Robust State Estimation in Uncertain Environments Using Point Process Models

状态估计的目标是减轻传感器测量中噪声的影响，并使用某些系统和测量模型提取感兴趣对象的固定或时变参数。噪声抑制是必要的，不仅因为没有传感器是完美的，而且因为我们对任何未知系统及其参数的知识或模型假设是不精确的。该估计器考虑模型的不确定性和噪声统计，以最佳估计的参数的一些最优性标准的利益。虽然状态估计通常只考虑系统（或模型）噪声和测量噪声的影响，但在估计移动对象随时间的状态时，目标跟踪考虑由于遗漏检测、误报警和来自其他感兴趣对象的干扰而引起的额外测量源不确定性。在目标跟踪中，状态估计的目标是减轻模型和传感器噪声以及测量源不确定性的影响。** 随着可负担得起的传感器的出现（例如，照相机、声纳浮标、卫星接收器），以状态估计和目标跟踪为目的的传感器处理已经变得普遍。这些传感器的无处不在和负担得起的性质导致了迄今为止在文献中尚未适当解决的额外的不确定性。在使用仅具有一个或少数传感器的昂贵雷达系统的传感器处理中，诸如传感器偏差、杂波、电子对抗和其他干扰的系统误差已经被有效地建模和解决。但是，由于大量的异构传感器可用，这些额外的不确定性来源还没有被建模或最佳解决。这种情况为拟议的工作提供了动力。*具体而言，我们将解决以下问题：1）减轻和利用各种环境条件来改善跟踪结果; 2）在存在严重杂波的情况下对低可观测目标进行检测前跟踪; 3）将状态估计与传感器管理相结合;以及4）借助于不确定的外部数据源（例如，地图、地形数据）。我们的解决方案是基于点过程模型和分析组合学（AC）的形式主义，这提供了一个有效的机制，在大规模的问题与广泛的不确定性。为了提供一个全面的解决方案，我们将模拟传感器内部和外部的各种形式的不确定性，开发强大的算法，以最大限度地减少传感器的努力，并量化新算法的性能使用扩展的AC形式主义。除了推进最先进的技术外，该项目还将在对加拿大至关重要的领域培养一些高素质的人才。

The objective of state estimation is to mitigate the effects of noise in sensor measurements and extract the fixed or time-varying parameters of an object of interest using certain system and measurement models. Noise mitigation is necessary not only because no sensor is perfect, but also because our knowledge or model assumptions about any unknown system and its parameters are imprecise. The estimator considers the model uncertainties and noise statistics in order to optimally estimate the parameters of the subject of interest to some optimality criterion. While state estimation typically considers only the effects of system (or model) noise and measurement noise, in estimating the state of a moving object over time, target tracking considers additional measurement-origin uncertainties due to missing detections, false alarms, and interference from other objects of interest. In target tracking, the objective of state estimation is then to mitigate the effects of model and sensor noise and those of measurement-origin uncertainties. ******With the emergence of affordable sensors (e.g., cameras, sonobuoys, satellite receivers), sensor processing with the objective of state estimation and target tracking has become common. The ubiquitous and affordable nature of these sensors results in additional uncertainties that have not been addressed properly in the literature to date. In sensor processing where expensive radar systems with only one or a handful of sensors are used, systemic errors such as sensor biases, clutter, electronic countermeasures, and other interference have been effectively modeled and addressed. But, given the large number of heterogeneous sensors available, these additional sources of uncertainties have not been modeled or addressed optimally. This situation provides the motivation for the proposed work.******Specifically, we will address the following problems: 1) mitigating and taking advantage of various environmental conditions to improve tracking results; 2) track-before-detect for low-observable targets in the presence of heavy clutter; 3) integration of state estimation with sensor management; and 4) constrained state estimation and prediction with the aid of uncertain external data sources (e.g., maps, terrain data). Our solution methodology is based on Point Process models and the Analytic Combinatorics (AC) formalism, which provide an efficient mechanism for working with a wide range of uncertainties in large-scale problems. To provide a comprehensive solution, we will model various forms of uncertainties that are internal and external to sensors, develop robust algorithms to minimize the efforts of sensors, and quantify the performance of the new algorithms using extensions to the AC formalism. In addition to advancing the state-of-the-art, the project will also produce a number of highly qualified personnel in areas of critical importance to Canada.