In ultra-wideband through-the-wall radar (TWR) imaging, conventional back projection (BP) algorithms offer clear physics and accurate wall delay compensation. However, their reliance on global delay traversal leads to high computational cost. The increasing compactness of modern TWR systems and the latency constraints of time-critical missions render existing BP methods inadequate for practical requirements. To reduce the redundant computations of BP in sparse TWR scenes, this paper proposes an energy selective forward projection (ESFP) method as an acceleration method built on the BP imaging model. After preprocessing enhances target sparsity, a closed-form mapping between the equivalent electrical length and the range-azimuth grid is constructed for each transmit-receive pair, which enables direct forward projection of selected echoes. On this basis, an adaptive high-energy range bin selection strategy is developed. It identifies a compact set of informative bins by examining peak structure characteristics and consistency across channels, without requiring an explicit SNR estimate as an input parameter, target number priors, or iterative optimization. Tests on a TWR platform show that ESFP reduces the imaging time by over an order of magnitude compared with representative BP algorithms, while maintaining comparable focusing performance in the sparse TWR scenarios.

Hyperspectral anomaly detection (HAD) aims to identify pixels whose spectral signatures significantly deviate from the surrounding background. Statistical-based detectors usually achieve fast processing but often exhibit limited robustness in complex scenes. In contrast, low-rank sparse representation (LRSR) and deep learning (DL)-based methods provide stronger detection capability but involve iterative optimization or network training, resulting in high computational cost. To balance detection performance and efficiency, this letter proposes a HAD method based on spectral local contrast and three-dimensional (3-D) spectral residual saliency. A multi-scale spectral local contrast measure with ring-shaped background region enhances anomaly responses while suppressing edge structures. A 3-D spectral residual model further highlights anomalous components in the spatial–spectral frequency domain, and a nonlinear fusion strategy integrates the two maps for final detection. Experiments on several benchmark datasets demonstrate competitive performance with low computational cost. The code is available at https://github.com/biangbiangliang/LCRS-HAD.

Rotary-wing targets, as integral components of modern aerial vehicles, play a crucial role in ensuring airborne safety through detection and identification. The estimation of micro-motion parameters of rotary-wing targets is paramount in this regard. In this paper, we address the issue of estimating micro-motion parameters of rotary-wing targets and propose a feature extraction method based on the sinusoidal envelope of scintillation bands. This method, grounded in gradient correction, involves computing the first-order partial derivative information of the mathematical expression of the time-frequency analysis method to eliminate scintillation bands and extract the sinusoidal envelope from the Doppler characteristics of rotary-wing targets. Finally, an improved inverse Radon transform is employed to extract features from the sinusoidal envelope. Simulation results demonstrate the effectiveness of this method in accurately estimating micro-motion parameters and blade count of rotary-wing targets in the absence of prior information.

Accurate imputation of missing data is crucial in the Industrial Internet-of-Things (IIoT), where operations are often compromised by noisy samples from harsh environments. Traditional imputation methods struggle with such noise due to their black-box nature or lack of adaptability. To address this issue, we recast data imputation as a distribution alignment challenge, utilizing the flexibility of optimal transport (OT) to handle noisy samples. Specifically, we first introduce the Proximal Optimal Transport (POT) problem, where the transportation cost is obtained by the network simplex approach with a selective matching mechanism, which renders it capable of matching distributions with noisy samples. Subsequently, we propose the POT-I framework, where the objective is to minimize the transport cost of POT. The produced gradient is used to refine the imputation value, which achieves missing data imputation (MDI) while getting robustness to noisy samples. Experiments on real-world IIoT datasets demonstrate the superiority of POT-I over state-of-the-art imputation methods.

The generation of radar images of noncooperative targets has always been a hot topic and difficulty in this field. Although the existing methods based on generative adversarial networks can solve the problem of insufficient number of radar images of noncooperative targets, they lack precise control over the target’s pose during image generation. In view of this, inspired by the ControlNet model’s ability to incorporate additional conditions for controllable generation in optical image synthesis, this paper develops a method for generating radar images of noncooperative targets with precise pose control based on the ControlNet model. In this paper, the geometric model of noncooperative targets is first constructed, and a limited number of target radar images are obtained through electromagnetic calculation. Secondly, a frequency domain denoising method is proposed to filter out the stripe noise in the radar images; meanwhile, Lee filter is applied to remove speckle noise from the radar images, thus building a database of radar images for noncooperative targets. Finally, the edge map of the optical images under different poses are used as control conditions to realize the generation of radar images of noncooperative targets with precise and controllable poses based on the ControlNet model. Simulation results demonstrate that this method can generate radar images of noncooperative targets with accurately controlled azimuth and elevation angles. Especially when the number of training samples is limited, the generated results outperform existing algorithms in terms of structural similarity, peak signal-to-noise ratio, and Fréchet inception distance.

