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Cooperative Geolocation Using Uavs With Gimballing Camera Sensors With Extensions For Communication Loss And Sensor Bias Estimation

机译：使用无人机与万向节式摄像机传感器进行协作性地理位置定位，并扩展通信损耗和传感器偏差估计

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摘要

This dissertation considers the geolocation of a point of interest (POI), i.e., determining the location of a POI in the world, using multiple cooperating uninhabited aerial vehicles (UAVs) with gimballing camera sensors. A square root sigma point information filter (SR-SPIF) is developed to provide a probabilistic estimate of the POI location. The SR-SPIF utilizes the UAV's onboard navigation system to save computation and also takes important properties for numerical accuracy (square root), tracking accuracy (sigma points), and fusion ability (information). The SR-SPIF is general and scales well to any tracking problem with multiple, moving sensors. In the development of the SR-SPIF, the errors in the navigation system output are assumed to be zero mean. However, in the practical application, there are non zero mean errors (biases), which degrade geolocation accuracy. Therefore, a decentralized approach to simultaneously estimate the biases on each UAV and the unknown POI location is developed. The new decentralized bias estimation approach provides accurate geolocation in spite of sensor biases and further scales well with the number of UAVs. Communication is an important part of a cooperative geolocation mission and in practice communication losses and delays are inevitable. Therefore, a new method for cooperative geolocation in the presence of communication loss, termed the predicted information method, is developed from a separable formulation of the extended information filter. The predicted information method is shown to give the exact solution for linear systems when the measurement dynamics are constant or known by all UAVs. In addition to theoretical developments, extensive experimental flight tests with ScanEagle UAVs have been performed. The experimental flight tests serve two purposes: 1) to develop practical guidelines for geolocation 2) to validate all of the new approaches presented in this dissertation. In addition to the flight tests, a high fidelity, distributed, hardware in the loop simulation test bed was developed and used as further validation of all new approaches.
机译：本文考虑了兴趣点（POI）的地理位置，即使用多个带有无环摄影机传感器的协作无人飞行器（UAV）确定POI在世界上的位置。平方根西格玛点信息滤波器（SR-SPIF）被开发来提供POI位置的概率估计。 SR-SPIF利用无人机的机载导航系统来节省计算，并具有数值精度（平方根），跟踪精度（σ点）和融合能力（信息）的重要属性。 SR-SPIF具有通用性，可以很好地扩展到多个移动传感器的任何跟踪问题。在SR-SPIF的开发中，导航系统输出中的误差被假定为零均值。但是，在实际应用中，存在非零均值误差（偏差），这会降低地理位置精度。因此，开发了一种分散方法来同时估计每个无人机和未知POI位置上的偏差。尽管传感器存在偏差，但新的分散偏差估计方法仍可提供准确的地理位置，并且可以随着无人飞行器的数量进一步扩展。交流是合作地理定位任务的重要组成部分，实际上，交流的损失和延误是不可避免的。因此，根据扩展信息过滤器的可分离公式，开发了一种在通信丢失的情况下进行协作地理定位的新方法，称为预测信息方法。当测量动力学是恒定的或所有UAV都知道时，预测的信息方法可以为线性系统提供精确的解决方案。除了理论上的发展外，还使用ScanEagle无人机进行了广泛的实验飞行测试。实验飞行测试有两个目的：1）制定地理定位实用指南2）验证本文提出的所有新方法。除飞行测试外，还开发了环路仿真测试台中的高保真，分布式硬件，并将其用作所有新方法的进一步验证。

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    Whitacre William;

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  • 年度 2010
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