Wide-field high-resolution cameras are advanced imaging devices with a wide field of view and high resolution. However, in practical applications, they encounter several challenges, such as small object scale, object occlusion, and complex background interference. To address these issues and enable effective detection of distant small ground targets by Wide-field high-resolution cameras, this paper proposes the YOLO-Wide method based on YOLOv11n. First, the Dysample module is introduced to replace the traditional upsampling module in YOLOv11n, enhancing the detection capability of small objects. Secondly, the Deformable Dual Attention Module (DDAM) is proposed, enabling the model to better focus on the target region, effectively suppressing the interference of complex backgrounds on detection results. Third, a residual connection module based on ODConv (ODConv-ResBlock) is introduced to improve the model’s feature extraction capability and fully explore the semantic information in the image. Fourth, the loss function is optimized by using FocalerCIoU, improving the model’s accuracy in bounding box localization. Finally, the position of the network detection head is adjusted to ensure that small objects at different scales are processed on the most suitable feature maps. Experimental results show that the YOLO-Wide achieves an [email protected] of 89.4% on the custom dataset, Small-Ground, which is a 9.1% improvement over the baseline YOLOv11n.

Dual-functional radar-communication (DFRC) systems have gained attention for their advantages in spectrum sharing and hardware integration. Frequency-hopping (FH) MIMO radar waveforms enable communication via index modulation but introduce challenges such as increased range sidelobes, suboptimal communication performance, and incompatibility with conventional MIMO detection. Based on these considerations, we propose a new DFRC waveform design that combines frequency-hopping code selection (FHCS), binary frequency-shift keying (BFSK), and permutation selection (PS), termed FHCS-BFSK-PS. This design utilizes FH code vectors, frequency codes, and carrier-to-antenna mapping matrices as modulation parameters, embedding communication information across three dimensions: FH code index, frequency, and spatial phase. For communications, leveraging the weak coupling among these parameters, a simple and efficient demodulation algorithm based on the matched filtering criterion is proposed. For radar detection, we analyze the impact of fast-time domain FH on conventional MIMO detection and propose a modified detection method. This approach calculates and sums the log-likelihood ratios across multiple subpulses, thereby avoiding the incoherent accumulation of steering vectors in the subpulse domain. Finally, simulation results demonstrate the feasibility of the proposed scheme in target detection and highlight its superior performance in terms of symbol error rate (SER), data rate, and ambiguity function characteristics.

Self-supervised monocular depth estimation has been widely studied for 3D perception, as it can infer depth, pose, and object motion from monocular videos. However, existing single-view and multi-view methods employ separate networks to learn specific representations for these different tasks. This not only results in a cumbersome model architecture but also limits the representation capacity. In this paper, we revisit previous methods and have the following insights: (1) these three tasks are reciprocal and all depend on matching information; and (2) different representations carry complementary information. Based on these insights, we propose Uni-DPM, a compact self-supervised framework to complete these three tasks with a shared representation. Specifically, we introduce an U-net-like model to synchronously complete multiple tasks by leveraging their common dependence on matching information, and iteratively refine the predictions by utilizing the reciprocity among tasks. Furthermore, we design a shared Appearance- Matching-Temporal (AMT) representation for these three tasks by exploiting the complementarity among different types of information. In addition, our Uni-DPM is scalable to downstream tasks, including scene flow, optical flow, and motion segmentation. Comparative experiments demonstrate the competitiveness of our Uni-DPM on these tasks, while ablation experiments also verify our insights.

This letter investigates source localization using time difference of arrival (TDOA) and frequency difference of arrival (FDOA). The improper distribution of the sensors can lead to a numerical ill-conditioning of the coefficient matrix, adversely affecting the accuracy of source localization. A two-stage method based on regularized constrained total least squares (RCTLSs) is proposed to solve this problem. First, the RCTLS theory is utilized to solve the ill-conditioned problem and the regularization parameter is obtained via minimizing the mean square error (MSE), thereby allowing for a more robust estimation. Second, the error of the RCTLS solution caused by ignoring the constraints in the first step is identified via the Taylor expansion of the intermediate variables. Simulation results reveal that the proposed method attains superior localization accuracy compared to other implemented methods by selecting the regularization parameter properly in diverse scenes.

Self-supervised multi-frame depth estimation outperforms single-frame approaches by utilizing not only appearance information, but also geometric information. A common practice for multi-frame methods is to employ feature-metric bundle adjustment (FBA) to refine depth map initialized from the single-frame prior. However, FBA cannot always provide effective residual updates due to unreliable matching costs, which are corrupted by thin texture, occlusion, and especially object motion. To tackle this problem, we propose a context-aware transformer (CAT) to refine the corrupted matching costs by leveraging the spatial context information. Specifically, the CAT adaptively aggregates matching costs according to the spatial affinity inferred from local appearance context, and produces reliable contextual costs for FBA. Moreover, we design a motion-aware regularization loss to provide supervision for regions with moving objects, making CAT competent for dynamic scenes. Extensive experiments and analyses on the KITTI and Cityscapes datasets demonstrate the effectiveness and superior generalization capability of our approach.