To address the challenge of effectively detecting weak vital signs in through-wall radar (TWR) applications under low signal-to-noise ratio (SNR) conditions, this paper proposes a detection algorithm based on joint space-time-frequency enhancement. Radar echoes first undergo pulse compression and preprocessing. In the space-time domain, adaptive noise filtering is performed on cross-channel signals based on Maximum Covariance Analysis (MCA), followed by a correlation-weighted accumulation to realize joint space-time enhancement. Subsequently, coherent integration is performed via Fast Fourier Transform (FFT) to transform the signal into the frequency domain. On this basis, the Harmonic Product Spectrum (HPS) technique is then employed to perform multi-scale spectral fusion on the Range-Doppler (RD) matrix, effectively enhancing periodic respiration signals while significantly suppressing random noise. Next, exploiting the spatial distribution, the enhanced multi-channel spectra are fused to construct a cross-channel correlation matrix, generating a vital sign feature map that benefits from the joint space-time-frequency enhancement. Following this, by combining Constant False Alarm Rate (CFAR) detection with an energy criterion, the algorithm achieves automatic target identification and range pairing. Finally, the two-dimensional (2D) spatial coordinates of the targets are resolved using the elliptical intersection method. Experimental results demonstrate that by fully exploiting multidimensional space-time-frequency features, the proposed method effectively overcomes the difficulty of detecting and localizing weak vital signs under low SNR conditions. The method significantly enhances the correlation of target echoes, and its detection accuracy under low SNR outperforms that of the referenced methods, enabling robust detection.

To address the shortcomings of existing vital signs detection radar systems, such as complex hardware design and high cost, and considering the need for versatility and flexibility, this paper designs and implements a continuous wave (CW) vital signs detection radar RF front-end based on AD9361. The AD9361 RF transceiver features high integration and strong flexibility. Key parameters including local oscillator frequency, signal bandwidth, and sampling rate can be adjusted through software programming, which effectively simplifies the hardware design of vital signs detection radar. First, this paper elaborates on the principle of vital signs detection. Second, it details the overall design scheme, including the system’s overall architecture and main software modules. Finally, the effectiveness and reliability of the system are verified through local oscillator testing experiments, loop-back experiments, and actual vital sign measurement analysis. The design scheme proposed in this paper can provide technical references for the development of CW vital signs detection radar towards flexibility, miniaturization, and low cost, and has certain engineering application value.

Infrared (IR) small target detection remains challenging in complex scenes due to high false alarm rates caused by strong edges, corner structures, and pixel-level non-homogeneous brightness (PNHB). To address these issues, this paper proposes a weighted improved ring local contrast measure (WIRLCM) for robust infrared small target detection. The proposed method consists of IRLCM and a structure-aware weighting mechanism. In the local contrast computation stage, an enhanced ring-shaped window incorporating an isolation belt is introduced to alleviate scale sensitivity and reduce background estimation bias induced by the direct adjacency between target and background regions. Based on this window design, an improved ring-based contrast formulation is developed to achieve more stable background estimation. The proposed formulation replaces the conventional single-pixel-width ring modeling strategy and eliminates redundant selection-and-comparison operations, thereby enhancing small targets while suppressing strong bright structures with lower computational complexity. In the structure-aware weighting mechanism, two complementary strategies are designed to jointly model target saliency and structural anisotropy. A multi-directional gradient-based enhancement factor exploits the distinct directional gradient responses of different local structures, reinforcing target responses while suppressing weak edge interference. Meanwhile, a corner suppression factor inspired by structure tensor analysis and multi-structuring-element top-hat filtering is integrated to further reduce false alarms caused by strong edge and corner clutter. Finally, the detection map is generated through the pixel-wise Hadamard product of the IRLCM and the weighting factor. Extensive experiments on multiple IR datasets demonstrate that WIRLCM achieves stable detection performance and improved clutter suppression across various complex background scenarios. The codes are available at https://github.com/biangbiangliang/WIRLCM.

