Rapid and robust initialization for monocular visual inertial navigation within multi-state Kalman filter

Sensor-fusion based navigation attracts significant attentions for its robustness and accuracy in various applications. To achieve a versatile and efficient state estimation both indoor and outdoor, this paper presents an improved monocular visual inertial navigation architecture within the Multi-State Constraint Kalman Filter (MSCKF). In addition, to alleviate the initialization demands by appending enough stable poses in MSCKF, a rapid and robust Initialization MSCKF (I-MSCKF) navigation method is proposed in the paper. Based on the trifocal tensor and sigma-point filter, the initialization of the integrated navigation can be accomplished within three consecutive visual frames. Thus, the proposed I-MSCKF method can improve the navigation performance when suffered from shocks at the initial stage. Moreover, the sigma-point filter is applied at initial stage to improve the accuracy for state estimation. The state vector generated at initial stage from the proposed method is consistent with MSCKF, and thus a seamless transition can be achieved between the initialization and the subsequent navigation in I-MSCKF. Finally, the experimental results show that the proposed I-MSCKF method can improve the robustness and accuracy for monocular visual inertial navigations.     

Super-resolution reconstruction of astronomical images using time-scale adaptive normalized convolution

In this work, we describe a new multiframe Super-Resolution (SR) framework based on time-scale adaptive Normalized Convolution (NC), and apply it to astronomical images. The method mainly uses the conceptual basis of NC where each neighborhood of a signal is expressed in terms of the corresponding subspace expanded by the chosen polynomial basis function. Instead of the conventional NC, the introduced spatially adaptive filtering kernel is utilized as the applicability function of shape-adaptive NC, which fits the local image structure information including shape and orientation. This makes it possible to obtain image patches with the same modality, which are collected for polynomial expansion to maximize the signal-to-noise ratio and suppress aliasing artifacts across lines and edges. The robust signal certainty takes the confidence value at each point into account before a local polynomial expansion to minimize the influence of outliers. Finally, the temporal scale applicability is considered to omit accurate motion estimation since it is easy to result in annoying registration errors in real astronomical applications. Excellent SR reconstruction capability of the time-scale adaptive NC is demonstrated through fundamental experiments on both synthetic images and real astronomical images when compared with other SR reconstruction methods.     

基于相似原理的风车状态进口空气流量和内阻力估算方法

风车状态进口流量和内阻力是试飞安全评估中必须获取的重要信息。为评估燃烧室空中复燃能力和风车状态下飞机阻力,在1维管流理论基础上,结合流量连续原理,介绍了1种通过直接测量尾喷管出口马赫数、间接获取空气流量和内阻力的测量方案,并根据相似原理,将其推广应用至不同几何的发动机,发展了1种不依赖发动机部件特性,且适用于不同几何结构的涡喷/涡扇发动机通用的风车状态空气流量和内阻力估算方法。最后以GE公司的CF34-10A发动机为例,对其风车状态下的进口空气流量和内阻力进行了估算,估算结果与GE公司提供的风车状态数据吻合,满足工程需求。该方法对多发飞机起飞、爬升、着陆以及巡航阶段单发失效时的飞机阻力性能估算和风车起动研究具有重要意义。     

基于星载视频SAR的海上运动目标监视方法

传统SAR处理方法已无法满足日益复杂的战场环境,在监测、跟踪感兴趣区域内的机动目标和场景变化方面存在不足。针对此情况,提出了一种基于星载视频SAR(Video SAR)的海上运动目标监视方法,通过对目标区域持续观测获得全面的目标态势信息。首先,分析了视频SAR工作原理,采用数据重叠复用的方式来保证SAR视频产品的流畅度;结合图像配准,再利用不同帧图像之间动目标主体位置偏差和阴影变化反演动目标速度,同时改进传统后向投影(Back Projection,BP)算法,使其适用于视频SAR的高效性要求;最后,通过计算机仿真验证了该方法的精确性和有效性。     

一种带随机时延和丢包的分布式多传感器融合估计方法

研究了分布式采样线性系统的最优信息融合问题。其中,传感器信息通过无线网络发送到中心单元,每个传感器的测量值受随机时延甚至丢包的影响,最优传感器融合设计为一个带有缓冲测量值的时变卡尔曼滤波器。进行了算例仿真与分析,表明了融合估计器的有效性。     

