Detecting power lines accurately remains a challenging task for low-altitude aircraft due to their low radiation and complex background, making them one of the most perilous impediments for such aircraft. To overcome this issue, in this letter, a knowledge-guided rotated network (KRNet) is proposed for power line detection in complex infrared scenes. The proposed method consists of the following main three steps. First, we develop a linear-distribution perception module (LDPM), which enhances feature representation by extracting and exploiting important linear and spatial distributional prior knowledge. Second, to more accurately locate the power line, we design a novel loss function rotation-aware complete intersection-over-union (RACIoU), which utilizes the prior knowledge of power line non-directionality to guide model learning. Finally, using this prior knowledge of the power line, we obtain good detection results and verify the effectiveness and advantages of the proposed method using the power line rotation detection dataset (PRDD) that we built. Our method outperforms other state-of-the-art benchmarks for power line detection.

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.

The typical issues are that the existing methods of moving target detection in spaceborne distributed aperture radar (SBDAR) suffer from the range cell migration (RCM) and Doppler frequency modulation (DFM) problems in space-time two-dimensional (2D) domain. To deal with these issues, a new SBDAR moving target detection method based on space–time hybrid integration (STHI) is developed in this paper. Firstly, the RCM and DFM in time dimension are removed by the second-order Keystone transform (SKT), a novel range frequency reversal process (NRFRP) and a modified scaled Fourier transform (MSCFT), to achieve the time dimensional coherent integration. Secondly, the spatial projection method is utilized to achieve the space dimensional integration of moving target by gridding the radar detection area, and the moving target is finely focused and detected. Finally, the effectiveness of the proposed method is verified by simulations.

Although space-time adaptive processing (STAP) is recognized as the optimal clutter suppression way for synthetic aperture radar (SAR) in theory, the deficient of independent and identically distributed range samples in real scenario limits its application. The reduce-dimension STAP methods can decrease the demand for range samples, but the assumption of moving target-free is always unsatisfied. The direct data domain methods only use the data of the range cell under test (RCUT) to avoid the assumption, but they are conducive to interference suppression than clutter suppression and have huge computational burden. Thus, in this letter, a single range data-based STAP method is proposed not only exploring the space-time statistical properties of clutter to suppress it, but also operating solely on the RCUT without recourse to range samples. Theoretical analyses and simulation results verify the effectiveness of the proposed method.

Inverse synthetic aperture radar (ISAR) imaging for maneuvering targets has always been a challenging task due to azimuth time-varying Doppler frequency modulation, especially under moving platform condition. In this case, the common assumption that the image projection plane (IPP) of the radar line-of-sight (LOS) direction is constant during coherent processing interval (CPI) is invalid. To address this issue, a novel ISAR imaging approach for maneuvering targets is proposed by exploiting nonstationary IPP in this article. First, considering time-varying LOS direction, the new geometric and signal models are developed, where 2-D spatial-variant phase error is mainly deduced. After that, a parametric image entropy minimum optimization combined with efficient particle swarm optimization (PSO) is used to obtain optimal motion parameters. In doing so, 2-D spatial-variant phase error terms are compensated accurately to produce well-focused ISAR image. Finally, the effectiveness and superiority of the proposed algorithm are verified by the simulation results and electromagnetic scattering data.

Unknown motions will make the synthetic aperture radar (SAR) images of ground moving targets defocused. The target signal easily exhibits Doppler ambiguity due to the limitation of pulse repetition frequency, which leads to the focusing difficulty of moving targets. To address these issues, a fast approach for SAR imaging of ground moving target with Doppler ambiguity is proposed. In this method, the first-order and quadratic phase are initially estimated by using proposed operations based on 2-D scaled Fourier transform and improved range frequency cross correlation function, respectively. With the estimated parameters, the moving target is then focused in the range–azimuth time domain by the matched filtering. The presented approach is fast, because its realization procedure does not have any parameter-searching step and can be sped up by nonuniform fast Fourier transform. Moreover, the proposed approach can handle Doppler ambiguity (including Doppler center blur and spectrum ambiguity), blind speed sidelobe, and scaled frequency spectrum aliasing. Both spaceborne and airborne real data-processing results are presented to confirm the effectiveness of the proposed method.

The front-wall clutter removal in the case of the through-the-wall radar (TWR) system is studied in this work. To remove the wall clutter, its low-rank property is utilized, and at the same time, the sparse property of the target returns is exploited to perform target reconstruction. To account for the unparalleled setting of the antenna and the wall, a weighted nuclear norm minimization (WNNM) is employed, and the resulting problem is solved in an alternating manner. In addition, different transmitted waveform signals, including monofrequency and stepped-frequency waveforms, are used to demonstrate their effects on the clutter suppression performances. The experimental results show that the proposed WNNM with stepped-frequency waveform outperforms other approaches.