Infrared (IR) small target detection faces two major challenges: single-frame-based methods are highly sensitive to random noise, while multiframe-based approaches often neglect the dynamic nature of background scenes, resulting in high false alarm rates. To address these issues, this letter proposes a novel ring local contrast measure (LCM) weighted by spatial-temporal prior (WSTRLCM). WSTRLCM comprises two key modules: a spatial-temporally enhanced ring local contrast and a spatial-temporal weighting factor. Specifically, a spatial-temporal enhancement scheme is first applied to the original image sequence to boost target saliency, and the ring contrast computation is optimized for improved efficiency. For the weighting factor, a small target enhancement strategy based on image block gradients is introduced. By incorporating structure tensor analysis, effective back-ground suppression is achieved, resulting in a refined spatial-temporal enhancement sequence. Furthermore, a tailored multiframe difference approach, designed according to target motion characteristics, is employed to generate the final weighting factor. The Hadamard product of the local contrast and the weighting factor is then computed, and adaptive thresholding is applied to obtain the final detection map. Experimental results on public datasets demonstrate that WSTRLCM achieves superior performance compared to several state-of-the-art methods, exhibiting enhanced target detection capability and strong background suppression. The code is available at https://github.com/biangbiangliang/WSTRLCM

Color polarization demosaicking (CPDM) aims to reconstruct full-resolution polarization images of four directions from the color-polarization filter array (CPFA) raw image. Due to the challenge of predicting numerous missing pixels and the scarcity of high-quality training data, existing network-based methods, despite effectively recovering scene intensity information, still exhibit significant errors in reconstructing polarization characteristics (degree of polarization, DOP, and angle of polarization, AOP). To address this problem, we introduce the image diffusion prior from text-to-image (T2I) models to overcome the performance bottleneck of network-based methods, with the additional diffusion prior compensating for limited representational capacity caused by restricted data distribution. To effectively leverage the diffusion prior, we explicitly model the polarization uncertainty during reconstruction and use uncertainty to guide the diffusion model in recovering high error regions. Extensive experiments demonstrate that the proposed method accurately recovers scene polarization characteristics with both high fidelity and strong visual perception.

Polarimetric imaging captures surface polarization characteristics, such as the Degree of Linear Polarization (DoLP) and the Angle of Polarization (AoP). In mainstream Division of-Focal-Plane (DoFP) color polarization imaging, recovering polarization parameters from captured mosaic arrays remains a challenging inverse problem. Existing DoFP cameras also face hardware bottlenecks and often cannot support high-frame-rate acquisition, limiting polarimetric imaging in dynamic video tasks. These limitations motivate joint spatial and temporal enhancement. To this end, we propose the first space-time polarization video reconstruction architecture. The method jointly models polarization directions in space and time and uses a polarization-aware implicit neural representation for continuous, high-fidelity upsampling. By analyzing temporal variations in polarization parameters, we further introduce a flow-guided polarization variation loss to supervise polarization dynamics. We also establish the first large-scale color DoFP polarization video benchmark to support this research direction. Extensive experiments on this benchmark demonstrate the effectiveness of the method.

Infrared small targets detection (IRSTD) is easily affected by structural edges and textured backgrounds, which degrades performance. Existing deep learning (DL-) and low-rank sparse representation (LRSR-) based methods suffer from high computational complexity, while local contrast measure (LCM-) based methods are limited in structure suppression and feature fusion. To address these issues, this paper proposes a feature-guided discriminative ring local contrast measure (FGDRLCM) for IRSTD. Specifically, an information-discriminative ring local contrast measure (ID-RLCM) is first designed to enhance small targets while suppressing structural interference. Then, a multi-directional group structure tensor model (MDG-STM) is introduced to improve the separability between targets and structural components, providing effective structure suppression cues. Finally, guided filtering is employed for adaptive feature fusion, followed by a global-statistics-based adaptive thresholding strategy to obtain detection results. Experimental results demonstrate that the proposed method achieves effective detection performance under complex backgrounds with low computational complexity, showing strong potential for practical applications. The code is available at https://github.com/biangbiangliang/FGDRLCM.

