Search Results (150)

Remote sensing tasks, such as land cover classification, are increasingly becoming multimodal problems, where information from multiple imaging devices, complementing each other, can be fused. In particular, synergies between optical and synthetic aperture radar (SAR) are widely recognized to be beneficial in a variety of tasks. At the same time, archival of multimodal imagery for global coverage poses significant storage requirements due to the multitude of available sensors, and their increasingly higher resolutions. In this paper, we exploit redundancies between SAR and optical imaging modalities to create a joint encoding that improves storage efficiency. A novel neural network design with progressive attentive fusion modules is proposed for joint compression. The model is also promptable at test time with a desired tradeoff between the input modalities, to enable flexibility in the fidelity of the joint representation to each of them. Moreover, we show how end-to-end optimization of the joint compression model, including its modality tradeoff prompt, allows for better accuracy on downstream tasks leveraging multimodal inference when a constraint on the rate is to be met. Full article

The continuous progress of synthetic aperture radar (SAR) imaging has led to a growing emphasis on the challenges involved in data acquisition and processing. And the challenges in data acquisition and processing have become increasingly prominent. However, traditional SAR imaging models are limited by their large demand for data sampling and slow image reconstruction speeds, which is particularly prominent in large-scale scene applications. To overcome these limitations, this study proposes an innovative L₁-Total Variation (TV) regularization sparse SAR imaging algorithm. The submitted algorithm constructs an imaging operator and an echo simulation operator to achieve decoupling in the azimuth and range dimensions, respectively, as well as to reduce the requirement for sampling data. In addition, a Newton acceleration iterative method is introduced to the optimization process, aiming to accelerate the speed of image reconstruction. Comparative analysis and experimental validation indicate that the proposed sparse SAR imaging algorithm outperforms conventional methods in resolution, target localization, and clutter suppression. The results suggest strong potential for rapid scene reconstruction and real-time monitoring in complex environments. Full article

The contemporary challenge in remote sensing lies in the precise retrieval of increasingly abundant and high-resolution remotely sensed images (RS image) stored in expansive data warehouses. The heightened spatial and spectral resolutions, coupled with accelerated image acquisition rates, necessitate advanced tools for effective data management, retrieval, and exploitation. The classification of large-sized images at the pixel level generates substantial data, escalating the workload and search space for similarity measurement. Semantic-based image retrieval remains an open problem due to limitations in current artificial intelligence techniques. Furthermore, on-board storage constraints compel the application of numerous compression algorithms to reduce storage space, intensifying the difficulty of retrieving substantial, sensitive, and target-specific data. This research proposes an innovative hybrid approach to enhance the retrieval of remotely sensed images. The approach leverages multilevel classification and multiscale feature extraction strategies to enhance performance. The retrieval system comprises two primary phases: database building and retrieval. Initially, the proposed Multiscale Multiangle Mean-shift with Breaking Ties (MSMA-MSBT) algorithm selects informative unlabeled samples for hyperspectral and synthetic aperture radar images through an active learning strategy. Addressing the scaling and rotation variations in image capture, a flexible and dynamic algorithm, modified Deep Image Registration using Dynamic Inlier (IRDI), is introduced for image registration. Given the complexity of remote sensing images, feature extraction occurs at two levels. Low-level features are extracted using the modified Multiscale Multiangle Completed Local Binary Pattern (MSMA-CLBP) algorithm to capture local contexture features, while high-level features are obtained through a hybrid CNN structure combining pretrained networks (Alexnet, Caffenet, VGG-S, VGG-M, VGG-F, VGG-VDD-16, VGG-VDD-19) and a fully connected dense network. Fusion of low- and high-level features facilitates final class distinction, with soft thresholding mitigating misclassification issues. A region-based similarity measurement enhances matching percentages. Results, evaluated on high-resolution remote sensing datasets, demonstrate the effectiveness of the proposed method, outperforming traditional algorithms with an average accuracy of 86.66%. The hybrid retrieval system exhibits substantial improvements in classification accuracy, similarity measurement, and computational efficiency compared to state-of-the-art scene classification and retrieval methods. Full article