Since only the spatial feature information of ship target is utilized, the current deep learning-based synthetic aperture radar (SAR) ship detection approaches cannot achieve a satisfactory performance, especially in the case of multiscale or rotations, and the complex background. To overcome these issues, a novel multidimensional domain deep learning network for SAR ship detection is developed in this work to exploit the spatial and frequency-domain complementary features. The proposed method consists of the following main three steps. First, to learn hierarchical spatial features, the feature pyramid network (FPN) is adopted to produce ship target spatial multiscale characteristics with a top-down structure. Second, with a polar Fourier transform, the rotation-invariant features of SAR ship targets are obtained in the frequency domain. After that, a novel spatial-frequency characteristics fusion network is then presented, which seeks to learn more compact feature representations across different domains by updating the parameters of sub-networks interactively. The detection results are obtained due to utilizing the multidimensional domain information, and we evaluate the effectiveness of the proposed method using the existing SAR ship detection data set (SSDD). The results of the proposed method outperform other convolutional neural network (CNN)-based algorithms, especially for multiscale and rotation ship targets under complex backgrounds.

Owing to the highly maneuvering character involved in targets, the nonuniform 3-D rotation motions make the assumption that the image projection plane (IPP) is constant during coherent processing interval (CPI) invalid. In this work, an efficient approach is proposed in ISAR imaging for highly maneuvering targets with nonstationary IPP. First, to reasonably describe the mobility of a highly maneuvering motion target, the geometry and signal model with nonstationary IPP are established, where the high-order phase model is deduced to describe the 2-D spatial-variant phase errors. Second, based on the developed signal model, considering the cost function obtained via conventional image entropy with local extremum, the subarray averaging operation in conjunction with entropy is utilized to accelerate the global optimal convergence. Finally, the accurate 2-D spatial-variant phase errors compensation terms are generated to produce the well-focused ISAR images. Compared with existing methods, the main advantages of this work are: 1) the geometry and signal model of the target with nonstationary IPP are established; 2) the subarray averaging operation in conjunction with image entropy is utilized to accelerate the global optimal convergence; and 3) the high-order signal model is derived to present the 2-D spatial-variant phase errors. Several numerical experiments using simulated data and electromagnetic data are conducted to demonstrate the validity of the proposed algorithm and signal model.

Partially observable Markov decision process (POMDP) is an extension to MDP. It handles the state uncertainty by specifying the probability of getting a particular observation given the current state. DESPOT is one of the most popular scalable online planning algorithms for POMDPs, which manages to significantly reduce the size of the decision tree while deriving a near-optimal policy by considering only $K$ scenarios. Nevertheless, there is a gap in action selection criteria between planning and execution in DESPOT. During the planning stage, it keeps choosing the action with the highest upper bound, whereas when the planning ends, the action with the highest lower bound is chosen for execution. Here, we propose LB-DESPOT to alleviate this issue, which utilizes the lower bound in selecting an action branch to expand. Empirically, our method has attained better performance than DESPOT and POMCP, which is another state-of-the-art, on several challenging POMDP benchmark tasks.

The emerging technology of deep neural networks has been proven to be successful for hyperspectral image analysis. However, it is still a great challenge to apply the deep learning method for quantitatively retrieving mineralogical composition, because typical deep neural networks generally require thousands of labeled samples for training, while only a few mineral samples can be acquired and examined for quantitative examination in practice. To address this challenge, this letter proposes a training data augmentation approach which incorporates the prior-knowledge of hyperspectral reflectance characteristics using the classic Hapke equations. Experiments over both laboratory and airborne hyperspectral remote sensing data show that the proposed method outperforms the widely used approaches for quantitative mineral analysis.

This letter presents a conceptually simple and effective dual-granularity triplet loss for visible-thermal person re-identification (VT-ReID). Generally, ReID models are always trained with the sample-based triplet loss and identification loss from the fine granularity level. Further, center-based loss could be introduced to encourage the intra-class compactness and inter-class discrimination from the coarse granularity level. Our proposed dual-granularity triplet loss well organizes the sample-based triplet loss and center-based triplet loss in a hierarchical fine to coarse granularity manner, just with some simple configurations of typical operations, such as pooling and batch normalization. Experiments on RegDB and SYSU-MM01 datasets show that with only the global features our dual-granularity triplet loss can improve the VT-ReID performance by a significant margin. It can be a strong VT-ReID baseline to boost future research with high quality.

