Robust State Estimation in Uncertain Environments Using Point Process Models

使用点过程模型在不确定环境中进行鲁棒状态估计

• 批准号: RGPIN-2017-05365
• 负责人: Kirubarajan, Thia
• 金额: $ 4.23万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=711621
• 关键词: Robust; State; Estimation; Uncertain; Environments

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.

状态估计的目标是减轻传感器测量中的噪声影响，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。降噪是必要的，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都是不精确的。估计器考虑模型不确定性和噪声统计，以便根据某些最优性标准最优地估计感兴趣主题的参数。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，但在估计移动物体随时间变化的状态时，目标跟踪会考虑由于漏检、误报和其他干扰造成的额外测量源不确定性。感兴趣的对象。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。 随着价格实惠的传感器（例如相机、声纳浮标、卫星接收器）的出现，以状态估计和目标跟踪为目标的传感器处理已变得普遍。这些传感器无处不在且价格低廉，导致了额外的不确定性，迄今为止的文献中尚未正确解决这些不确定性。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，传感器偏差、杂波、电子对抗和其他干扰等系统误差已被有效建模和解决。但是，鉴于可用的异构传感器数量众多，这些额外的不确定性来源尚未得到最佳建模或解决。这种情况为拟议的工作提供了动力。 具体来说，我们将解决以下问题：1）缓解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先跟踪后检测； 3）状态估计与传感器管理的集成； 4）借助不确定的外部数据源（例如地图、地形数据）进行约束状态估计和预测。我们的解决方案方法基于点过程模型和分析组合 (AC) 形式主义，它提供了一种有效的机制来处理大规模问题中的各种不确定性。为了提供全面的解决方案，我们将对传感器内部和外部的各种形式的不确定性进行建模，开发强大的算法以最大限度地减少传感器的工作量，并使用 AC 形式主义的扩展来量化新算法的性能。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批高素质人才。