Frequency agile radar (FAR) exhibits robust anti-jamming capabilities and a superior low probability of intercept performance due to its randomized carrier frequency (CF) and pulse repetition frequency (PRF) hopping sequences. The advent of compressed sensing (CS) theory has effectively addressed the coherent processing challenges of frequency agile signals. Nonetheless, the reconstructed results often suffer from elevated sidelobe levels, which lead to significant sparse recovery errors. The performance of sparse reconstruction is greatly influenced by the correlation between the dictionary matrix columns. Specifically, weaker correlation usually means better target detection performance and lower false alarm probability. Consequently, this paper adopts the maximum coherence coefficient (MCC) between the dictionary matrix columns as the cost function. In addition, in order to reduce the correlation of the dictionary matrix and improve the target detection performance, a genetic algorithm (GA) is employed to jointly optimize the CF hopping coefficients and PRFs of the FAR. The echo of optimized signals is subsequently reconstructed using the alternating direction method of multipliers (ADMM) algorithm. Simulation results demonstrate the effectiveness of the proposal. Full article

Inverse synthetic aperture radar (ISAR) technology is widely used in the field of target recognition. This research addresses the image reconstruction error in sparse imaging for bistatic radar systems. In this paper, sparse imaging technology is studied, and a sparse imaging recovery algorithm based on an improved Alternating Direction Method of Multipliers is proposed. The algorithm accelerates the convergence of the algorithm by dynamically adjusting iterative parameters in the iterative process. Experiments show that the algorithm proposed in this paper has lower relative recovery error in the case of different noise levels and sparsity, and it can be concluded that the algorithm proposed in this paper has a lower relative recovery error than the ADMMs (Alternating Direction Method of Multipliers). Full article

Microwave computational imaging (MCI) based on coded apertures does not rely on relative motion between the radar platform and the target, enabling forward-looking imaging. The performance of MCI depends on the computational methods and modulation of the coded aperture, particularly its design. However, current research methods treat the optimization of the coded aperture and computational imaging processing as independent tasks, with no unified framework to link these two aspects, limiting the potential for improving system performance. This paper proposes a novel deep learning-based MCI framework that jointly optimizes the coded aperture and image reconstruction process. Unlike traditional methods that decouple these two stages, our approach trains the sensing and reconstruction networks in an end-to-end fashion. The key novelty lies in constructing an end-to-end imaging network based on a convolutional neural network (CNN) where the coded aperture is modeled as a convolutional layer within the network. Physical constraints on the coded aperture are enforced by adding regularizers to the loss function. Simulation experiments demonstrate that under low signal-to-noise ratio (SNR) and low compression ratio conditions, the proposed method improves peak signal-to-noise ratio (PSNR) by 5 dB to 8 dB, enhances SSIM by 10% to 15%, and reduces relative imaging error by 0.5% to 1%. Full article

High-precision and high-resolution scattering data play a crucial role in remote sensing applications, including ocean environment monitoring, target recognition, and classification. This paper proposes a deep learning-based model aimed at enhancing and reconstructing the spatial resolution of Sentinel-1 backscatter NRCS (Normalized Radar Cross Section) data for China’s offshore seas, including the Bohai Sea, Yellow Sea, East China Sea, Taiwan Strait, and South China Sea. The proposed model innovatively integrates a Self-Attention Feature Fusion based on the Weighted Channel Concatenation (SAFF-WCC) module, combined with the Global Attention Mechanism (GAM) and High-Order Attention (HOA) modules. The feature fusion module effectively regulates the proportion of each feature during the fusion process through weight allocation, significantly enhancing the effectiveness of multi-feature integration. The experimental results show that the model can effectively enhance the fine structural features of marine targets when the resolution is doubled, though the enhancement effect is slightly diminished when the resolution is quadrupled. For high-resolution data reconstruction, the proposed model demonstrates significant advantages over traditional methods under a scale factor of 2 across four key evaluation metrics, including PSNR, SSIM, MS-SSIM, and MAPE. These results indicate that the proposed deep learning-based model is not only well-suited for scattering data from China’s offshore seas but also provides robust support for subsequent research on ocean target recognition, as well as the compression and transmission of SAR data. Full article

Sparse Synthetic Aperture Radar (SAR) imaging has garnered significant attention due to its ability to suppress azimuth ambiguity in under-sampled conditions, making it particularly useful for high-resolution wide-swath (HRWS) SAR systems. Traditional compressed sensing-based sparse SAR imaging algorithms are hindered by range–azimuth coupling induced by range cell migration (RCM), which results in high computational cost and limits their applicability to large-scale imaging scenarios. To address this challenge, the approximated observation-based sparse SAR imaging algorithm was developed, which decouples the range and azimuth directions, significantly reducing computational and temporal complexities to match the performance of conventional matched filtering algorithms. However, this method requires iterative processing and manual adjustment of parameters. In this paper, we propose a novel deep neural network-based sparse SAR imaging method, namely the Self-supervised Azimuth Ambiguity Suppression Network (SAAS-Net). Unlike traditional iterative algorithms, SAAS-Net directly learns the parameters from data, eliminating the need for manual tuning. This approach not only improves imaging quality but also accelerates the imaging process. Additionally, SAAS-Net retains the core advantage of sparse SAR imaging—azimuth ambiguity suppression in under-sampling conditions. The method introduces self-supervision to achieve orientation ambiguity suppression without altering the hardware architecture. Simulations and real data experiments using Gaofen-3 validate the effectiveness and superiority of the proposed approach. Full article

