Experimental Study of Outdoor UAV Localization and Tracking using Passive RF Sensing
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Extensive use of unmanned aerial vehicles (UAVs) is expected to raise privacy and security concerns among individuals and communities. In this context, detection and localization of UAVs will be critical for maintaining safe and secure airspace in the future. In this work, Keysight N6854A radio frequency (RF) sensors are used to detect and locate a UAV by passively monitoring the signals emitted from the UAV. First, the Keysight sensor detects the UAV by comparing the received RF signature with various other UAVs’ RF signatures in the Keysight database using an envelope detection algorithm. Afterward, time difference of arrival (TDoA) based localization is performed by a central controller using the sensor data, and the drone is localized with some error. To mitigate the localization error, implementation of an extended Kalman filter (EKF) is proposed in this study. The performance of the proposed approach is evaluated on a realistic experimental dataset. EKF requires basic assumptions on the type of motion throughout the trajectory, i.e., the movement of the object is assumed to fit some motion model (MM) such as constant velocity (CV), constant acceleration (CA), and constant turn (CT). In the experiments, an arbitrary trajectory is followed, therefore it is not feasible to fit the whole trajectory into a single MM. Consequently, the trajectory is segmented into sub-parts and a different MM is assumed in each segment while building the EKF model. Simulation results demonstrate an improvement in error statistics when EKF is used if the MM assumption aligns with the real motion.
预计无人机（Unmanned Aerial Vehicles, UAVs）的大规模部署将引发个人与社群的隐私及安全顾虑。在此背景下，无人机的检测与定位技术对于未来保障空域安全至关重要。本研究采用是德科技（Keysight）N6854A型射频（Radio Frequency, RF）传感器，通过被动监测无人机发射的射频信号实现无人机的检测与定位。首先，该传感器将接收到的无人机射频特征与自身数据库中各类无人机的射频特征进行比对，借助包络检测算法完成无人机检测。随后，中央控制器利用传感器数据执行基于到达时间差（Time Difference of Arrival, TDoA）的定位流程，得到存在一定误差的无人机位置估计结果。 为缓解该定位误差，本研究提出采用扩展卡尔曼滤波（Extended Kalman Filter, EKF）进行优化。所提方法的性能通过真实实验数据集开展验证。扩展卡尔曼滤波需要对运动轨迹的运动形式做出基本假设，即假定目标运动符合某类运动模型（Motion Model, MM），例如匀速（Constant Velocity, CV）模型、匀加速（Constant Acceleration, CA）模型以及恒转弯（Constant Turn, CT）模型。由于实验中无人机沿任意轨迹飞行，无法将整条轨迹适配至单一运动模型，因此将轨迹划分为若干子段，并在构建扩展卡尔曼滤波模型时，为每个子段分配不同的运动模型假设。仿真结果表明，当运动模型假设与目标实际运动情况相符时，采用扩展卡尔曼滤波可有效改善误差统计性能。