Extracting distinctive, robust, and general 3D local features is essential to downstream tasks such as point cloud registration. However, existing methods either rely on noise-sensitive handcrafted features, or depend on rotation-variant neural architectures. It remains challenging to learn robust and general local feature descriptors for surface matching. In this paper, we propose a new, simple yet effective neural network, termed SpinNet, to extract local surface descriptors which are rotation-invariant whilst sufficiently distinctive and general. A Spatial Point Transformer is first introduced to embed the input local surface into an elaborate cylindrical representation (SO(2) rotation-equivariant), further enabling end-to-end optimization of the entire framework. A Neural Feature Extractor, composed of point-based and 3D cylindrical convolutional layers, is then presented to learn representative and general geometric patterns. An invariant layer is finally used to generate rotation-invariant feature descriptors. Extensive experiments on both indoor and outdoor datasets demonstrate that SpinNet outperforms existing state-of-the-art techniques by a large margin. More critically, it has the best generalization ability across unseen scenarios with different sensor modalities.

Recently, the staring radar has been developed and used to detect and track micro-UAVs. It shows significant advantages in the surveillance of such slow-moving targets with low RCS for its sensitivities and high Doppler resolutions. Meanwhile, the feature of wide area transmission of staring radar results in multipath effect, especially for low altitude targets. A novel elevation angle estimation approach for staring radar is proposed in order to measure the height of targets. Field trials with micro-UAVs show that the proposed approach can help staring radar to obtain target height parameter accurately.

The ultrahigh frequency ultra-wideband synthetic aperture radar (UHF UWB SAR) has the well foliage penetrating and high-resolution imaging, which can be used to detect the concealed area under the foliage in forests. This paper presents an airborne UHF UWB SAR experiment and imaging results. During the winter, an airborne campaign has been carried out in Shanxi Province in China, and the raw data was collected. In this experiment, the SAR system was integrated onboard a CESSNA-172 airplane. The antenna was fixed on the suspension arm of the right wing of the airplane, while the other part of the SAR system was placed on the back seat of this airplane. The experimental results have been obtained from the collected raw data, which proved the imaging performance of the airborne UHF UWB SAR system as well as the validity of the imaging method.

The Fourier transform-based range Doppler method is commonly used in an inverse synthetic aperture radar. Although it has achieved good success in most scenarios, its performance is determined by the rotational angle, and the cross-range resolution is extremely low in the case of a small rotational angle. In this letter, to improve the cross-range resolution, a novel cross-range compression scheme based on the iterative adaptive approach (IAA) is proposed. In addition to the standard IAA to achieve high resolution, the efficient IAA is introduced to suppress the sidelobes due to noise. Both the simulation and experimental results demonstrate that the proposed method has the advantages of parameter-free, high accuracy, and high efficiency.

A novel polarimetric calibration scheme on the basis of lexicographic matrix decomposition is proposed, and a new polarimetric active radar calibrator (PARC) with two independently rotatable antennas is designed to obtain the lexicographic matrices. Thus, the proposed method is realizable and can operate with the lexicographic target vectors instead of the polarization scattering matrix (PSM). The base elements of the lexicographic target vectors are the vectors corresponding to the new PARC with the antennas at different orientations. Moreover, the elements of the vector are just the elements of the PSM. Hence, the error coefficients corresponding to a polarimetric measurement system are directly achieved, and the polarimetric calibration is simple and accurate. Experimental results confirm the superiority of the presented strategy. A 35-dB improvement in the system cross-polarization isolation is obtained.

This letter proposes a signal processing method of passive bistatic radar (PBR) exploiting an uncooperative radar as an illuminator. Compared with other opportunity illuminators, the transmitting signal of a radar usually has a better ambiguity function, which leads to a higher range resolution. Two channels are needed in PBR system. The reference channel is used to estimate radar signal parameters and reconstruct directly propagated signal. The surveillance channel is used to receive scattered wave. An array antenna and a simultaneous multibeam algorithm are necessary in the surveillance channel due to the flexible beam scanning of the uncooperative radar. The procedure of the proposed method is explained in detail, which is then followed by a field experiment. Preliminary results from the field experiment show that the proposed method can be applied to target angle and bistatic range measurement successfully.

In traditional inverse synthetic aperture radar (ISAR) imaging of aircraft, rigid-body motion is usually assumed, and a focused ISAR image can be obtained using the classical range-Doppler algorithm after the translational motion compensation. However, in real-world situations, nonrigid-bodies such as the fast-rotating blades, propellers or turbofans are often present in aircraft. The ISAR image of the main body will be shadowed by the micro-Doppler (m-D) induced by the rotating parts of the target, whereas the m-D parameter estimation of the rotating parts also becomes more difficult because of the interference from the main body returns. To solve this problem, the Doppler frequency difference between the main body and the rotating parts of the aircraft is analyzed and the complex local mean decomposition (CLMD) is applied to separate the m-D signature in the ISAR imaging. The CLMD method can separate the oscillation mode embedded in signals accurately and decompose the complex modulation nonstationary signals adaptively into some stable monocomponents. After the separation, better geometrical features of the main body and better m-D features of the rotating parts can be obtained by processing each independently. The results from the simulated and measured data are given to verify the validity of the algorithm proposed in this paper.