基于改进YOLOv3的SAR舰船图像目标识别技术

合成孔径雷达(SAR)图像自动目标识别(ATR)技术是人工图像解译的关键技术之一。针对传统的SAR舰船目标检测算法大多受限于场景且泛化能力较差的问题,设计了一种基于改进YOLOv3网络的检测模型。将YOLOv3与DenseNet网络融合,使用稠密网络模块代替用于提取中小尺度特征的残差网络模块,通过训练得到模型的最优权重,实现端到端的目标检测。使用综合交并比(GIoU)损失代替交并比(IoU)边界框回归损失,提供更加准确的边界框位置信息,提高检测精度,采用中国科学院空天信息研究院制作的SAR图像船舶检测数据集进行测试。测试结果表明:与原YOLOv3算法相比,改进后的YOLOv3检测准确率提高了1.4%。     

单脉冲跟踪雷达多目标检测算法性能分析

对2种检测单脉冲雷达主波束内是否存在2个不可区分目标的算法进行了推广,将其应用到波束内可能存在N个不可区分的目标,并仿真了波束内存在3个目标时的检测性能。仿真表明,在总信噪比相同时,3个目标的检测概率不一定大于2个目标的检测概率,处于方位向上间距最大的2个目标的信噪比对整个检测概率影响较大。     

Subsonic impulsively starting flow at a high angle of attack with shock wave and vortex interaction

Impulsively starting flow, by a sudden attainment of a large angle of attack, has been well studied for incompressible and supersonic flows, but less studied for subsonic flow. Recently, a preliminary numerical study for subsonic starting flow at a high angle of attack displays an advance of stall around a Mach number of 0.5, when compared to other Mach numbers. To see what happens in this special case, we conduct here in this paper a further study for this case, to display and analyze the full flow structures. We find that for a Mach number around 0.5, a local supersonic flow region repeatedly splits and merges, and a pair of left-going and right-going unsteady shock waves are embedded inside the leading edge vortex once it is sufficiently grown up and detached from the leading edge. The flow evolution during the formation of shock waves is displayed in detail. The reason for the formation of these shock waves is explained here using the Laval nozzle flow theory. The existence of this shock pair inside the vortex, for a Mach number only close to 0.5, may help the growing of the trailing edge vortex responsible for the advance of stall observed previously.     

针对弹载平台红外小目标的检测与跟踪算法

为了提高对红外小目标检测与跟踪的精度与速度，设计了一种针对红外小目标的单目标检测与跟踪算法,包含一个基于MB_LBP+AdaBoost与管道滤波器的目标检测模块，和一个有跟踪失败监测机制的基于模板匹配的多尺度跟踪模块。经过测试，该算法对边长11~31像素大小的指定类型的红外小目标取得了较好的检测效果，目标跟踪模块对于目标的尺度变化，快速运动，遮挡有较高的鲁棒性，算法中的跟踪失败监测机制在检测到跟踪异常的情况下能重新调用目标检测算法找回目标，经过测试对比，本方法跟踪的精度和速度综合优于主流跟踪算法，并且能够在弹载平台上实时运行。     

Automatic extraction of lineaments based on wavelet edge detection and aided tracking by hillshade

Lineaments refer to the linear or curvilinear textures on remote sensing image, whose general spatial distribution characteristics are often the response of deep geological sturcture at the surface. Firstly, we use wavelet modulus maximum transformation to detect the edges with 4 scales on Landsat – 8 OLI B5 image and analyze their multi-scale characteristics. As the result, it is determined that the optimal scale of edge detection is 4, and the outline that consist of the edge pixels is roughly corresponding to the geological structure of mine area. Thus the incomplete lineaments have been extracted by using the 2D otsu algorithm. Secondly, the hillshade map generated based on DEM is processed to generate binarized linear shadow. Finally, the linear shadow is superimposed on the lineaments preliminarily extracted to obtain the optimized lineaments. Experiment results show that, based on the method, there are some deformation and displacement between the lineaments extracted and the actual geological structure, and it fail to effectively extract Qilinchang Fault, but lineaments are in good correspondence with Kuangshanchang Fault, Dongtou Fault and Niulan River Fault, which are basically in accord with the geological structure framework of the mine area.     

