Synthetic Aperture Radar (SAR) images are often degraded by multiplicative speckle noise, hindering their processing and analysis. While Convolutional Neural Networks (CNNs) have limited receptive fields, limiting global feature capture, Transformer-based methods struggle to differentiate between features and noise as network depth increases. We propose DAMSNet, a novel U-Net-based architecture designed for the efficient reduction of noise in SAR images while preserving fine details. Experiments on both synthetic and real SAR datasets show that DAMSNet has demonstrated superior performance over traditional methods and state-of-the-art deep learning algorithms. To the best of our knowledge, DAMSNet is the first to incorporate a differential attention mechanism for SAR image despeckling.

This letter mainly considers the environmental clutter problem in distinguishing between stationary humans and animals through-wall circumstances. Focusing on the challenges of object identification in the time–frequency map, we propose a cross-scale feature aggregation (CSFA) network based on channel–spatial attention, which can improve the identification accuracy of stationary humans and animals. Specifically, life detection radar is utilized to collect data, and the time–frequency analysis method synchrosqueezing transform (SST) is used to suppress the signal noise and generate higher-resolution time–frequency maps. In order to make full use of the target information, we use a feature pyramid network (FPN) to obtain multilevel feature information maps from time–frequency maps. Then, the CSFA module is utilized to extract detailed micro-Doppler feature information from feature maps. And we use a deep convolutional neural network (CNN) to classify humans from animals. Experimental results show that the proposed model has a better performance in accuracy compared with the existing methods.

This letter considers the coherent integration problem for a maneuvering target in low signal-to-noise-ratio (SNR) circumstances. Focusing on the range migration (RM) and Doppler frequency migration (DFM) problems caused by the motion of the target, we propose a new method called Radon-general linear chirplet transform (RGLCT). Jointly motion parameters search is employed to obtain the trajectory of the maneuvering target and the coherent integration is achieved via general linear chirplet transform (GLCT). Because of the non-sensitive-to-noise feature of the GLCT, RGLCT can realize weak target coherent integration in very low SNR environments. Multi-target detection can be achieved successfully because the GLCT is not influenced by the cross-term components. Finally, simulations and real data experiments are performed to demonstrate the effectiveness of the method. The results show that the proposed method has superior detection ability than methods including Radon-Fourier transform (RFT), and Radon-Lv’s distribution (RLVD). Both theory and experiments have fully proved that the proposed method can effectively realize coherent integration in low SNR environments.

To improve the life-detection radar resolution under certain hardware conditions, in this letter, a deep mutual learning generative adversarial network model (Deep Mutual GAN) is proposed. In the proposed model, the generator can improve the angular resolution of the input low-resolution radar image by five times, which is enough to meet our requirements for the resolution of life detection. We innovatively use two generators in GAN with the same network structure and make the two generators learn from each other. In this way, the learning process of a generator is not only achieved by its confrontation with the discriminator but also guided by another generator. As a result, the knowledge of the generator is no longer only obtained through its own learning; each generator learns knowledge from another generator while learning knowledge by itself. The proposed model can effectively make the convergence of GAN more stable and improves the super resolution effect. We also introduce the details of the network structure of generator and discriminator, in which residual learning and a symmetrical network structure are applied. The experimental results show that the proposed method can achieve state-of-the-art imaging effect, which is meaningful for subsequent target detection and recognition.

Spaceborne synthetic aperture radar (SAR) can operate at various modes, including stripmap mode, spotlight mode, sliding spotlight mode, and Terrain observation by progressive scans (TOPS) mode. These four imaging modes can be regarded as unified, differing in rotation-center ranges. To uniformly focus the data of these four imaging modes in real-time, this article proposes a real-time unified focusing algorithm (RT-UFA) for the multi-mode SAR via azimuth sub-aperture complex-valued image combining and scaling. The imaging processing can be performed while the data are being recorded. In the first stage of imaging, sub-aperture complex-valued images with relative low-resolution can be obtained by the cascade of the extended chirp scaling (ECS) and azimuth dechirp. Then, these complex-valued images are coherently combined by shifting the integer number of pixels, and thus the full-resolution image of all the recorded data can be obtained. The azimuth scaling and the pixels shifting in the RT-UFA are analyzed in detail. Simulation and SAR data results are presented to validate the analysis and RT-UFA.

Multiple azimuth channels (MACs) synthetic aperture radar (SAR) can theoretically achieve high azimuth resolution and wide swath (HRWS). Nevertheless, in practice, channel mismatch will lead to ghost or azimuth ambiguities, which will degrade the imaging quality. This article proposes a novel idea for estimating the channel mismatch of MACs SAR in the image domain. First, we found that the degree of freedom (DOF) of MACs signals doubles after signal reconstruction and imaging. As a result, when the channel number is not great enough, the subspace method for error estimation is unable to be implemented. To deal with this problem, we introduce a DOF compression method based on spectral filtering. This method can decrease the image-domain DOF. Finally, an image-domain subspace method is proposed to estimate the channel phase error, using the focused data and selecting the high SNR region of SAR images. The proposed method has advantages for the channel phase error estimation. Simulated space-borne MACs SAR data and real measured airborne SAR data are processed to demonstrate the effectiveness of the proposed method.

In this paper we propose an analytical technique for the steady-state dynamic temperature analysis (SSDTA) of multiprocessor systems with periodic applications. The approach is accurate and, moreover, fast, such that it can be included inside an optimization loop for embedded system design. Using the proposed solution, a temperature-aware reliability optimization, based on the thermal cycling failure mechanism, is presented. The experimental results confirm the quality and speed of our SSDTA technique, compared to the state of the art. They also show that the lifetime of an embedded system can significantly be improved, without sacrificing its energy efficiency, by taking into consideration, during the design stage, the steady-state dynamic temperature profile of the system.

Large-scale integration with deep sub-micron technologies has led to high power densities and high chip working temperatures. At the same time, leakage energy has become the dominant energy consumption source of circuits due to reduced threshold voltages. Given the close interdependence between temperature and leakage current, temperature has become a major issue to be considered for power-aware system level design techniques. In this paper, we address the issue of leakage energy optimization through temperature aware idle time distribution (ITD). We first propose an offline ITD technique to optimize leakage energy consumption, where only static idle time is distributed. To account for the dynamic slack, we then propose an online ITD technique where both static and dynamic idle time are considered. To improve the efficiency of our ITD techniques, we also propose an analytical temperature analysis approach which is accurate and, yet, sufficiently fast to be used inside the energy optimization loop.

With new technologies, temperature has become a major issue to be considered at system level design. In this paper we propose a temperature aware idle time distribution technique for energy optimization with dynamic voltage scaling (DVS). A temperature analysis approach is also proposed which is accurate and, yet, sufficiently fast to be used inside the optimization loop for idle time distribution and voltage selection.

With new technologies, temperature has become a major issue to be considered at system level design. Without taking temperature aspects into consideration, no approach to energy or/and performance optimization will be sufficiently accurate and efficient. In this paper we propose an on-line temperature aware dynamic voltage and frequency scaling (DVFS) technique which is able to exploit both static and dynamic slack. The approach implies an offline temperature aware optimization step and on-line voltage/frequency settings based on temperature sensor readings. Most importantly, the presented approach is aware of the frequency/temperature dependency, by which important additional energy savings are obtained.

This paper proposes a temperature-aware dynamic voltage selection technique for energy minimization and presents a thorough analysis of the parameters that influence the potential gains that can be expected from such a technique, compared to a voltage selection approach that ignores temperature.