UAV Position Estimation and Collision Avoidance Using the Extended Kalman Filter
UAV Position Estimation and Collision Avoidance Using the Extended Kalman Filter
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DOI：
10.1109/tvt.2013.2243480
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发表时间：
2013-07-01
影响因子：
6.8
通讯作者：
De Nardi, Renzo
中科院分区：
文献类型：
--
作者：
Luo, Chunbo;McClean, Sally I.;De Nardi, Renzo
无人机在信息收集和数据融合方面发挥着不可估量的作用。由于它们的移动性和部署环境的复杂性，持续的位置感知和碰撞避免至关重要。如果无人机的全球定位系统（GPS）信号很弱或不可用，则无人机可能遇到和/或造成危险。研究了利用机载通信模块接收信号强度（RSS）对无人机在野外搜索场景中进行恒定定位和避碰的问题。在RSS中发现有色噪声，这使得在基于RSS的位置估计中广泛使用的最小二乘（LS）算法中的无偏假设无效。提出了一种色噪声模型，并将其应用于扩展卡尔曼滤波（EKF）距离估计中。此外，在无人机飞行过程中不断变化的路径损耗因子也会影响估计的准确性。为了克服这一挑战，我们提出了一种自适应算法来估计路径损耗因子。给定位置和速度信息，如果检测到碰撞，我们进一步采用正交规则来调整UAV预定义的轨迹。理论结果表明，这种算法可以提供有效的修改，以满足所需的性能。实验结果证实了所提算法的优越性。
Unmanned aerial vehicles (UAVs) play an invaluable role in information collection and data fusion. Because of their mobility and the complexity of deployed environments, constant position awareness and collision avoidance are essential. UAVs may encounter and/or cause danger if their Global Positioning System (GPS) signal is weak or unavailable. This paper tackles the problem of constant positioning and collision avoidance on UAVs in outdoor (wildness) search scenarios by using received signal strength (RSS) from the onboard communication module. Colored noise is found in the RSS, which invalidates the unbiased assumptions in least squares (LS) algorithms that are widely used in RSS-based position estimation. A colored noise model is thus proposed and applied in the extended Kalman filter (EKF) for distance estimation. Furthermore, the constantly changing path-loss factor during UAV flight can also affect the accuracy of estimation. To overcome this challenge, we present an adaptive algorithm to estimate the path-loss factor. Given the position and velocity information, if a collision is detected, we further employ an orthogonal rule to adapt the UAV predefined trajectory. Theoretical results prove that such an algorithm can provide effective modification to satisfy the required performance. Experiments have confirmed the advantages of the proposed algorithms.