天籁射电阵的空间碎片探测性能分析

提出以曲靖非相干散射雷达作为一种可能的发射源与天籁射电阵组成一套双基地雷达空间碎片探测系统，对该系统的空间碎片探测性能进行计算与仿真分析，包括可探测目标雷达散射截面积(RCS)、天籁射电阵对空间目标与碎片探测时间、探测效率等。分析表明，该系统具有探测直径10 cm以下级空间小碎片的潜力，并在最后提出了一种基于该系统的交叉波束的确定方法及对空间碎片定位的方法。     

Sensor-fault tolerant attitude determination using two-stage estimator

Satellite attitude determination accuracy significantly drops when sensor-fault occurs. Hence, a proper mitigation strategy to detect sensor-fault and accurately estimate corresponding fault magnitudes is mandatory for robust and accurate attitude determination. In this paper, a novel sensor-fault tolerant precise attitude estimator is proposed consisting of two stages. In the first stage, sensor-fault is detected, and the associated sensor parameter change is roughly estimated using an interacting multiple-model (IMM) approach. Subsequently, the second stage is triggered. The sensor parameter change is precisely estimated with a new sensor-parameter-augmented filter. This is defined as a selectively augmented extended Kalman filter (SAEKF) in this paper. The conventional augmented extended Kalman filter (AEKF) is computationally more expensive than the proposed SAEKF. The SAEKF augments only the sensor parameters affected by sensor-faults, not the full sensor parameters, into the state vector. This leads to a significant computational time-saving. A transition method from the first stage to the second stage is also investigated. Numerical simulation results demonstrate that the proposed two-stage approach has smaller attitude determination errors than the existing algorithms, ranged from 21.7% to 88.8%, in cases with gyro scale factor error or misalignment.     

Automatic detection of typical dust devils from Mars landscape images

This paper presents an improved algorithm for automatic detection of Martian dust devils that successfully extracts tiny bright dust devils and obscured large dust devils from two subtracted landscape images. These dust devils are frequently observed using visible cameras onboard landers or rovers. Nevertheless, previous research on automated detection of dust devils has not focused on these common types of dust devils, but on dust devils that appear on images to be irregularly bright and large. In this study, we detect these common dust devils automatically using two kinds of parameter sets for thresholding when binarizing subtracted images. We automatically extract dust devils from 266 images taken by the Spirit rover to evaluate our algorithm. Taking dust devils detected by visual inspection to be ground truth, the precision, recall and F-measure values are 0.77, 0.86, and 0.81, respectively.     

天基逆合成孔径激光雷达高灵敏度接收技术

逆合成孔径激光雷达(ISAL)是一种主动式成像雷达,是合成孔径技术和激光雷达技术相结合的产物,具有极高的成像分辨率。由于无大气干扰,因此天基成像的环境十分有利于ISAL系统的部署,然而探测灵敏度不够,难以实现远距离探测的问题极大地制约了天基ISAL系统的应用。针对这一难点,提出使用高灵敏度相干平衡探测实现ISAL的空间远距离探测,计算了ISAL平衡探测的信噪比。在此基础上,提出了一种基于频率上转换的高灵敏度平衡探测接收方法,即信号光和本振光先经过频率上转换,再进行平衡探测,并实验验证了该方法的可行性。     

星面探测机器人自主移动技术

为进一步扩大探测范围,提高探测效率,并为后续星面基地建设提供服务,星面探测机器人需从远程地面遥控方式向自主移动方式发展,为此需要解决地图构建、鲁棒定位、探测导航等问题。为推动星面探测机器人自主移动能力的发展,借鉴目前快速发展的无人驾驶技术和地面自主移动技术,归纳总结了目前国内外星面探测移动技术的研究现状和存在的问题,以及地面自主移动研究在地图构建、鲁棒定位、探测导航等方面取得的进展。在结合星面探测应用的特殊性的基础上,提出了未来在长期定位和可变形重建领域开展更深入研究的建议。