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Statistical modeling and data fusion of automotive sensors for object detection applications in a driving environment.

机译:用于行驶环境中的对象检测应用的汽车传感器的统计建模和数据融合。

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摘要

In this work, we consider the application of classical statistical inference to the fusion of data from different sensing technologies for object detection applications in order to increase the overall performance for a given active safety automotive system.;Research evolved mainly around a centralized sensor fusion architecture assuming that three non-identical sensors, modeled by corresponding probability density functions (pdfs), provide discrete information of target being present or absent with associated probabilities of detection and false alarm for the sensor fusion engine. The underlying sensing technologies are the following standard automotive sensors: 24.5 GHz radar, high dynamic range infrared camera and a laser-radar. A complete mathematical framework was developed to select the optimal decision rule based on a generalized multinomial distribution resulting from a sum of weighted Bernoulli random variables from the Neyman-Pearson lemma and the likelihood ratio test. Moreover, to better understand the model and to obtain upper bounds on the performance of the fusion rules, we assumed exponential pdfs for each sensor and a parallel mathematical expression was obtained based on a generalized gamma distribution resulting from a sum of weighted exponential random variables for the situation when the continuous random vector of information is available.;Mathematical expressions and results were obtained for modeling the following case scenarios: (i) non-identical sensors, (ii) identical sensors, (iii) combination of nonidentical and identical sensors, (iv) faulty sensor operation, (v) dominant sensor operation, (vi) negative sensor operation, and (vii) distributed sensor fusion.;The second and final part of this research focused on: (a) simulation of statistical models for each sensing technology, (b) comparisons with distributed fusion, (c) overview of dynamic sensor fusion and adaptive decision rules.
机译:在这项工作中,我们考虑将经典统计推断应用于来自不同传感技术的数据融合以进行对象检测应用,以提高给定主动安全汽车系统的整体性能。研究主要围绕集中式传感器融合架构展开假设通过相应的概率密度函数(pdf)建模的三个不相同的传感器为传感器融合引擎提供了存在或不存在的目标离散信息以及相关的检测概率和错误警报。基本的传感技术是以下标准汽车传感器:24.5 GHz雷达,高动态范围红外摄像头和激光雷达。开发了一个完整的数学框架,以基于广义多项式分布选择最佳决策规则,该多项式分布来自Neyman-Pearson引理和似然比检验的加权Bernoulli随机变量之和。此外,为了更好地理解模型并获得融合规则性能的上限,我们为每个传感器假定了pdf pdf,并根据由加权指数随机变量总和得出的广义伽马分布获得了并行数学表达式。当连续的信息向量可用时的情况。数学表达式和结果用于以下情况的建模:(i)不相同的传感器,(ii)相同的传感器,(iii)相同和不相同的传感器的组合, (iv)错误的传感器操作,(v)主导传感器操作,(vi)负传感器操作和(vii)分布式传感器融合。;本研究的第二部分也是最后一部分着重于:(a)每个模型的统计模型的仿真感应技术,(b)与分布式融合的比较,(c)动态传感器融合和自适应决策规则的概述。

著录项

  • 作者

    Hurtado, Miguel A.;

  • 作者单位

    Purdue University.;

  • 授予单位 Purdue University.;
  • 学科 Applied Mathematics.;Engineering Automotive.;Remote Sensing.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 138 p.
  • 总页数 138
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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