This paper mainly focuses on synthetic aperture radar (SAR) ground moving target imaging. Although there exists many excellent SAR moving target imaging algorithms, two issues, the maneuverability of the SAR platform and the type of data used for moving target imaging, are not discussed by most of them. Thus, a novel SAR image domain-ground moving target imaging method is proposed to preliminarily handle the aforementioned two issues. The method proposed contains two main steps. The first one is the pre-imaging of the raw data, and the second one is focusing the ground moving target's image data by a proposed one-dimensional parameter traversal approach. Numerical experiments are finally presented to verify the effectiveness of the proposed ground moving target imaging method.

The synthetic aperture radar (SAR) image of ground moving targets will be typical smeared given the range migration (RM) and Doppler frequency migration (DFM). To deal with these issues, a new SAR ground moving target imaging method based on modified second-order keystone transform (MSOKT) and keystone transform (KT) is developed in this paper. Firstly, the time reversing process is utilized to separate the second-order phase. Secondly, the range curvature migration and DFM are removed by MSOKT, and then the second-order phase is estimated. Finally, the moving target is finely focused after eliminating residual RWM by KT. The main contributions of this paper are listed as follows: 1) the proposed method can effectively focus moving targets without residual errors; 2) the effects of Doppler ambiguity and blind speed sidelobe are further handled. The effectiveness of the proposed method is confirmed by the simulation and real data-processing results.

Owing to the large range cell migration (RCM) and fast time-variant Doppler frequency modulation (DFM) generated by rapidly spinning targets, it is difficult to efficiently obtain well-focused inverse synthetic aperture radar (ISAR) images via conventional algorithms because of the multidimensional search requirement. Inspired by the inherent azimuth spatial invariance in strip-map synthetic aperture radar (SAR) imaging mode, an efficient range-Doppler domain ISAR imaging method for rapidly spinning targets is proposed in this article. First, echo signal is transformed into range-Doppler domain and its precise analytical expression is derived according to the principle of stationary phase (POSP). Second, the energy of scatterers distributed in different range cells is extracted along the rotating radius. By doing so, the energy is concentrated in the same range cell. After that, the high-order phase terms of the signal are compensated and the CLEAN technique is also applied to reduce the sidelobes of a strong scatterer. Finally, 3-D ISAR image of the spinning target is reconstructed by projecting the spatial parameters to 3-D cylindrical coordinates. Furthermore, in this article, the output signal-to-noise ratio (SNR) gain, anti-noise performance, the mismatched phase error, and the computational complexity analyses are also provided. Compared with existing approaches, the proposed method has advantages in the computational complexity and low SNR environment thanks to the only 1-D search and the coherent integration gain obtained. Both the theoretical derivations and the simulated results demonstrate the effectiveness of the proposed method.

The recovery problem of synthetic aperture radar (SAR) signal in the presence of radio frequency interference (RFI) is studied. To perform RFI suppression, in this paper, the RFI is modeled as the combination of multiple complex sinusoids such that the RFI suppression problem becomes a frequency estimation one. To accurately estimate model parameters, by exploiting sparse representation of the RFI, a gridless approach based on atomic norm minimization is proposed, which completely removes the off-grid issue. Finally, to recover the SAR signal, a joint scheme is devised to simultaneously perform the RFI suppression and the SAR signal recovery under an optimization framework. The resultant optimization is efficiently solved by a two-step process based on the coordinate descent approach. Simulation results and real-world experiments are provided to show the superior performance of the proposed approach.

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.

In this letter, we propose a synthetic aperture radar (SAR) target configuration recognition algorithm based on the fusion of multifeature low-rank representations (LRRs). First, Gabor, principal component analysis, and wavelet features are extracted for the SAR training set and test set, respectively. Second, with the LRR model, each feature of the test samples is represented by those of the training set, leading to the corresponding coefficient matrix. Then, the preliminary prediction labels of all features of the test sample are obtained according to the LRR coefficients. Third, in order to further improve the confidence of recognition and reduce the instability of the algorithm, a two-stage decision fusion strategy is adopted to obtain the final prediction labels. The first stage utilizes a vote fusion for the recognition results of multiaspect neighborhood test samples for each feature pattern, which exploits the strong correlation of these neighborhood samples. Furthermore, the second stage fuses the three results obtained in the first stage through Bayesian inference. Bayesian inference is widely used in decision fusion, which can improve the confidence of results by about 3%. Experiments on the moving and stationary target acquisition and recognition data set demonstrate the effectiveness and superiority of the proposed algorithm.

