Formation flying synthetic aperture radar (FF-SAR) systems, as an important development direction of multichannel SAR, can achieve high-resolution wide-swath (HRWS) imaging. Coherently combining data from satellite receivers puts a strain on traditional real-time processing systems based on individual satellites. Characteristics such as the power of real-time on-orbit processing platform must be properly balanced with constrained memory and parallel computational resources. This paper proposes a distributed SAR real-time imaging method based on embedded Graphics Processing Units (GPUs). The parallel computing method of chirp scaling (CS) algorithm is designed based on parallel programming model of compute unified device architecture (CUDA), and the optimization methods of memory and performance are proposed for the hardware architecture of embedded GPUs. In particular, unified memory management method is used to avoid data copying and communication delays between the CPU and GPU. A hardware verification system for distributed SAR real-time imaging processing based on multiple embedded GPUs is constructed. The proposed algorithm takes 5.86 seconds to process single-precision floating-point complex imaging with a data size of 8192 × 8192 on a single Jetson Nano platform. The actual power consumption is less than 5 W, and the performance-to-power ratio is greater than 1.7%. The experimental results show that the real-time processing method based on embedded GPUs proposed in this paper has high performance and low power consumption.
It is important to detect the defect of products efficiently in modern industrial manufacturing. Image processing is one of common techniques to achieve defect detection successfully. To process images degraded by noise and lower contrast effects in some scenes, this paper presents a new energy functional with background fitting, then deduces a novel model which approximates to estimate the smoothed background and performs the nonlinear diffusion on the residual image. Noise removal and background correction can be both successfully achieved while the defect feature is preserved. Finally, the proposed method and some other comparative methods are performed on several experiments with some classical degraded images. The numerical results and quantitative evaluation show the efficiency and advantages of the proposed method.
Wind shear must be rapidly recognized during flight because it represents a safety threat. The conventional recognition algorithms rely mainly on the processing of radar signals. Consequently, their results are not two-dimensional and are difficult to visualize, preventing pilots from rapidly and accurately recognizing and predicting the position of shear lines. This study proposes an algorithm for recognizing regions of horizontal wind shear at different altitudes from airborne weather radar. A simulation analysis is used to analyze the radial velocity and spectral width data gathered by ground-based Doppler radar. The algorithm employs the Perona-Malik partial differential equation model to pre-treat Doppler radar base data to reduce noise, maintain fidelity, and remove isolated points. A region-growing algorithm, using radar spectral width and average radial velocity data, is used to rapidly identify regions of horizontal wind shear at different altitudes. By analyzing precipitation events in Fuyang, Chengdu, and Nanjing in China, this study verifies the feasibility of the proposed algorithm. As the simulation results show, the proposed algorithm has better accuracy, better speed, and better shear line continuity than conventional recognition approaches. The improved recognition speed could help pilots recognize and predict wind shear to guarantee flight safety.
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