Energy-efficient data collection for UAV-assisted IoT: Joint trajectory and resource optimization

Internet of Things (IoT) can be conveniently deployed while empowering various applications, where the IoT nodes can form clusters to finish certain missions collectively. As energy-efficient operations are critical to prolong the lifetime of the energy-constrained IoT devices, the Unmanned Aerial Vehicle (UAV) can be dispatched to geographically approach the IoT clusters towards energy-efficient IoT transmissions. This paper intends to maximize the system energy efficiency by considering both the IoT transmission energy and UAV propulsion energy, where the UAV trajectory and IoT communication resources are jointly optimized. By applying large-system analysis and Dinkelbach method, the original fractional optimization is approximated and reformulated in the form of subtraction, and further a block coordinate descent framework is employed to update the UAV trajectory and IoT communication resources iteratively. Extensive simulation results are provided to corroborate the effectiveness of the proposed method.     

STABC-IR: An air target intention recognition method based on bidirectional gated recurrent unit and conditional random field with space-time attention mechanism

The battlefield environment is changing rapidly, and fast and accurate identification of the tactical intention of enemy targets is an important condition for gaining a decision-making advantage. The current Intention Recognition (IR) method for air targets has shortcomings in temporality, interpretability and back-and-forth dependency of intentions. To address these problems, this paper designs a novel air target intention recognition method named STABC-IR, which is based on Bidirectional Gated Recurrent Unit (BiGRU) and Conditional Random Field (CRF) with Space-Time Attention mechanism (STA). First, the problem of intention recognition of air targets is described and analyzed in detail. Then, a temporal network based on BiGRU is constructed to achieve the temporal requirement. Subsequently, STA is proposed to focus on the key parts of the features and timing information to meet certain interpretability requirements while strengthening the timing requirements. Finally, an intention transformation network based on CRF is proposed to solve the back-and-forth dependency and transformation problem by jointly modeling the tactical intention of the target at each moment. The experimental results show that the recognition accuracy of the jointly trained STABC-IR model can reach 95.7%, which is higher than other latest intention recognition methods. STABC-IR solves the problem of intention transformation for the first time and considers both temporality and interpretability, which is important for improving the tactical intention recognition capability and has reference value for the construction of command and control auxiliary decision-making system.     

未知纬度条件下基于空间圆拟合的SINS初始对准方法

传统的捷联惯导系统(SINS)晃动基座初始对准算法,如积分双矢量方法和多矢量Wahba方法等均需要精确的纬度信息,粗对准偏航角误差较大.针对此特点,提出了一种未知纬度条件下基于空间圆拟合的SINS初始对准方法.根据重力矢量在惯性系中绕地轴旋转包含北向信息的特征,以初始时刻凝固载体坐标系作为惯性系,首先对重力矢量在凝固载体系内进行投影并滤波,对其矢量端点进行空间圆拟合,然后通过三角几何关系得到导航坐标系,从而完成对准过程.通过仿真验证,证实了该对准方法不需要已知对准点的精确位置信息,相比于积分双矢量方法与多矢量Wahba方法,偏航角对准精度分别提高了12.37'与5.10'.     

火箭飞行异常遥测天线实时跟踪角度预测研究

针对火箭飞行异常下的遥测天线继续实时跟踪难的问题,提出一种飞行异常后的实时跟踪角度的预测方法.首先介绍几种常用坐标系以及坐标系之间的转换关系,并将火箭飞行异常后的运动简化为考虑惯性速度的自由落体运动,给出自由落体速度模型和实时跟踪角度的计算方法步骤.设计仿真实验,验证方法的有效性,可以作为火箭飞行异常后遥测天线实时跟踪...