星地激光时间比对在卫星导航系统中的应用

星地精密激光时间比对具有精度高、系统误差少等优点,因此利用星地精密激光时间比对,不但可以对无线电时间比对进行外部精度检验,而且可以检验并分离无线电伪距测量的系统误差,分析检测设备时延的不稳定性和卫星钟的中短期性能指标,提高卫星钟差的预报精度。     

电离层闪烁对导航接收机性能的影响

为定量分析电离层闪烁对接收机环路的影响,利用信号在电离层闪烁影响下的等效传输信道模型,通过数值仿真对比分析了电离层闪烁对接收信号幅度、载波相位、码相位、载噪比和I/Q支路跟踪值的性能影响。结果表明：电离层闪烁会使接收载噪比出现深度的快速衰落,严重时会出现高达5dB的突降,从而显著地影响用户的定位性能;在相同的电离层闪烁强度下,较小的环路带宽会发生较弱的载波相位周跳。     

基于射频的导航卫星星间链路信息传输与仿真研究

星间链路是导航卫星实现精密定轨和自主导航的关键技术之一。导航卫星通过星间链路完成伪距测量和数据交换,维持系统稳定运行的时空基准,保证系统持续提供精准导航服务。根据全球导航卫星系统的建设情况和发展趋势,首先介绍星间观测和信息传输频段,并从天线特征、多址控制方式和网络拓扑结构等角度分析了射频链路的工作体制。最后,针对实际导航卫星星座,应用OPNET平台建立导航信息传输仿真模型,通过分析信息传输效率,验证了基于射频链路导航信息传输的可行性和有效性,对全球导航卫星系统的星间链路研究具有一定的参考价值。     

指标测试在导航雷达检修维护中的应用

通过研究船用导航雷达系统结构及各功能模块作用,对影响导航雷达正常运行的关键技术指标进行了详细的分析。提出在船用导航雷达的检修维护工作中,利用无线电计量设备测试关键技术指标,从而对设备状态进行准确评估的方法。通过实践证明,该方法准确可靠,对设备的检修维护工作提供了良好的参考。     

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.     

单兵导航多传感器信息融合自适应滤波算法

基于足绑式INS的单兵导航系统,通过将惯性导航系统、人体运动学约束、磁强计等多传感器信息进行融合得到准确的单兵导航信息。对于匀速步行时的单兵导航,可采用普通Kalman滤波算法进行多传感器信息融合,但不能满足跑步等激烈运动模式下单兵导航需求。提出衰减记忆新息自适应Kalman滤波算法,可满足多步态模式下的单兵导航多源信息融合的需求,实验结果验证了算法的有效性。     

Optimal Earth’s reentry disposal of the Galileo constellation

Nowadays there is international consensus that space activities must be managed to minimize debris generation and risk. The paper presents a method for the end-of-life (EoL) disposal of spacecraft in Medium Earth Orbit (MEO). The problem is formulated as a multiobjective optimisation one, which is solved with an evolutionary algorithm. An impulsive manoeuvre is optimised to reenter the spacecraft in Earth’s atmosphere within 100?years. Pareto optimal solutions are obtained using the manoeuvre $\mathrm{\Delta }v$ and the time-to-reentry as objective functions to be minimised. To explore at the best the search space a semi-analytical orbit propagator, which can propagate an orbit for 100?years in few seconds, is adopted. An in-depth analysis of the results is carried out to understand the conditions leading to a fast reentry with minimum propellant. For this aim a new way of representing the disposal solutions is introduced. With a single 2D plot we are able to fully describe the time evolution of all the relevant orbital parameters as well as identify the conditions that enables the eccentricity build-up. The EoL disposal of the Galileo constellation is used as test case.     

MBOC调制及Simulink仿真

MBOC（6,1,1／11）是Galileo和GPS系统的互操作导航信号调制方式。文章在介绍MBOC调制实现方式的基础上,对MBOC调制进行Simulink建模并进行实时动态仿真,仿真得到的功率谱和相关函数表明MBOC调制信号有着比BOC（1,1）调制更好的性能,并且能够实现GPS和Galileo系统的兼容与互操作。     

UAV navigation in high dynamic environments: A deep reinforcement learning approach

Unmanned Aerial Vehicle (UAV) navigation is aimed at guiding a UAV to the desired destinations along a collision-free and efficient path without human interventions, and it plays a crucial role in autonomous missions in harsh environments. The recently emerging Deep Reinforcement Learning (DRL) methods have shown promise for addressing the UAV navigation problem, but most of these methods cannot converge due to the massive amounts of interactive data when a UAV is navigating in high dynamic environments, where there are numerous obstacles moving fast. In this work, we propose an improved DRL-based method to tackle these fundamental limitations. To be specific, we develop a distributed DRL framework to decompose the UAV navigation task into two simpler sub-tasks, each of which is solved through the designed Long Short-Term Memory (LSTM) based DRL network by using only part of the interactive data. Furthermore, a clipped DRL loss function is proposed to closely stack the two sub-solutions into one integral for the UAV navigation problem. Extensive simulation results are provided to corroborate the superiority of the proposed method in terms of the convergence and effectiveness compared with those of the state-of-the-art DRL methods.     

考虑钟差修正的X射线脉冲星导航算法研究

基于X射线脉冲星的航天器自主导航依赖于精确时间的测量,因此星载时钟 钟差对导航性能有不容忽视的影响。在原子钟钟差模型和航天器动力学模型基础上,提 出一种考虑星载时钟钟差修正的脉冲星导航算法。仿真结果表明,该算法可以有效消除钟差 的影响,保证了导航精度,研究结果对脉冲星导航的工程应用有一定的理论参考价值。