Internet of Things (IoT) devices often deliver sensitive sensing and control data over wireless links that are inherently broadcast, making passive over-the-air eavesdropping a persistent threat even for simple point-to-point transmissions. Many physical-layer security (PLS) techniques, however, rely on accurate channel knowledge, additional spatial degrees of freedom, or strong secrecy assumptions that are often hard to guarantee in practical IoT links. In this paper, we propose an anti-eavesdropping secure transmission scheme by deliberately embedding controllable artificial interference into a conventional BPSK waveform on the same carrier, thereby forming an interference-embedded waveform transmitted over an AWGN channel. To exploit the common architectural asymmetry in IoT systems, the legitimate receiver located at an IoT gateway/edge node employs an Anti-Eavesdropping Waveform Demodulator (AEWD), a ResNet–self-attention–BiLSTM neural demodulator trained end-to-end to recover bits directly from raw I/Q samples without explicit interference parameter estimation. Under identical channel conditions, conventional model-based receivers that perform deterministic interference suppression followed by demodulation incur substantial BER degradation and frequently exhibit interference-limited error floors. Extensive simulations across a wide range of SNR and SIR demonstrate that AEWD consistently outperforms classical baselines under both single-tone and multi-tone nonstationary interference. The results suggest that waveform-level interference embedding combined with a dedicated neural demodulator can create a practical receiver performance gap to strengthen confidentiality for IoT communications.

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).

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.

With the increase of spaceborne synthetic aperture radar (SAR) platforms, numerous SAR images are available for ship detection applications. Traditional deep learning-based detection methods struggle with the distributional disparities in SAR images acquired from different platforms, arising from differences in radar characteristics and data acquisition conditions. Existing approaches employ domain adaptation (DA) techniques to align domain distribution and thus mitigate distribution divergence. However, due to the inherent specificity of SAR images, i.e., ship targets and background environments exhibit highly visual similarity, these methods may inadvertently destroy the discriminative representations of ship targets, resulting in poor cross-domain detection performance. To alleviate this dilemma, we propose a source-assisted hierarchical semantic calibration (SHSC) framework for ship detection across different satellite SAR images. First, a source-assisted semantic calibration module (SSCM) is designed, which performs multilevel semantic calibration by constructing a source-assisted (SA) detector as a guiding mechanism to preserve the discriminative semantics of ship targets. Then, the uncertainty-aware guided feature-level alignment module (UG-FAM) and instance-level alignment module (UG-IAM) are developed, which effectively capture the crucial ship target attributes by emphasizing the learning of those discriminative samples. Extensive experiments are conducted on the datasets obtained from the TerraSAR, Gaofen-3, Sentinel-1, and RadarSat-2 satellites. The experimental results show that the proposed SHSC method outperforms the other UDA approach by an average of more than 3% on AP in ship target detection accuracy across different satellite SAR images.

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.

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.

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.

To better interpret the inverse synthetic aperture radar (ISAR) imaging results, it is highly desirable to present them in the homogeneous range-cross-range domain, rather than the conventional range-Doppler (RD) domain. This process is referred to as cross-range scaling and the rotating angle velocity (RAV) of the moving target must be estimated first to achieve that goal. In this paper, an efficient cross-range scaling approach based on 2-D discrete wavelet transform (2D-DWT) and pseudopolar fast Fourier transform (PPFFT) is developed. To be exact, first, 2D-DWT is applied to two sequential ISAR images to obtain the dominant feature points based on the fact that the ISAR images are usually redundant for estimating RAV. By doing so, the data dimensional reduction and noise suppression are also realized. After that, second, via the efficient PPFFT, two sequential RD ISAR images are mapped into the pseudopolar coordinate to convert the rotational motion into the translational motion along the pseudo angle direction. Finally, to estimate the RAV, a new normalized correlation cost function is constructed and the Golden section algorithm is employed to efficiently find the optimal RAV. Compared with the conventional methods, the advantages of the proposed method are threefold: 1) the rotation center of a target is no longer required prior; 2) without the interpolation operation and the utilization of data dimensional reduction via 2D-DWT, the computational complexity of the proposed method is significantly reduced;and 3) the accurate RAV estimation is achieved in the case of low signal-to-noise ratio condition. The results from both the simulated and the measured data demonstrate that the proposed approach outperforms the state-of-the-art algorithms in terms of the estimation accuracy and computational complexity.

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.

The detection and parameters estimation of linear frequency-modulated (LFM) signal are important for modern radar applications, but they are also challenged by the fact that echo signal is often of low signal-to-noise ratio (SNR) due to reasons of long imaging distance and/or limited transmitted power, and the target of small size and/or hidden characteristics. To enhance the SNR, in our previous work, a novel coherently integrated cubic phase function (CICPF) was recently developed for the parameters estimation of the multicomponent LFM signal. In the CICPF, the auto-terms are coherently integrated to enhance the performance in the case of low SNR and also to suppress the cross-terms and spurious peaks. In this paper, as an extension of our previous work, the theoretical performance analyses including several important properties and the fast implementation are provided. Furthermore, the asymptotic mean squared error of a CICPF-based estimator as well as the output SNR of a CICPF-based detector are theoretically derived in closed-forms. From the performance point of view, the proposed CICPF attains the Cramer-Rao bound at low input SNR. The complexity analysis also indicates that the CICPF with the nonuniform fast Fourier transform is computationally efficient without needing the interpolation operation and parameter search. Numerical studies of the CICPF confirm the theoretical analysis and demonstrate superior performance of the proposed approach compared with other state-of-the-art approaches, especially under the low-SNR condition. Finally, the proposed CICPF is applied for the ground moving target imaging in synthetic aperture radar. Results using simulated and experimental data demonstrate that it provides an effective means to obtain well-focused image for ground moving targets.

In the aforementioned letter, the authors indicate that with a$2M + 1$sensor uniform linear array (ULA), up to$2M - 1$sources can actually be localized by the proposed algorithm. In this comment, however, we prove that the algorithm will no longer be valid if the number of sources exceeds$M$. A numerical simulation is performed to verify this conclusion.