The complexity of backgrounds, such as roads and cloud edges, is an important factor affecting the accuracy of small target detection. In response to the high false alarm rate caused by such complex backgrounds with strong edge noise, this letter proposed an infrared small target detection method based on ring local contrast measure with edge suppression (ESRLCM). First, a ring window is designed based on the distribution characteristics of small targets, and the ring local contrast map is calculated based on the grayscale changes within the window. Then, the Gabor filters and improved image gradient are used to weight the obtained ring local contrast map, eliminating the influence of edge noise. Finally, a small target is detected through simple threshold processing. Testing on public datasets proves that, at the same detection rate, the algorithm proposed in this letter has a lower false alarm rate compared to similar state-of-the-art algorithms.

For snapshot polarization imaging, the color polarization demosaicking is essential to reconstruct full resolution from a mosaic array, which is the latest unsolved issue. Due to the mosaic array missing a large number of key pixels, existing one-step deep learning-based methods exhibit limited demosaicking performance. Hence, we make the first attempt to address the color polarization demosaicking task through the diffusion model, namely DCPM. Specifically, we extend the residual-based diffusion process to the task of color polarization demosaicking and improve the network architecture to accommodate full-resolution polarization images. Moreover, considering the polarization property of images, a customized loss function is proposed to assist in the diffusion model training. Extensive experiments on both synthetic and real-world benchmarks demonstrate the effectiveness of the proposed method. The source code will be available at https://github.com/JJGNB/ DCPM.

Infrared and visible image fusion (IVF) enables comprehensive representation of low-light scenes. Current methods are prone to yield visually poor results in extremely dark conditions because they tend to focus solely the fusion process without considering the degradation of source images. To solve this problem, a novel infrared and visible image fusion method is proposed, which incorporates low-light image enhancement (LLIE) within unified framework to achieve visually compelling fusion results even under severe environments, namely VCIF. The network first acquires proficient capabilities in illumination correction and chromatic transformation over LLIE tasks. Then, the LLIE module of VCIF is refined for varying brightness conditions through image enhancement and denoising. Finally, the fusion step is realized through elaborate fusion rules and a simple encoder decoder structure based on Transformer blocks. Moreover, a maximum selection loss that integrates intensity and gradient constrained on different color spaces is designed to boost the fusion performance. Experimental results exhibit that the proposed method outperforms the state-of-the-art methods by generating human-aligned visual results. The source code will be available at https://github.com/JJGNB/VCIF.

Ultra-wideband MIMO through-wall radar has been widely used in disaster rescue, anti-terrorism investigation, and intelligent security through multi-antenna cooperative transmission and reception, which not only significantly improves the radial and azimuth resolution, but also has strong medium penetration ability. However, in practical applications, existing systems and mainstream detection algorithms rely too much on the prior modeling of walls and noise parameters, which can easily produce false alarms and missed detections in unknown walls or dynamic scenes. In order to solve above challenges, this paper proposes a joint algorithm based on local contrast detection and adaptive region-growing feature discrimination, which first enhances the local contrast of the PCF-weighted BP radar image, and then introduces the region-growing algorithm to segment target area, and eliminates false alarms according to the geometric features of region. Simulation and field experiments show that the proposed algorithm has better comprehensive performance.

In through-the-wall detection scenarios with low signal-to-noise ratio (SNR) and strong clutter, existing target detection methods generally suffer from inaccuracies, poor real-time performance, and the limitation of detecting only moving or stationary targets. To address these challenges, this paper proposes a through-the-wall radar (TWR) target detection method based on cross-correlation adaptive robust principal component analysis (CCARPCA) capable of simultaneously detecting multiple moving and stationary targets. First, pulse compression is applied to original echo signals using the inverse fast Fourier transform, resulting in high-resolution one-dimensional range profiles. Second, the principal component analysis algorithm suppresses strong clutter interferences, thereby improving the SNR. Next, the back projection algorithm is employed for multi-channel coherent imaging, enabling the extraction of 2-dimensional information and enhancing the sparsity of cross-correlation data. Lastly, considering the drawbacks of the robust principal component analysis (RPCA), such as long detection time and poor robustness, this paper introduces the cross-correlation coefficient and proposes the CCARPCA algorithm, which completely separates the target from the background noise. The experimental results based on a series of simulated and measured data demonstrate the effectiveness of the proposed method in detecting both moving and stationary targets behind walls. Compared to generalized likelihood ratio test, constant false alarm rate, and RPCA, our method achieves a substantial improvement of over 16.4% in detection accuracy based on measured data while maintaining real-time detection capability. Additionally, its detection performance is less sensitive to changes in initial parameters, indicating its superior robustness.

