier handover is defined as the handover occurred from a macro cell tier to a small cell tier, or vice versa. Based on stochastic geometry, we first propose a linear approximation approach to study the coverage of small cells. Using the proposed method, we then derive analytical expressions for cross-tier handover rate and sojourn time inside a small cell, which are the key parameters in mobility performance evaluation of an SCN. According to the analysis, we show that the small cell coverage area can be well represented by a biased circle, and the expected value of small cell size is inversely proportional to the square root of the BS density in the macro cell tier. Another important observation is that the handover rate and sojourn time change linearly with the reciprocal of small cell radius. These analytical results could provide fundamental supports for improving mobility management in SCNs.

Radio frequency interference is a major issue for synthetic aperture radar (SAR) imaging. Especially with the presence of wide band interference (WBI), the signal to interference and noise ratio of the measurements are greatly degraded, and thus makes it difficult to obtain high quality SAR image. In this paper, we analyzed the WBI signatures in a real measured data, and addressed the WBI mitigation problem by using the Eigensubspace filtering on the instantaneous spectra. Experimental results of the real measured data show that the proposed scheme is effective for suppressing the interference signal and for obtaining high-quality image.

For polarimetric synthetic aperture radar (PolSAR) data, various polarimetric signatures can be obtained by target decomposition techniques, which are of great help for characterizing the land cover. It is straightforward to combine these polarimetric features together and formulate them as a third-order polarimetric feature tensor. However, how to make full use of the abundant information provided by these polarimetric features remains a challenge. A feasible solution is applying feature extraction (FE) techniques on the high-dimensional polarimetric manifold to obtain a lower dimensional intrinsic feature set. Common FE methods, such as principal component analysis (PCA), independent component analysis (ICA), etc., use matrix linear algebra and require rearranging the original tensor into a matrix. This leads to the loss of the spatial information of the PolSAR data. In this paper, to jointly take advantage of the spatial and feature information, a novel FE scheme incorporating ICA with the tensor decomposition techniques is proposed. After applying the proposed FE method on the third-order polarimetric feature tensor, each PolSAR image pixel is represented by a low-dimensional intrinsic feature vector. Furthermore, these feature vectors are fed to the k-nearest neighbor (KNN) classifier and support-vector-machine classifier for supervised classification. Simulated data, together with two measured data sets, i.e., Flevoland of Airborne Synthetic Aperture Radar (AIRSAR) and Québec City of Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), are utilized to evaluate the performance of the proposed method. For comparison purpose, several classical and advanced FE methods, such as PCA, ICA, Laplacian eigenmaps, and LRTAdr- (K1,K2,p), are also applied. The experimental results demonstrate the superiority of the proposed FE method in three folds: 1) The extracted features by the proposed method are more discriminative, characterized by the high separability in the scatterplots; 2) the classification accuracy is improved as much as approximately 7% compared with the complex Wishart classifier; and 3) the proposed method is computational efficient and has fast convergence.

With the development of target decomposition theorems for polarimetric synthetic aperture radar (PolSAR) data, various informative polarimetric descriptors could be obtained. The redundancy among these descriptors poses a hindrance to accurate classification. In this paper, we propose a tensor-based dimension reduction technique, which aims to obtain a lower-dimensional intrinsic feature set from the high-dimensional polarimetric manifold. We combine 48 polarimetric features together and formulate them as a third-mode tensor. The spatial information is taken into consideration for feature reduction. Experimental results in comparison with principal component analysis (PCA), independent component analysis (ICA) and Laplacian Eigenmaps (LE) proves its effectiveness.

Narrow-band interference (NBI) poses a hindrance to high quality imaging for synthetic aperture radar (SAR). In this paper, we addressed the NBI suppression problem by introducing two advanced techniques: the complex empirical mode decomposition (CEMD) and the independent component analysis (ICA). Both of these two methods utilize the statistical difference between the useful radar echoes and NBI. They decompose the contaminated pulse into some basis signals, and select out the basis that corresponding to NBI. Then the contribution of NBI is excised by filtering out the corresponding NBI components. We compare the performance of these advanced methods with the conventional notching filtering method. The experimental results of the real dataset show the effectiveness of the proposed methods.