Multiple-Input Multiple-Output (MIMO) radar enjoys the advantages of a high degree of freedom and relatively large virtual aperture, so it has various forms of applications in several aspects such as remote sensing, autonomous driving and radar imaging. Among all multiplexing schemes, Time-Division Multiplexing (TDM)-MIMO radar gains a wide range of interests, as it has a simple and low-cost hardware system which is easy to implement. However, the time-division nature of TDM-MIMO leads to the dilemma between the lower Pulse Repetition Interval (PRI) and more transmitters, as the PRI of a TDM-MIMO system is proportional to the number of transmitters while the number of transmitters significantly affects the resolution of MIMO radar. Moreover, a high PRI is often needed to obtain unambiguous imaging results for MIMO-SAR 3D imaging applications on a fast-moving platform such as a car or an aircraft. Therefore, it is of vital importance to develop an algorithm which can achieve unambiguous TDM-MIMO-SAR 3D imaging even when the PRI is low. Inspired by the motion compensation problem associated with TDM-MIMO radar imaging, this paper proposes a novel imaging algorithm which can utilize the phase shift induced by the time-division nature of TDM-MIMO radar to achieve unambiguous MIMO-SAR 3D imaging. A 2D-Compressed Sensing (CS)-based method is employed and the proposed method, which is called HPC-2D-FISTA, is verified by simulation data. Finally, a real-world experiment is conducted to show the unambiguous imaging ability of the proposed method compared with the ordinary matched-filter-based method. The effect of velocity error is also analyzed with simulation results. Full article

Drone-based synthetic aperture radar (SAR) systems have increasingly gained attention due to their potential for rapid surveillance in localized areas. This paper presents a novel approach to SAR processing for subsurface target detection from a lightweight drone platform. The limited processing capacity and memory resources of small SAR platforms demand efficient recovery performance for high-resolution imaging. Compressed sensing (CS) algorithms are widely used to mitigate data storage requirements, yet they often suffer from challenges related to computational burden and detection errors. CS theory exploits signal sparsity and the incoherence of sensing matrices to reconstruct target information from reduced data measurements. Although random sensing matrices are commonly employed to ensure the independence of measured data, they incur high computational cost and memory resources. While deterministic sensing matrices provide fast data recovery, they suffer from increased internal interference, leading to degraded performance in noisy environments. This paper proposes a novel hybrid sensing matrix and recovery algorithm for efficient target detection in small drone-based SAR platforms. After establishing the principles of signal sampling and recovery, SAR imaging simulations are conducted to evaluate the performance of the proposed method with respect to data compression, processing speed, and recovery accuracy. For verification, a custom-built drone SAR platform is utilized to recover subsurface targets obscured by high-clutter backgrounds. Experimental results demonstrate the effective recovery of buried target images, highlighting the potential of the proposed method for practical applications in high-clutter environments. Full article

The traditional probe–pass integration system embeds communication information into a radar waveform, which leads to a high level of waveform autocorrelation sidelobes and a poor false symbol rate at low signal-to-noise ratios. This article proposes a three-dimensional indexed modulation-based design method for probe–pass integration waveforms. This method realises communication information modulation and demodulation by simultaneously indexing orthogonal waveform selection, transmitting pulse PRI changes and carrier frequency changes in three dimensions, and applying compressed perception technology to solve the problems of PRI shortcuts and carrier frequency, resulting in a velocity term in the received waveform that cannot be accumulated by phase reference to realise velocity super-resolution. Finally, the radar detection performance and communication performance are simulated and analysed, and the simulation results reveal that the method proposed in this paper can not only satisfy the radar detection performance requirements but also achieve a lower unsigned rate on the basis of an improved communication rate. Full article

