In this article, we propose a method based on time coding metasurface (TCM) to modulate the characteristics of radar targets in complex high-resolution range profiles (HRRPs). Unlike the existing works where metasurfaces are typically attached to target surfaces, in our work, the metasurface and controlled target are separable, making modulation more flexible. The proposed method involves controlling the equivalent scattering phase of distance offset false targets within the HRRP, which are generated through TCM by adjusting the modulation parameters. The scattering characteristics of the false target and the controlled target are combined within the same distance unit to create a superimposed scattering feature vector. By taking into account the influence of interference effect, we are able to modulate the scattering characteristics of radar targets within the complex HRRP. The proposed method was validated through theoretical analysis, simulation experiments, and actual experiments conducted in various scenarios. The experimental results demonstrate that the false targets generated by the external TCM can be used to enhance or weaken the scattering intensity of the original target in HRRP, thereby changing the overall image characteristics and posing challenges to target recognition. This method can achieve the modulation of radar target image characteristics under noncoverage conditions, expanding the application scenarios of metasurfaces in the field of radar electronic countermeasures (ECM).

Aircraft target detection in synthetic aperture radar (SAR) images is a challenging task due to the discrete scattering points and severe background clutter interference. Currently, methods with convolution- or transformer-based paradigms cannot adequately address these issues. In this letter, we explore diffusion models for SAR image aircraft target detection for the first time and propose a novel Diffusion-based aircraft target Detection network for SAR images (DiffDet4SAR). Specifically, the proposed DiffDet4SAR yields two main advantages for SAR aircraft target detection: 1) DiffDet4SAR maps the SAR aircraft target detection task to a denoising diffusion process of bounding boxes without heuristic anchor size selection, effectively enabling large variations in aircraft sizes to be accommodated and 2) the dedicatedly designed a scattering feature enhancement (SFE) module further reduces the clutter intensity and enhances the target saliency during inference. Extensive experimental results on the SAR-AIRcraft-1.0 dataset show that the proposed DiffDet4SAR achieves 88.4% mAP50, outperforming the state-of-the-art methods by 6%. The code is available at https://github.com/JoyeZLearning/DiffDet4SAR .

Owing to the large degrees of freedom and reduced mutual coupling by generating difference coarrays, nonuniform linear arrays have aroused great interest in direction of arrival estimation. Previous works have shown some improved sparse arrays, while few find the common features hidden within these structures. In this paper, we define a generic-coarray concept to reveal the impacts of variable ranges and element spacing on the uniform degrees of freedom (uDOFs), by which the sufficient condition for the connected coarrays is derived. We then propose an enhanced and generalized coprime array (EGCA) structure from the generic-coarray perspective. We show that the closed-form expression for the range of uDOFs is a function of sensor numbers and inter-element spacing. We prove that, by coarray extension and hole filling, the optimized EGCA possesses more uDOFs than the previous coprime arrays. Furthermore, EGCA also provides the minimum number of sensor pairs with small separation. Simulations verify the superiority of EGCA using the subspace-based algorithm.

Recently, there have been tremendous efforts in developing lightweight Deep Neural Networks (DNNs) with satisfactory accuracy, which can enable the ubiquitous deployment of DNNs in edge devices. The core challenge of developing compact and efficient DNNs lies in how to balance the competing goals of achieving high accuracy and high efficiency. In this paper we propose two novel types of convolutions, dubbed Pixel Difference Convolution (PDC) and Binary PDC (Bi-PDC) which enjoy the following benefits: capturing higher-order local differential information, computationally efficient, and able to be integrated with existing DNNs. With PDC and Bi-PDC, we further present two lightweight deep networks named Pixel Difference Networks (PiDiNet) and Binary PiDiNet (Bi-PiDiNet) respectively to learn highly efficient yet more accurate representations for visual tasks including edge detection and object recognition. Extensive experiments on popular datasets (BSDS500, ImageNet, LFW, YTF, etc.) show that PiDiNet and Bi-PiDiNet achieve the best accuracy-efficiency trade-off. For edge detection, PiDiNet is the first network that can be trained without ImageNet, and can achieve the human-level performance on BSDS500 at 100 FPS and with $< $ 1 M parameters. For object recognition, among existing Binary DNNs, Bi-PiDiNet achieves the best accuracy and a nearly $2\times$ reduction of computational cost on ResNet18.

This paper focuses on generating Inverse Synthetic Aperture Radar (ISAR) images from optical images, in particular, for orbit space targets. ISAR images are widely applied in space target observation and classification tasks, whereas, limited to the expensive cost of ISAR sample collection, training deep learning-based ISAR image classifiers with insufficient samples and generating ISAR samples from emulation optical images via image translation techniques have attracted increasing attention. Image translation has highlighted significant success and popularity in computer vision, remote sensing and data generation societies. However, most of the existing methods are implemented under the discipline of extracting the explicit pixel-level features and do not perform effectively while entailing translation to domains with specific implicit features, such as ISAR image does. We propose a meta-learning based domain prior to implicit feature modelling and apply it to CycleGAN and UNIT models to realize effective translations between the ISAR and optical domains. Two representative implicit features, ISAR scattering distribution feature from the physical domain and the classification identifying feature from the task domain, are elaborately formulated with explicit modelling in statistic form. A meta-learning based training scheme is introduced to leverage the mutual knowledge of domain priors across different samples, and thus allows few-shot learning capacity with dramatically reduced training samples. Extensive simulations validate that the obtained ISAR images have better visible-authenticity and training-effectiveness than the existing image translation approaches on various synthetic datasets. Source codes are available at.

Class-Incremental Learning (CIL) aims at incrementally learning novel classes without forgetting old ones. This capability becomes more challenging when novel tasks contain one or a few labeled training samples, which leads to a more practical learning scenario, i.e ., Few-Shot Class-Incremental Learning (FSCIL). The dilemma on FSCIL lies in serious overfitting and exacerbated catastrophic forgetting caused by the limited training data from novel classes. In this paper, excited by the easy accessibility of unlabeled data, we conduct a pioneering work and focus on a Semi-Supervised Few-Shot Class-Incremental Learning (Semi-FSCIL) problem, which requires the model incrementally to learn new classes from extremely limited labeled samples and a large number of unlabeled samples. To address this problem, a simple but efficient framework is first constructed based on the knowledge distillation technique to alleviate catastrophic forgetting. To efficiently mitigate the overfitting problem on novel categories with unlabeled data, uncertainty-guided semi-supervised learning is incorporated into this framework to select unlabeled samples into incremental learning sessions considering the model uncertainty. This process provides extra reliable supervision for the distillation process and contributes to better formulating the class means. Our extensive experiments on CIFAR100, miniImageNet and CUB200 datasets demonstrate the promising performance of our proposed method, and define baselines in this new research direction.