Clustering is a promising interference mitigation scheme in dynamic TDD systems. However, most previous works just took large-scale path loss or coupling loss as criteria of the clustering schemes, thus the throughput performance would be limited by the varying traffic requirements among different small cells within one cluster. In this paper, a novel dynamic cluster-based Interference Mitigation and Traffic Adaptation (IMTA) scheme is proposed and evaluated with dense deployment of small cells (SCs). Firstly, a new clustering criterion named Differentiating Metric (DM) is defined. Based on the defined DM value, a DM matrix is formed and further presented by a clustering graph. In the clustering graph, the dynamic clustering strategy is mapped to a MAX K-CUT problem, which is addressed in polynomial time by a proposed heuristic clustering algorithm. Furthermore, the system level simulation results demonstrate a promising improvement on uplink traffic throughput (UTP) in our proposed scheme compared with traditional clustering schemes.

In LTE-Advanced (LTE-A) and beyond networks, deploying complementary small cells on an existing macro layer is recognized as an attractive solution to improve the network capacity and provide seamless broadband services in local areas. However, existing cell discovery mechanism is tailored for homogeneous networks (macro only). User Equipment (UE) can't energy-efficiently detect the small cells on a dedicated carrier or maximally exploit the offloading opportunities provided by such heterogeneous deployments. In this paper, we propose a Predictive Connection time based Inter-frequency Measurement (PCIM) solution to cope with the problems. With the aid of the positioning results, the small cell connection time is derived, both user velocity and moving direction are taken into account. Using 3rd Generation Partnership Project (3GPP) LTE-A Heterogeneous Network (HetNet) mobility evaluation methodology, the proposed PCIM scheme is compared with some currently standardized techniques. Simulation results provide insights on the small cell discovery schemes in terms of energy efficiency and small cell usage efficiency, and demonstrate the benefits of the proposed PCIM scheme over the other alternatives.

The mitigation of narrow-band interference (NBI) is an appealing topic in the synthetic aperture radar (SAR) community. It is an underdetermined single-channel separation problem. This paper proposes a method for NBI mitigation using the independent subspace analysis. First, each single pulse is transformed onto a manifold time-frequency distribution by the short-time Fourier transform (STFT). Then, the singular value analysis is carried out to extract the prominent features corresponding to the NBIs. Next, independent component analysis is employed to obtain statistically independent basis components. Furthermore, the independent subspaces corresponding to NBI are reconstructed and subtracted from the raw signal space. The signal with NBI mitigated is resynthesized by inverse STFT. Finally, after processing all the pulses, a well-focused SAR imagery is obtained by a conventional imaging algorithm. Experimental results of simulated and measured data have demonstrated the effectiveness of the proposed method.

The narrow-band interference (NBI) is a common jamming signal against synthetic aperture radar (SAR), which can degrade the imaging quality severely. This paper proposes a new method for NBI suppression in the data domain based on the independent component analysis (ICA). In this method, echoes contaminated by the NBI are identified in the frequency domain. Next, time filtering and whitening are performed to the identified echoes. Then, the ICA is carried out to decompose the echoes into a series of basis signals, and the jamming components are selected by thresholding. Finally, the NBI is reconstructed and subtracted from the echoes, and the well-focused SAR imagery is obtained by conventional imaging methods. The proposed method copes well with the time-varying NBI with little signal loss. Results of simulated and measured data have proved the validity of the proposed method.

Based on the synthetic aperture radar (SAR) geometric model, a novel, fast algorithm of large scene deceptive jamming against the space-borne SAR is proposed. First, we divide the jamming scene template into sub-templates according to the depth of focus in the range dimension. Next, each sub-template is decomposed into the slow-time-dependent and slow-time-independent terms in the range frequency-azimuth time domain. The slow-time-independent terms are generated off-line while the slow-time-dependent terms are generated by real-time 1-D frequency modulation. Then, the sub-templates are convolved with the intercepted SAR signals simultaneously. Finally, fast deceptive jamming is achieved by incorporating all the sub-templates together. In the proposed method, the two-step realization of the sub-templates and the parallel sub-block processing improves the algorithm efficiency. The simulation results prove the validity of the proposed algorithm.