In automotive millimeter-wave (MMW) radar systems, achieving high-precision direction of arrival (DOA) estimation with a limited number of array elements is a crucial research focus. Compressive sensing (CS) techniques have been demonstrated to offer superior performance in DOA estimation compared to spectral estimation methods. However, traditional CS methods suffer from an off-grid effect, which causes their reconstruction results to deviate from the actual positions of the signal sources, thereby reducing the accuracy. Currently, as a gridless method, atomic norm minimization (ANM) has shown effectiveness in DOA estimation for uniform linear arrays (ULAs). However, the performance of ANM is suboptimal in non-uniform linear arrays (NULAs), and their computational efficiency is not satisfactory. In this paper, we propose a novel algorithm for DOA estimation in NULA, drawing inspiration from the alternating descent conditional gradient algorithm framework. First, we construct an atomic set based on the observation scene and select the atoms with the highest correlation to the residuals as potential signal sources for global estimation. Then, we construct a mapping function for the signal sources in the continuous domain and perform conditional gradient descent in the neighborhood of each signal source, addressing the bias introduced by the off-grid effect. We compared the proposed algorithm with ANM, Iterative Shrinkage Thresholding (IST), and Multiple Signal Classification (MUSIC) algorithms. Simulation experiments validate that the proposed algorithm effectively addresses the off-grid effect and is applicable to DOA estimation in coprime and random arrays. Furthermore, real data experiments confirm the effectiveness of the proposed algorithm. Full article

Millimeter-wave automotive radar has become an essential tool for autonomous driving, providing reliable sensing capabilities under various environmental conditions. To reduce hardware size and cost, sparse arrays are widely employed in automotive radar systems. Additionally, because the targets detected by automotive radar typically exhibit sparsity, compressed sensing-based algorithms have been utilized for sparse array reconstruction, achieving superior performance. However, traditional compressed sensing algorithms generally assume that targets are located on a finite set of grid points and perform sparse reconstruction based on predefined grids. When targets are off-grid, significant off-grid errors can occur. To address this issue, we propose an automotive radar sparse reconstruction algorithm based on accelerated Atomic Norm Minimization (ANM). By using the Iterative Vandermonde Decomposition and Shrinkage Threshold (IVDST) algorithm, we can achieve fast ANM, which effectively mitigates off-grid errors while reducing reconstruction complexity. Furthermore, we adopt a Generalized Likelihood Ratio Test (GLRT) detector to eliminate noise and clutter in the automotive radar operating environment. Simulation results show that our proposed algorithm significantly improves reconstruction accuracy compared to the iterative soft threshold (IST) algorithm while maintaining the same computational complexity. The effectiveness of the proposed algorithm in practical applications is further validated through real-world data experiments, demonstrating its superior capability in clutter elimination. Full article

Frequency-modulated continuous-wave (FMCW) millimeter-wave (mmWave) radar systems are increasingly utilized in environmental sensing due to their high range resolution and robust sensing ability in severe weather environments. However, mutual interference among radar systems significantly degrades the target detection capability. Recent advancements in interference mitigation utilizing deep learning (DL) approaches have demonstrated promising results. DL-based approaches typically have high computational costs, which makes them unsuitable for real-time applications with strict latency requirements and limited computing resources. In this paper, we propose an efficient solution for real-time radar interference mitigation. A lightweight transformer, which is smaller and faster than the baseline transformer, is designed to reduce interference. The integration of linear attention mechanisms with depthwise separable convolutions significantly reduces the network’s computational complexity while maintaining a comparable performance. In addition, a two-stage knowledge distillation (KD) process is deployed to compress the network and enhance its efficiency. The staged distillation approach alleviates the training difficulties associated with substantial differences between the teacher and student networks. Both simulated and real-world experiments demonstrate that the proposed method outperforms the state-of-the-art methods while achieving high processing speeds. Full article

It is challenging to estimate the elevation angle of low-altitude targets due to the multipath effect. Various signal processing techniques have been proposed to mitigate these effects, including the use of multi-frequency signals as opposed to single narrowband signals. However, the optimal type of multi-frequency signals and their effective utilization have not been thoroughly explored. Compressive sensing was also proposed as a high-resolution angle estimation method. But, that was conducted with narrowband signals. In this paper, we employ MIMO-OFDM signals along with a block sparse Bayesian learning fast marginalized (BSBL-FM) method. This combination allows for the effective processing of multi-frequency signals and provides high resolution estimates. The MIMO-OFDM approach represents radar signals in a block-sparse matrix form, and the BSBL-FM method leverages this sparsity to achieve high-resolution angle estimates. Simulation results demonstrate that our method can accurately estimate angles at extremely low altitudes where the elevation angle is less than 1 degree. Full article