【摘要】 随着物联网与智能移动设备的普及,对移动设备定位与追踪的需求日益增加。然而,现有研究大多集中于静态信号源场景,定位方法依赖一个或者多个专用设备来测量到达时间(ToA)、到达角度(AoA)等指示源方向的信息,存在体积、重量和功耗方面的限制,难以直接应用于无人机移动信号源追踪场景。为此,本文提出利用无人机且仅依赖信号强度信息进行移动信号源追踪。针对信号源移动导致的信号强度分布与信号源位置的关系未知、受到噪声影响且没有源方向信息的挑战,本文设计了一种面向移动信号源追踪的高斯过程时空建模与无人机在线飞行规划算法。该方法将高斯过程扩展到时空高维场景,通过引入时间维度实现对移动信号源时空分布的准确建模。具体而言,无人机首先通过自身搭载的传感器获取信号源的强度信息,并实时建模该移动信号的强度分布,以准确预测信号源的位置变化。进一步地,设计了多阶段自适应在线飞行规划算法,将追踪任务分为探索阶段和追踪阶段,该算法通过IVR和LW评估函数的动态切换机制,有效解决了充分探索与尽早追踪的平衡问题。该方法通过反复迭代时空建模与路径规划两个步骤,系统地优化飞行路径,使得无人机能够在预定步数内追踪至信号源,完成追踪任务。最后,本文构建递进式验证框架,通过大量仿真实验和真机实验验证了提出的无人机移动信号源追踪算法的有效性。更多还原

【Abstract】 With the widespread adoption of Internet of Things(IoT) and intelligent mobile devices, the demand for mobile device positioning and tracking has been growing significantly, particularly in critical application domains such as search and rescue, environmental monitoring, and security surveillance. However, existing research predominantly focuses on static signal source scenarios, where positioning methods typically rely on one or multiple dedicated devices to measure directional indicators such as Time of Arrival(ToA) and Angle of Arrival(AoA). These devices present significant limitations in terms of size, weight, and power consumption, making them difficult to directly apply to mobile signal source tracking scenarios. To address these limitations, this paper proposes a novel approach that utilizes unmanned aerial vehicles(UAVs) relying solely on Received Signal Strength Indicator(RSSI) for mobile signal source tracking. Given the challenges posed by mobile signal sources—including the unknown relationship between signal strength distribution and signal source position due to source mobility, measurement noise interference, and the absence of directional information, this paper designs a comprehensive solution comprising two key components: a Gaussian process spatiotemporal modeling framework and an online flight planning algorithm specifically tailored for mobile signal source tracking. The proposed method represents a significant innovation by extending Gaussian process applications to spatiotemporal high-dimensional scenarios. This extension is achieved by introducing the temporal dimension, which enables accurate modeling of the spatiotemporal distribution of mobile signal sources. The method operates through a systematic process. Initially, the UAV utilizes onboard sensors to acquire signal strength information in real-time. Subsequently, the system establishes the spatiotemporal distribution of signal strength based on an improved Gaussian process regression model. This approach enables accurate prediction of changes in signal source position over time. Furthermore, building upon this foundation, the paper presents a multi-stage adaptive online flight planning strategy, which divides the tracking task into two distinct phases: exploration and tracking phases. This strategy incorporates a dynamic switching mechanism between two evaluation functions: Integrated Variance Reduction(IVR) and Likelihood Weighted(LW). Through this mechanism, the system effectively achieves an optimal balance between global exploration and local precise positioning. This design effectively addresses the fundamental trade-off between sufficient exploration and timely tracking convergence. Subsequently, the system operates through iterative execution of spatiotemporal modeling and path planning steps, systematically optimizing the flight path at each iteration. This iterative approach ensures that the UAV can successfully track and reach the mobile signal source within a predetermined number of steps. To validate the proposed approach, this paper establishes a comprehensive progressive validation framework. The framework encompasses three distinct levels: theoretical validation, parameter optimization, and practical application. The first level involves extensive simulation experiments that verify the effectiveness of each algorithmic module individually. The second level focuses on optimizing system parameters for enhanced performance. Finally, the third level convincingly demonstrates practical viability through real-world experiments using actual UAV platforms. Through extensive simulation experiments, the effectiveness of each algorithmic module is verified, and the effectiveness of the proposed UAV-based mobile signal source tracking algorithm is validated through real-world experiments.更多还原