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.     

基于声矢量传感器原理的目标被动定向算法

声测被动定向是声学监视技术的重要研究内容.为了实现对低空运动目标的声学监视,提出了一种基于声矢量传感器原理的被动定向方法,推导出了基于互谱法声强测量技术的定向算法,并对定向算法进行了分析和数值仿真.仿真表明:当传感器尺寸为0.28m、信号信噪比为5dB时,定向精度在2.5°以内.仿真结果说明,该定向算法是正确可行的,有较大的实用价值.该研究成果可直接用于空中运动目标,如飞机、导弹和无人机等目标的声测被动定向.     

高频地波雷达目标高度估计的AR模型方法

高度估计对于高频地波雷获取三维信息、提高预警能力很有意义。然而目标RCS的随机起伏对高度估计产生严重影响,通过对RCS随机起伏的AR模型建模和模型参数的自适应估计,提出了在RCS变化的条件下高度估计的扩展卡尔曼滤波算法。通过Monte Carlo仿真和外场试验数据的处理,验证了该方法对抑制RCS随机起伏、提高高度估计精度的有效性。     

基于频率补偿的时延估计方法

由于2个测量站的通道幅相不一致而导致送给时延估计器的两路信号的频率有差异,继而影响时延估计和系统定位精度。首先理论推导了频率差异是如何影响广义相关时延估计的,接着应用频域相关函数对频率差异做准确估计和补偿,最后给出了频差补偿后的时延估计方法。以4种常用通信信号为例,仿真验证了不同信噪比下所提出方法的正确性和有效性。     

多点源交叉波束干涉效应特征分析

介绍多点源交叉波束通过干涉效应实现交汇空间功率合成的基本原理,根据空间功率合成机理,建立对功率合成效率、同相叠加点间距的计算机仿真,完成多点源交叉波束干涉效应特征分析,以此作为多点源交叉波束空间功率合成的评估依据。     

多项式输出中相关变量的结构贡献和相关贡献分析

以二次不含交叉项的多项式为例,解析推导了正态相关输入变量对输出响应量方差贡献的结构贡献部分和相关贡献部分。通过算例验证了解析结果的正确性。将所研究的指标和已有的进行对比,归纳出结构贡献部分和相关贡献部分的侧重与统一。解析解可直接用于极限状态函数不超过两次且不含交叉项的结构和相关贡献的识别,为其他新的数值算法提供了参考对照。     

基于目标RCS加权滤波方法抑制角闪烁的研究

单脉冲雷达进入末段跟踪状态后,其测角误差主要来自于目标的角闪烁。利用目标(RCS)起伏与角闪烁线偏差绝对值之间的负相关性,采用目标RCS加权滤波的方法能够有效抑制角闪烁。仿真数据和实测数据的验证结果表明,该方法能够有效抑制单脉冲雷达角闪烁现象,具有较高的工程应用价值。     

ISAR成像方位向定标方法研究

提出了一种适合工程应用的ISAR方位向定标方法,通过对相位数据的平滑、分段、拟合,完成了对转速的快捷准确的估算,同时分析了散射点分布对定标精度的影响,结合仿真和试验数据验证了方法的有效性,并根据工程实践的经验,提出了一些提高定标精度的准则。     

基于混沌特征的高分辨雷达目标识别

根据高分辨导弹制导雷达的基本工作原则和舰船目标的散射特性,利用现有雷达回波的仿真技术,在合理的战术想定下,计算了三类典型舰船的雷达回波.在此基础上,研究了利用雷达回波功率谱的方法来识别目标类型的有效性,并基于混沌理论提出了一种比功率谱方法更加有效的新的识别方法,从而为进一步利用混沌理论来开发雷达目标识别技术奠定了初步的理论基础.     

Aircraft routing under the risk of detection

The deterministic problem for finding an aircraft's optimal risk trajectory in a threat environment has been formulated. The threat is associated with the risk of aircraft detection by radars or similar sensors. The model considers an aircraft's trajectory in three‐dimensional (3‐D) space and represents the aircraft by a symmetrical ellipsoid with the axis of symmetry directing the trajectory. Analytical and discrete optimization approaches for routing an aircraft with variable radar cross‐section (RCS) subject to a constraint on the trajectory length have been developed. Through techniques of Calculus of Variations, the analytical approach reduces the original risk optimization problem to a vectorial nonlinear differential equation. In the case of a single detecting installation, a solution to this equation is expressed by a quadrature. A network optimization approach reduces the original problem to the Constrained Shortest Path Problem (CSPP) for a 3‐D network. The CSPP has been solved for various ellipsoid shapes and different length constraints in cases with several radars. The impact of ellipsoid shape on the geometry of an optimal trajectory as well as the impact of variable RCS on the performance of a network optimization algorithm have been analyzed and illustrated by several numerical examples. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006