基于模拟退火的改进粒子群算法研究及应用

为加快粒子群算法效率,跳出局部最优陷阱,得到高精度最优解,文章提出了基于模拟退火的带收缩因子的粒子群混合算法(SACPSO)。首先,对混合优化算法进行了分析;然后,对混合算法进行函数数值仿真;最后,将SACPSO算法应用于PID参数整定问题。结果表明,改进粒子群算法的稳定性和搜索精度有了明显提高,收敛速度明显加快;在PID参数整定应用上,同传统方法相比,系统稳定,收敛性能好。     

基于侧扫声纳图像的AUV侧向速度估计与DVL粗差检测

复杂水底环境和自主潜航器(AUV）机动会造成Doppler测速仪(DVL）速度测量结果中出现粗差,提出一种检测DVL速度粗差数据的方法。该方法利用侧扫声纳图像数据,结合SIFT特征匹配算法求取声纳图像的边缘特征点。根据声纳图像成像原理,计算出每一帧图像相对上一帧图像对应特征点像素的横向平移量;结合特征像素平移量与AUV实际横向位移的比例尺度关系以及声纳侧扫周期推算出AUV侧向速度。该侧向速度用于检测水下组合导航中DVL的速度粗差,在几十米水深的浅水区,该方法能够检测并剔除出大于0.2m/s的DVL侧向速度粗差,显著减小DVL前向速度误差。算法可实时实现,改善AUV水下组合导航性能。     

基于阶层标识的无人机自主精准降落系统

随着微小型无人机（UAV）在航拍、测绘、环境监测、快递投送等民用领域的广泛应用,对微小型无人机的可用性和可靠性提出了更高的要求。为了使微小型无人机能够精确地完成自主降落,由于计算机视觉部署成本低、独立性强、信息丰富等特点,提出了通过识别匹配一种多层嵌套二维编码的阶层降落标识来进行相对定位的算法,并展示了与之对应的阶层标识检测及定位的无人机自主降落系统,由于编码的信息量与其他系统相比,具有高低空的高识别率、编码空间大等特点,故此系统可同时支持单个或多个停机坪的配置,且成本低廉,无需添加机载设备成本。最后对该系统进行仿真验证和实飞测试,表明所提出算法能够有效地实现无人机全自主降落。     

直线电机地铁车辆牵引系统国产化应用研究

为解决国内直线电机(LIM)轨道交通牵引传动系统关键技术及备品备件完全依赖进口、维修维护困难、车辆运营成本高的现状,研究开发了具有完全自主知识产权的大功率强迫风冷式LIM牵引变流器系统。针对LIM特殊结构引起的边端效应等问题对传统间接磁场定向矢量控制方式进行了优化。考虑到LIM次级时间常数发生变化对磁场定向的影响,导致转矩控制性能下降,提出了一种基于最小电流的参数在线辨识方法。所提方法计算简单、不依赖于电机其他参数,试验台测试结果证明了该算法的准确性和有效性。在地铁实际运营线路的试验结果表明,国产化牵引变流器各项性能满足车辆技术要求,而且效率等部分指标优于原车进口牵引系。     

Adaptive robust cubature Kalman filtering for satellite attitude estimation

This paper is concerned with the adaptive robust cubature Kalman filtering problem for the case that the dynamics model error and the measurement model error exist simultaneously in the satellite attitude estimation system. By using Hubel-based robust filtering methodology to correct the measurement covariance formulation of cubature Kalman filter, the proposed filtering algorithm could effectively suppress the measurement model error. To further enhance this effect and reduce the impact of the dynamics model error, two different adaptively robust filtering algorithms, one with the optimal adaptive factor based on the estimated covariance matrix of the predicted residuals and the other with multiple fading factors based on strong tracking algorithm, are developed and applied for the satellite attitude estimation. The quaternion is employed to represent the global attitude parameter, and three-dimensional generalized Rodrigues parameters are introduced to define the local attitude error. A multiplicative quaternion error is derived from the local attitude error to maintain quaternion normalization constraint in the filter. Simulation results indicate that the proposed novel algorithm could exhibit higher accuracy and faster convergence compared with the multiplicative extended Kalman filter, the unscented quaternion estimator, and the adaptive robust unscented Kalman filter.