无人机路径优化,用于基于贝叶斯的费舍尔信息矩阵的只有角度的自我定位和目标跟踪
Kutluyil Dogancay1, Hatem Hmam2
1UniSA STEM, University of South Australia, Mawson Lakes Campus, Mawson Lakes, SA 5095, Australia.
新的算法优化无人机 (UAV) 路径,用于自定位和目标跟踪,使用地标轴承和到达角度数据. 当全球导航卫星系统 (GNSS) 无法使用时,这种方法可以提高导航准确性.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 导航和指导的导航和指导.
- 信号处理 信号处理
背景情况:
- 全球导航卫星系统 (GNSS) 的不可靠性需要替代方法来实现无人机导航.
- 准确的自我定位和目标跟踪对于无人机任务至关重要,特别是在GNSS被拒绝的环境中.
- 定向估计对于精确的无人机自我定位至关重要,因为错误可以显著降低性能.
研究的目的:
- 开发用于无人机自我定位和目标跟踪的新路径优化算法.
- 集成信标轴承和到达角度 (AOA) 测量,以增强导航.
- 共同估计无人机的方向和位置,以提高准确性.
主要方法:
- 制定了联合自我定位和目标跟踪作为一个加增状态向量的卡尔曼过问题.
- 在卡尔曼波器框架内利用信标轴承和目标AOA测量.
- 在路径规划中使用贝叶斯学费舍尔信息矩阵优化 (A和D最佳性标准).
- 提出了一个修改后的闭式投影算法,用于最佳的无人机路径确定.
主要成果:
- 通过广泛的模拟开发并验证了新的无人机路径优化算法.
- 证明了联合状态估计 (位置和方向) 的有效性,以改善自我定位.
- 在各种测量噪声水平中评估算法性能.
- 在没有GNSS可用性的场景中展示了拟议方法的实用性.
结论:
- 开发的算法提供了强大的无人机自我定位和目标跟踪解决方案.
- 最佳路径规划显著提高了导航准确性,特别是在GNSS挑战的环境中.
- 联合估计方法有效地减轻了定向错误,提高了整体本地化性能.
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