参照化流形空间融合学习的敏感特征提取与异常检测方法

针对遥测振动信号冲击强、响应周期短、共振频带宽和小样本等特点导致异类模式识别率低的问题,提出基于参照化流形空间融合学习的敏感特征提取与异常检测方法。采用多尺度分析方法将信号正交无遗漏地分解到各尺度带中,提取多尺度特征构造高维特征集;以相同的正常信号样本结合相同类型的异常样本建立专属参照化模型单元,采用线性流形学习获取各参照化模型单元多尺度流形特征差异,增强异常特征的敏感性。融合各参照化模型单元的投影矩阵对原始特征集进行升维再学习,获取低维多尺度敏感流形特征;输入到分类器实现对未知样本状态辨识。实测信号处理结果验证了算法的有效性。     

The prediction formula and a risk-based sensor scheduling method in target detection with guiding information

Sensor scheduling is used to improve the sensing performance in the estimation of targets' states. However, few papers are on the sensor scheduling for target detection with guiding information. This letter can remedy this deficiency. A risk-based target detection method with guiding information is provided firstly, based on which, the sensor scheduling approach is aiming at reducing the risk and uncertainty in target detection, namely risk-based sensor scheduling method. What should be stressed is that the Prediction Formula in sensor scheduling is proposed. Lastly, some examples are conducted to stress the effectiveness of this proposed method.     

Arbitrary-oriented target detection in large scene sar images

Target detection in the field of synthetic aperture radar (SAR) has attracted considerable attention of researchers in national defense technology worldwide, owing to its unique advantages like high resolution and large scene image acquisition capabilities of SAR. However, due to strong speckle noise and low signal-to-noise ratio, it is difficult to extract representative features of target from SAR images, which greatly inhibits the effectiveness of traditional methods. In order to address the above problems, a framework called contextual rotation region-based convolutional neural network (RCNN) with multilayer fusion is proposed in this paper. Specifically, aimed to enable RCNN to perform target detection in large scene SAR images efficiently, maximum sliding strategy is applied to crop the large scene image into a series of sub-images before RCNN. Instead of using the highest-layer output for proposal generation and target detection, fusion feature maps with high resolution and rich semantic information are constructed by multilayer fusion strategy. Then, we put forwards rotation anchors to predict the minimum circumscribed rectangle of targets to reduce redundant detection region. Furthermore, shadow areas serve as contextual features to provide extraneous information for the detector identify and locate targets accurately. Experimental results on the simulated large scene SAR image dataset show that the proposed method achieves a satisfactory performance in large scene SAR target detection.     

利用复单脉冲比的平面阵列雷达群目标检测方法

针对平面阵列雷达群目标检测问题,以矩形阵为例,详细分析方位向和俯仰向复单脉冲比虚部的统计特性;建立平面阵列雷达群目标检测模型,根据恒虚警检测原理推导复单脉冲比虚部的椭圆判决域,并利用复单脉冲比实部辅助判决,综合提出基于复单脉冲比的平面阵列雷达群目标检测算法。针对双目标情形进行仿真实验,结果表明:在较高信噪比、较大角度间隔以及幅度相当且相位差较大的条件下算法能够有效检测群目标。     

GNSS阵列接收机信号解扩前的欺骗干扰检测算法

为了降低天线阵接收机欺骗干扰检测方法的计算量,提出一种信号解扩前的欺骗干扰检测方法。该方法利用不同天线上信号和噪声相关性的差异,在信号解扩前估计其功率,从而进行欺骗干扰检测。仿真结果表明,该方法在降低运算量的同时,具有良好的检测性能。     

战场威胁约束下的纯方位探测单观测站轨迹优化

为保证战场环境下单站纯方位无源探测的精度和观测站的战场生存能力,提出一种战场威胁约束下的单观测站轨迹优化方法。对典型战场威胁进行分析,建立战场威胁和单观测站运动轨迹的数学模型。在此基础上综合考虑单观测站探测精度和所受威胁程度,分别构建精度打分函数和威胁打分函数,得到单观测站的路径选择函数,并对观测站不同轨迹进行评价。仿真结果表明,所提方法能够有效规避战场威胁并提高定位精度,保证战场环境下单站纯方位探测的准确性和可靠性。     

小波变换在零件边缘检测中的应用

目前对机械零件的尺寸、口径等参数的测量大多采用接触测量,而利用零件图像的边缘信息进行非接触测量,则是一种新的更好的检测方式。用此方法可以对机械零件图像的边缘进行精确的检测,再根据相应的数学关系得到相关参数的值,不但可以提高零件检测的精度,还可以大大节省人力和物力。模拟边缘检测技术的具体过程,依次采用最佳阈值迭代算法、小波变换模极大值法、边缘连接局部处理法对零件图像边缘信息进行边缘检测和提取,并对检测过程进行了重点分述。最后,通过一个具体实验,验证了小波技术与其他技术结合使用所获得的良好效果,从而进一步显示了小波变换在边缘检测技术中应用的巨大潜力。     

舰载雷达低空作战性能评估

根据美军用的ADC模型,结合低空目标的特点,提出对舰载雷达低空作战性能的评估方法,把性能评估的重点放在探测能力、定位能力、抗干扰能力和识别能力这四个能力因子上,并给出确定方法.所作研究可为定量分析舰载雷达对低空目标作战性能提供依据.     

图像匹配技术在对岸火力支援中的应用

针对水面舰艇对岸远程火力支援的作战需求和未来的发展趋势,对卫星和空基侦察信息在舰载武器对岸火力支援作战中的应用可行性进行了分析,提出了通过综合应用卫星和舰载无人机(UAV)侦察图像进行景象匹配,精确定位岸上远程目标对射击偏差进行观测修正的技术思路,为水面舰艇对岸上远程固定目标实施精确打击提供了一种可行和有效的手段.     

混沌时间序列中弱信号的检测

介绍了混沌的基本特性,利用混沌时间序列对RBF神经网络进行训练,则训练好的神经网络对混沌序列未来时刻的值具有一定的预测能力.若混沌序列中包含有目标信号,则其混沌特性将受到影响,导致神经网络对其预测误差的增大.这样可根据预测误差对淹没在混沌杂波及混沌杂波加一定强度的白噪声中的弱目标信号进行检测.仿真结果表明这种方法优于传统的目标检测方法,有着较好的杂波抑制能力.