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[转载]【无人机】【2008.09】用于小型无人机目标定位的轨迹优化

已有 191 次阅读 2021-7-24 23:17 |系统分类:科研笔记|文章来源:转载

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本文为美国MIT(作者:Sameera S. Ponda)的硕士论文,共197页。

 

配备导航系统和视频功能的小型无人机目前正被部署用于情报、侦察和监视任务。其中一个特别的任务是计算机载传感器检测到的目标位置估计。将无人机的状态估计与成像传感器收集的信息相结合,可以得到目标的方位测量值,用于确定目标的位置。这种三维纯方位估计问题是非线性的,传统的滤波方法会产生有偏和不确定的估计,有时会导致滤波不稳定。仔细选择测量位置可以大大提高滤波器的性能,使目标位置估计的误差最小化,提高滤波器的收敛性。

 

这项工作的目标是开发引导算法,使无人机能够按照轨迹飞行,增加测量提供的信息量,提高整体估计的可观测性,从而实现正确的目标跟踪和准确的目标位置估计。目标估计的性能取决于相对于目标和先前测量的位置。过去的研究已经提供了使用Fisher信息矩阵(FIM)量化一组信息含量的方法。在建立目标函数的基础上,通过职能指令手册和数值优化方法产生无人机轨迹,以局部最大化FIM的信息量。在这个项目中,轨迹优化使得无人机飞行路径得以发展,提供了关于目标参数最高数量的信息,同时考虑了传感器限制、无人机动力学和操作约束。针对飞行器运动约束的多种不同场景,对静止目标、动态目标和多目标进行了轨迹优化。由此产生的轨迹显示了无人机所采取的螺旋路径,其重点在于增加测量之间的角度间隔,减小与目标之间的相对距离,从而最大限度地利用每个测量提供的信息,提高估计性能。

 

基于信息的轨迹设计的主要缺点是Fisher信息矩阵依赖于目标的真实位置。本项目采用同时进行目标位置估计与无人机轨迹最优化来解决此问题。考虑了扩展卡尔曼滤波和粒子滤波两种估计算法,用目标估计的均值代替真实目标位置进行轨迹优化。估计和优化算法按顺序运行并实时更新。研究结果表明,螺旋型无人机的轨迹可以提高滤波收敛性和总体估计精度,说明了基于信息的轨迹设计在小型无人机目标定位中的重要性。

 

Small unmanned aerial vehicles (UAVs),equipped with navigation systems and video capability, are currently beingdeployed for intelligence, reconnaissance and surveillance missions. Oneparticular mission of interest involves computing location estimates fortargets detected by onboard sensors. Combining UAV state estimates withinformation gathered by the imaging sensors leads to bearing measurements ofthe target that can be used to determine the target’s location. This 3-Dbearings-only estimation problem is nonlinear and traditional filtering methodsproduce biased and uncertain estimates, occasionally leading to filter instabilities.Careful selection of the measurement locations greatly enhances filterperformance, motivating the development of UAV trajectories that minimizetarget location estimation error and improve filter convergence. The objectiveof this work is to develop guidance algorithms that enable the UAV to flytrajectories that increase the amount of information provided by themeasurements and improve overall estimation observability, resulting in propertarget tracking and an accurate target location estimate. The performance ofthe target estimation is dependent upon the positions from which measurementsare taken relative to the target and to previous measurements. Past researchhas provided methods to quantify the information content of a set of measurementsusing the Fisher Information Matrix (FIM). Forming objective functions based onthe FIM and using numerical optimization methods produce UAV trajectories thatlocally maximize the information content for a given number of measurements. Inthis project, trajectory optimization leads to the development of UAV flightpaths that provide the highest amount of information about the target, whileconsidering sensor restrictions, vehicle dynamics and operation constraints.The UAV trajectory optimization is performed for stationary targets, dynamictargets and multiple targets, for many different scenarios of vehicle motionconstraints. The resulting trajectories show spiral paths taken by the UAV,which focus on increasing the angular separation between measurements andreducing the relative range to the target, thus maximizing the informationprovided by each measurement and improving the performance of the estimation.

The main drawback of information basedtrajectory design is the dependence of the Fisher Information Matrix on thetrue target location. This issue is addressed in this project by executingsimultaneous target location estimation and UAV trajectory optimization. Twoestimation algorithms, the Extended Kalman Filter and the Particle Filter areconsidered, and the trajectory optimization is performed using the mean valueof the target estimation in lieu of the true target location. The estimationand optimization algorithms run in sequence and are updated in real-time. Theresults show spiral UAV trajectories that increase filter convergence andoverall estimation accuracy, illustrating the importance of information-basedtrajectory design for target localization using small UAVs.

 

1.  引言

2. 目标定位估计

3. 无人机轨迹优化

4. 用于随机目标的无人机轨迹优化

5. 结论

附录A Cramer-Rao下界和Fisher信息矩阵的推导

附录粒子滤波算法


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