This paper addresses the problem of recovering a synthetic aperture radar (SAR) signal that is corrupted by both radio frequency interference (RFI) and wideband interference (WBI). The time–frequency domain is utilized for both the SAR signal and interference in the form of sparse representations. By doing so, a unified framework that allows one to suppress both the RFI and WBI while recovering the SAR signal can be developed. The resulting framework is an optimization problem that is efficiently solved using a customized alternating direction method of multipliers approach. Finally, simulation results are provided to demonstrate that the performance of the joint estimation algorithm is superior to the performances of other methods in terms of both subjective and objective evaluation standards.

The problem of synthetic aperture radar image recovery in the presence of wideband interference (WBI) is investigated. Delayed versions of a transmitted signal are utilized to construct a dictionary in which a signal of interest (SOI) has a sparse representation. In this letter, WBI is sparsely represented by the time-frequency domain. By utilizing the transform domains, a joint estimation approach is devised to simultaneously perform WBI suppression and SOI recovery within an optimization framework. Based on the separability property in the optimization, an alternating direction method of multipliers-based approach is developed to efficiently obtain a solution. Finally, simulation results are presented to demonstrate the superior performance of the joint estimation algorithm.

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.

This letter addresses the problem of synthetic aperture radar (SAR) image recovery in the presence of radio frequency interference (RFI), which degrades SAR image quality if it is not effectively suppressed. In this letter, the RFI is modeled as the superposition of multiple complex sinusoids such that the RFI suppression problem is transformed to a frequency estimation problem. To accurately estimate the amplitudes of the sinusoids and their corresponding frequencies in the case of a low number of range samples, the frequency sparsity in the frequency domain is successfully exploited. From the estimated amplitudes and frequencies, the RFI can be reconstructed and then used for suppression. To recover the signal of interest (SOI) and by utilizing the estimated RFI, a joint estimation is derived to simultaneously perform the RFI suppression and the SOI recovery. This joint approach can effectively suppress the RFI even if it overlaps with the SOI in both the time and frequency domains. The common threshold decision approach is not required for our joint estimation to reduce the RFI. Simulation results and real-world experiments are presented to demonstrate the superior performance of the joint estimation algorithm.

With an appropriate geometric configuration, a helicopter-borne rotating synthetic aperture radar (ROSAR) can break through the limitations of conventional strip-map monostatic SAR on forward-looking imaging. Owing to such a capability, ROSAR has extensive potential applications, such as self-navigation and self-landing. Moreover, it has many advantages if combined with frequency-modulated continuous wave (FMCW) technology. In this letter, a novel geometric platform configuration and an imaging algorithm for helicopter-borne FMCW-ROSAR are proposed. First, by adopting the higher order approximation of slant range model to improve the azimuth resolution for FMCW-ROSAR, the precise 2-D spectrum of the echo signal is derived based on series reversion. Moreover, at the same time, the Doppler offset caused by the continuous motion of the antenna is analyzed and compensated as well. Then, according to the analysis on the range-dependent velocity variation caused by ROSAR geometric configuration, an efficient inverse chirp-Z transform is utilized to remove the variant range cell migration, and a well-focused SAR image can thus be obtained. Finally, the experimental results with simulated data demonstrate the effectiveness of the proposed algorithm.

Accurate focusing of highly squinted azimuth-variant bistatic synthetic aperture radar data is a difficult issue due to the relatively large range migration, sensibility of the higher order terms, and the inherent geometric variance. To accommodate for this problem, extended azimuth nonlinear chirp scaling algorithm is investigated in this letter. First, range-azimuth coupling is mitigated through a linear range walk correction operation, and then, bulk secondary range compression is implemented to compensate the residual range cell migration and cross-coupling terms. Following which, the characteristics of the azimuth-dependent quadratic and cubic phase terms are analyzed, and modified scaling coefficients are derived by adopting higher order approximation and incorporating the azimuth-dependent range offset caused by the inherent geometric configuration. Compared with traditional nonlinear chirp scaling method, large azimuth depth of focusing can be realized without changing the overall procedure. Simulation results validate the effectiveness of the proposed algorithm.

High-resolution imaging for squint azimuth-variant bistatic synthetic aperture radar system is a challenging task due to the existence of the spatial variance of range cell migration (RCM) and Doppler frequency modulation (FM) rate. To address this problem, azimuth nonlinear chirp scaling (ANLCS) is investigated in this letter. First, linear range walk is removed and then ANLCS is applied in the range frequency azimuth time domain to correct the azimuth-variant RCMs and to equalize the different FM rates. Taking the 2-D variance caused by the azimuth-variant configuration into consideration, a new perturbation function is derived based on the bistatic geometry. Using method of series reversion, a close form of range-azimuth coupling is obtained and corrected in the range Doppler domain by an interpolation-free operation. Incorporated with the secondary range compression, this method leads to a more accurate focusing for azimuth-variant bistatic configurations, even with high squints. Simulation results validate the effectiveness of the method.