Nonlinear Trajectory SAR Imaging Algorithms:Overview and Experimental Comparison

The nonlinear characteristics of the motion trajectory of the synthetic aperture radar(SAR)flight platform can lead to severe two-dimensional space-variance characteristics of the signal,greatly affecting the imaging quality,and are currently considered as one of the difficulties in the field of SAR imaging.This paper first discusses the nonlinear trajectory SAR model and its space-variance characteristics and then discusses algorithms such as scaling-based algorithms,interpolation-based algorithms,time-domain algorithms,and hybrid algorithms.The relative merits and applicability of each algorithm are analyzed.Finally,computer simulation and actual data validation are conducted.

基于DPSIR模型的安徽省低碳经济发展成效评价

基于驱动力-压力-状态-影响-响应(DPSIR)理论模型,构建包含五个层级的指标体系,采用熵权法对指标进行赋值,对安徽省低碳经济发展进行综合评价.研究发现,安徽城镇化率提高和城乡居民收入增加,人们对绿色生存环境和健康生活方式的诉求越来越强烈,形成对低碳经济发展的直接驱动.发展低碳经济不仅直接推动消费绿色升级,还将创造更多的新兴就业岗位,对高碳产业升级转型中的过剩劳动力起到吸纳作用.然而,2010年~2019年安徽经济发展仍处于高碳区间,这与经济对工业的依赖度高且能源结构以煤为主存在直接关系.安徽低碳经济发展存在较大抬升空间,未来政策宜从密切监测低碳关键指标变化、坚持高耗能产业转型升级、推动低碳技术进步和产学研融合、创新低碳经济投融资机制、发展低碳服务业等多点发力.

Optimal Matching Analysis of Net Buoyancy and Pitching Angle for Underwater Gliders

Control parameter optimization is an efficient way to improve the endurance of underwater gliders(UGs),which influences their gliding efficiency and energy consumption.This paper analyzes the optimal matching between the net buoyancy and the pitching angle and proposes a segmented control strategy of Petrel-L.The optimization of this strategy is established based on the gliding range model of UG,which is solved based on the approximate model,and the variations of the optimal control parameters with the hotel load are obtained.The optimization results indicate that the segmented control strategy can significantly increase the gliding range when the optimal matching between the net buoyancy and the pitching angle is reached,and the increase rate is influenced by the hotel load.The gliding range of the underwater glider can be increased by 10.47%at a hotel load of 0.5 W.The optimal matching analysis adopted in this study can be applied to other UGs to realize endurance improvement.

3D laser scanning strategy based on cascaded deep neural network

A 3D laser scanning strategy based on cascaded deep neural network is proposed for the scanning system converted from 2D Lidar with a pitching motion device. The strategy is aimed at moving target detection and monitoring. Combining the device characteristics, the strategy first proposes a cascaded deep neural network, which inputs 2D point cloud, color image and pitching angle. The outputs are target distance and speed classification. And the cross-entropy loss function of network is modified by using focal loss and uniform distribution to improve the recognition accuracy. Then a pitching range and speed model are proposed to determine pitching motion parameters. Finally, the adaptive scanning is realized by integral separate speed PID. The experimental results show that the accuracies of the improved network target detection box, distance and speed classification are 90.17%, 96.87% and 96.97%, respectively. The average speed error of the improved PID is 0.4239°/s, and the average strategy execution time is 0.1521 s.The range and speed model can effectively reduce the collection of useless information and the deformation of the target point cloud. Conclusively, the experimental of overall scanning strategy show that it can improve target point cloud integrity and density while ensuring the capture of target.

Advanced high-order nonlinear chirp scaling algorithm for high-resolution wide-swath spaceborne SAR

Spaceborne Synthetic Aperture Radar(SAR) is a well-established and powerful imaging technology that can provide high-resolution images of the Earth’s surface on a global scale. For future SAR systems, one of the key capabilities is to acquire images with both high-resolution and wide-swath. In parallel to the evolution of SAR sensors, more precise range models, and effective imaging algorithms are required. Due to the significant azimuth-variance of the echo signal in High-Resolution Wide-Swath(HRWS) SAR, two challenges have been faced in conventional imaging algorithms. The first challenge is constructing a precise range model of the whole scene and the second one is to develop an effective imaging algorithm since existing ones fail to process highresolution and wide azimuth swath SAR data effectively. In this paper, an Advanced High-order Nonlinear Chirp Scaling(A-HNLCS) algorithm for HRWS SAR is proposed. First, a novel Second-Order Equivalent Squint Range Model(SOESRM) is developed to describe the range history of the whole scene, by introducing a quadratic curve to fit the deviation of the azimuth FM rate. Second, a corresponding algorithm is derived, where the azimuth-variance of the echo signal is solved by azimuth equalizing processing and accurate focusing is achieved through a high-order nonlinear chirp scaling algorithm. As a result, the whole scene can be accurately focused through one single imaging processing. Simulations are provided to validate the proposed range model and imaging algorithm.