火星进入点误差对开伞点分布影响分析

随着火星探测任务需求的提升,着陆器在火星表面着陆精度的要求越来越高。着陆器开伞点分布是影响着陆精度的重要因素。文章围绕火星大气进入过程,简要介绍了着陆器运动学方程,并给出相应的数学模型。针对进入点（接触大气层）存在的初始状态误差,借助MonteCarlo法进行了着陆器开伞点分布情况分析。通过1000次重复仿真试验得出结论,着陆器开伞点的纵向误差在20~40km,横向误差在5~10km。最后,针对提高开伞点精度,提出两点建议并简要介绍了相关制导算法。     

基于改进动静态滤波的脉冲星/CNS深空探测组合导航方法

为了提高深空探测器的自主导航系统性能,文章提出一种基于改进动静态滤波的脉冲星/CNS组合导航方法。脉冲星观测数据和星光角距信息通过改进动静态滤波器进行信息融合。其中,动态滤波采用UKF处理采样速率快、测量方程非线性强的星光角距测量量,静态滤波采用EKF处理采样速率慢、测量方程线性特征明显的脉冲星测量量,其运行周期可以是动态滤波的若干倍。利用该改进动静态滤波进行组合导航,可避免联邦滤波器中由于各局部滤波器采用相同的状态方程而导致的融合滤波结果不具备最优性的问题。仿真分析表明,基于所提出的组合导航系统,深空探测器的导航精度可提高至10km以内。     

基于挖掘性学习和探索性学习相结合的管理学原理教学方法研究

在分析高校《管理学原理》教学现状及存在问题的基础上,本研究借鉴挖掘性学习和探索性学习理论并结合长期教学实践,提出了基于挖掘性学习和探索性学习相结合的《管理学原理》教学方法及改革思路。     

网络环境下大学英语自主学习监控体系的构建

网络环境下的大学英语自主学习是大学英语教学改革的一项重要内容。为保障自主学习效果,本文提出构建多层次监控体系,即教师对学生的监控、软件对学生的监控、学生间相互监控和学生自我监控,并阐述监控效果及可能的发展方向。     

基于深度图推理的卫星背板部件检测方法

在轨加注是一种典型的在轨服务操作,它对于降低空间运输成本和任务风险起着重要的作用,视觉感知系统可以感知操作任务周围环境并提供给控制系统。目前在轨加注依赖于人,在人员监控下完成或通过遥操作完成,缺乏自主性。本文围绕未来高自主性的基于深度强化学习的在轨加注方法,对基于深度学习的视觉感知方法展开了研究,针对基于深度学习的方法对相似实例的检测存在精确率低、对光照变化敏感等缺点,提出了基于深度图推理的卫星背板部件检测方法。提出的方法可以有效地检测复杂形状的目标,不依赖于手工设计的特征;提高了复杂光照环境下部件的检测正确率;可以有效区分外形相似的不同部件;其有效性在数学仿真和物理仿真中均得到了验证。     

一种基于天文测速的火星捕获段天地联合导航方法

为提高火星捕获段探测器的导航精度,提出一种基于天文测速的天地联合导航方法。该方法在地面无线电测距、测速的基础上,引入探测器与恒星的视向速度作为新增观测量,采用扩展卡尔曼滤波(EKF)对探测器状态进行估计。理论分析与仿真结果均表明,与仅依靠地面无线电导航相比,采用基于天文测速的天地联合观测导航方法能有效提高探测器的位置与速度估计精度,且导航精度的提升效果与马尔柯夫最优估计理论的预测值有较好的吻合度。当天文测速精度与地面测速精度相当时,位置估计精度较地面无线电导航提高了近一倍。     

启发式深空探测器任务规划方法

针对深空探测器的任务规划过程特点,考虑约束的数值特性,提出一种基于规划活动相关度的领域无关启发式规划方法。定义了活动的相关度,结合规划过程,给出自学习式的相关度动态更新过程。设计基于相关度的活动选择机制,形成启发式规划算法。数值计算结果表明,文中提出的方法有效地减少了规划过程中规划步数和回溯步数,提高了深空探测器规划的效率。     

Synchronized perturbation elimination and DOA estimation via signal selection mechanism and parallel deep capsule networks in multipath environment

State-of-the-art model-driven Direction-Of-Arrival (DOA) estimation methods for multipath signals face great challenges in practical application because of the dependence on the precise multipath model. In this paper, we introduce a framework, based on deep learning, for synchronizing perturbation auto-elimination with effective DOA estimation in multipath environment. Firstly, a signal selection mechanism is introduced to roughly locate specific signals to spatial subregion via frequency domain filters and compressive sensing-based method. Then, we set the mean of the correlation matrix’s row vectors as the input feature to construct the spatial spectrum by the corresponding single network within the parallel deep capsule networks. The proposed method enhances the generalization capability to untrained scenarios and the adaptability to non-ideal conditions, e.g., lower SNRs, smaller snapshots, unknown reflection coefficients and perturbational steering vectors, which make up for the defects of the previous model-driven methods. Simulations are carried out to demonstrate the superiority of the proposed method.     

Ground maneuver for front-wheel drive aircraft via deep reinforcement learning

The maneuvering time on the ground accounts for 10%–30% of their flight time, and it always exceeds 50% for short-haul aircraft when the ground traffic is congested. Aircraft also contribute significantly to emissions, fuel burn, and noise when taxiing on the ground at airports. There is an urgent need to reduce aircraft taxiing time on the ground. However, it is too expensive for airports and aircraft carriers to build and maintain more runways, and it is space-limited to tow the aircraft fast using tractors. Autonomous drive capability is currently the best solution for aircraft, which can save the maneuver time for aircraft. An idea is proposed that the wheels are driven by APU-powered (auxiliary power unit) motors, APU is working on its efficient point; consequently, the emissions, fuel burn, and noise will be reduced significantly. For Front-wheel drive aircraft, the front wheel must provide longitudinal force to tow the plane forward and lateral force to help the aircraft make a turn. Forward traction effects the aircraft’s maximum turning ability, which is difficult to be modeled to guide the controller design. Deep reinforcement learning provides a powerful tool to help us design controllers for black-box models; however, the models of related works are always simplified, fixed, or not easily modified, but that is what we care about most. Only with complex models can the trained controller be intelligent. High-fidelity models that can easily modified are necessary for aircraft ground maneuver controller design. This paper focuses on the maneuvering problem of front-wheel drive aircraft, a high-fidelity aircraft taxiing dynamic model is established, including the 6-DOF airframe, landing gears, and nonlinear tire force model. A deep reinforcement learning based controller was designed to improve the maneuver performance of front-wheel drive aircraft. It is proved that in some conditions, the DRL based controller outperformed conventional look-ahead controllers.