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首页> 外文学位 >Probabilistic identification and discrimination of deep space objects via astrometric and photometric data fusion.
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Probabilistic identification and discrimination of deep space objects via astrometric and photometric data fusion.

机译：通过天体和光度数据融合对深空物体进行概率识别和鉴别。

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摘要

In this dissertation two problems are studied in detail; shape estimation and space object classification. The progress made towards addressing these space situational awareness problems is discussed in this dissertation and simulation results are shown. The feasibility of the proposal approaches is shown. Additional areas of research are discussed, including generalized shape parameters estimation, inertia estimation, and mass estimation.;The main focus of this dissertation presents a new method, based on a multiple-model adaptive estimation approach, to determine the most probable attribute, such as shape, of a resident space object in orbit among a number of candidate attribute models while simultaneously recovering the observed resident space object's inertial orientation and trajectory. Multiple-model adaptive estimation uses a parallel bank of filters to provide multiple resident space object state estimates, where each filter is purposefully dependent on a mutually unique resident space object model. Estimates on the conditional probability of each model given the available measurements are provided from the multiple-model adaptive estimation approach. The multiple-model adaptive estimation state estimates are determined using a weighted sum of the individual models, weighted by model probabilities, whereas the shape estimates are determined from the model with the highest probability. Each filter employs the unscented estimation approach, reducing passively-collected electro-optical data to infer the unknown state vector comprised of the resident space object inertial-to-body orientation, position and respective temporal rates. Each hypothesized model results in a different observed optical cross-sectional area. The effect of solar radiation pressure may be recovered from accurate angles-data alone, if the collected measurements span a sufficiently long period of time so as to make the non-conservative mismodeling effects noticeable. However, for relatively short data arcs, this effect is weak and thus the temporal brightness of the resident space object can be used in conjunction with the angles data to exploit the fused sensitivity to both resident space object attributes and associated trajectory, the very same ones which drive the non-conservative dynamic effects. Recovering these attributes and trajectories with sufficient accuracy is shown in this dissertation, where the attributes are inherent in unique resident space object models. The performance of this strategy is demonstrated via simulated scenarios.
机译：本文详细研究了两个问题。形状估计和空间物体分类。本文讨论了解决这些空间态势感知问题的进展，并给出了仿真结果。显示了提议方法的可行性。讨论了其他研究领域，包括广义形状参数估计，惯性估计和质量估计。；本论文的主要重点是提出一种基于多模型自适应估计方法来确定最可能属性的新方法，例如作为形状，在多个候选属性模型中在轨道上的一个居住空间物体，同时恢复观察到的居住空间物体的惯性方向和轨迹。多模型自适应估计使用并行的滤波器组来提供多个驻留空间对象状态估计，其中每个滤波器有目的地依赖于相互唯一的驻留空间对象模型。给定可用测量值，可以从多模型自适应估计方法中提供每个模型的条件概率估计。使用各个模型的加权总和（通过模型概率加权）确定多模型自适应估计状态估计，而从概率最大的模型确定形状估计。每个滤波器采用无味估计方法，减少了被动收集的电光数据，以推断出由居住空间物体惯性到人体的方向，位置和各自的时间速率组成的未知状态向量。每个假设的模型都会导致观察到的光学横截面面积不同。如果收集的测量值跨越足够长的时间段，以使非保守的误建模效果显着，则可以仅从准确的角度数据中恢复太阳辐射压力的影响。但是，对于相对较短的数据弧，此效果较弱，因此可以将常驻空间对象的时间亮度与角度数据结合使用，以利用对常驻空间对象属性和关联轨迹（两者非常相同）的融合敏感性从而驱动非保守的动态效果。本文以足够的精度恢复了这些属性和轨迹，其中这些属性是唯一的驻留空间对象模型所固有的。通过模拟场景演示了该策略的性能。

著录项

  • 作者

    Linares, Richard.;

  • 作者单位

    State University of New York at Buffalo.;

  • 授予单位 State University of New York at Buffalo.;
  • 学科 Engineering Aerospace.
  • 学位 Ph.D.
  • 年度 2013
  • 页码 139 p.
  • 总页数 139
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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