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摘要在传感器网络中基于TOA定位多个事件的一个根本问题就是确认各个TOA和事件的对应关系。本文在声传感器检测时间上间隔紧密的事件情况下考虑这个问题。由于声音相对较低的传播速度，多个事件发出的声音到达传感器的时序和多个事件实际发生的次序不一定相同，从而会产生时序交错问题。本文证明在典型的实际应用背景下这些潜在的时序交错可以得到有效解决，并且研究了一种能够在TOA存在测量噪声情况下成功定位多个事件的算法。算法采用了并行和分层的处理过程从而避免了尝试TOA和事件之间所有可能的关系所带来的额外的计算复杂性。算法将假设的事件发生时间离散化后产生一组候选事件位置的集合，集合里包含真实事件在TOA存在测量噪声情况下的定位结果。通过迭代提炼产生的估计结果，首先抛弃明显的虚假事件，然后通过把TOA和多个事件匹配的算法，在实现多个发声事件准确定位的同时分辨出异常值和遗漏值。28502
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关键词 发声事件 声源定位 到达时间 时序交错
毕业论文设计说明书外文摘要
Title Localizing Multiple Events Using Times of Arrival:
a Parallelized, Hierarchical Approach to the Association Problem
Abstract
A fundamental problem in localizing multiple events based on Times of Arrival (TOAs) at a number of sensors is that of associating TOAs with events.We consider this problem in the context of acoustic sensors monitoring events that are closely spaced in time. Due to the relatively low speed of propagation of sound,the order in which the events arrive at a sensor need not be the same as the order in which they occur, potentially creating fundamental ambiguities. We then show that these potential ambiguities are not a bottleneck in typical practical settings, proposing and evaluating an algorithm that successfully localizes multiple events using noisy observations. The algorithm employs parallelism and hierarchical processing to avoid the excessive complexity of naively trying all possible associations of events with TOAs. We use dicretization of hypothesized event times to enable us to efficiently generate a set of candidate event locations, which contain noisy versions of true events as well as phantom events.We refine these estimates iteratively,discarding “obvious” phantoms, and then solve a linear programming formulation for matching true events to TOAs, while identifying outliers and misses.
Keywords acoustic events acoustic source localization time of arrival(TOA) time sequence interleaved.
目 次
1 引言 1
1.1 研究背景和意义 1
1.2 国内外研究现状 2
1.3 论文主要工作 3
1.4 论文结构安排 3
2 基于无线声传感网的声源定位技术 5
2.1 基于DOA定位的方法 5
2.2 基于能量定位 5
2.3 基于TOA/TDOA定位 6
3 顺序交错情况下基于TOA的多声源定位方法研究 8
3.1 顺序交错的概念及其对定位的影响 8
3.2 基于TOA的多声源的顺序交错的定位的算法 9
3.2.1 生成候选事件 10
3.3.2 精确事件的估计 13
3.2.3 从候选事件集合中鉴别真实事件和异常值 13
3.3 本章小结 17
4 仿真实验 18
4.1 探测节点无漏检和异常值情况 18