Enhancing the performance of single-carrier underwater acoustic (UWA) communication systems necessitates the development of accurate channel estimation techniques within the receiver. Among various approaches, the recursive least squares (RLS)-based channel estimation method has been extensively employed due to its rapid convergence and robust tracking capabilities. Over time, numerous enhancements have been incorporated into the RLS framework to further improve channel estimation performance for UWA systems. In this article, a novel bidirectional joint iterative ${l}_{{1}}$ -RLS (Bi-Ji- ${l}_{{1}}$ -RLS) algorithm is proposed to optimize channel estimation for single-carrier UWA communication systems. The proposed algorithm exploits bidirectional diversity and the inherent sparsity of UWA channels to achieve performance improvements. A comprehensive transient analysis is developed, jointly considering the bidirectional estimation mechanism and the ${l}_{{1}}$ -norm constraint, which elucidates the operational behaviors and confirms the advantages of the Bi-Ji- ${l}_{{1}}$ -RLS algorithm over the conventional ${l}_{{1}}$ -RLS approaches. Simulation results further validate the theoretical analysis, demonstrating enhanced estimation accuracy. Moreover, experimental results obtained from undersea trials substantiate the practical effectiveness of the proposed algorithm, highlighting its moderate superiority over existing techniques in real-world UWA communication scenarios. Specifically, the proposed algorithm achieves the minimum bit error rate (BER) of ${0}.{0832}\%$ and the maximum output signal-to-noise ratio (OSNR) of ${12}.{6024}$ dB.

The high-precision estimation of multi-dimensional parameters for spatial targets based on high-resolution range profiles is crucial for target recognition. However, existing estimation methods face difficulties in resolving the strong coupling between the target shape and the micro-motion parameters, as well as in fully utilizing micro-motion information under complex modulation characteristics. To address these challenges, this paper proposes a multi-dimensional parameter-estimation method for spatial targets based on micro-range decomposition. A micro-range model of the target is first constructed, and the micro-range modulation characteristics are analyzed. Then, micro-range coefficients are selected based on their Cramér–Rao lower bound (CRLB), and the correlation between these coefficients and target parameters is exploited for scattering center matching. An optimization model is further built for multi-dimensional parameter estimation, enabling the accurate estimation of parameters such as precession frequency, precession angle, and structural dimensions under both single-view and multi-view conditions. The experimental results show that in the dual-view case, all parameters are estimated with relative errors (REs) below 1.15% and root mean square error (RMSE) values below 0.05. In the single-view case, key parameters are estimated with REs under 15%. Compared with conventional methods, the proposed method achieves lower RMSE and significantly improved robustness and stability. These results demonstrate the effectiveness and practical potential of the proposed method for spatial target parameter estimation.

As a novel configuration of along-track multistatic synthetic aperture radar (Multi-SAR), the high frame rate along-track swarm SAR (ATS-SAR) has garnered significant attention in recent years due to its exceptional efficiency in reducing data acquisition time. Motivated by its potential for high-resolution imaging of moving targets, this article investigates the application of ATS-SAR in moving target imaging. However, high frame rate ATS-SAR-based moving target imaging confronts two critical challenges: time-space coupling and partial data loss in moving target echoes. To address these challenges, we first conduct a comprehensive analysis and theoretical derivation of the moving target echo model under the high frame rate ATS-SAR configuration. Subsequently, we propose an innovative motion parameter estimation algorithm that exploits unique echo characteristics to achieve high-performance imaging. Furthermore, we introduce the high-resolution, high frame rate ATS-SAR subaperture collaborative imaging algorithm for moving targets (MT-SACIm-ATS). Extensive simulations and a real measured experiment validate the effectiveness of the MT-SACIm-ATS algorithm, demonstrating imaging performance that closely approximates reference imaging results. Comparative analysis with several state-of-the-art algorithms further highlights the superiority of the proposed approach in terms of resolution and robustness.