This paper presents information-theoretic performance analysis of passive sensor networks for detection of moving targets. The proposed method falls largely under the category of data-level information fusion in sensor networks. To this end, a measure of information contribution for sensors is formulated in a symbolic dynamics framework. The network information state is approximately represented as the largest principal component of the time series collected across the network. To quantify each sensor’s contribution for generation of the information content, Markov machine models as well as x-Markov (pronounced as cross-Markov) machine models, conditioned on the network information state, are constructed; the difference between the conditional entropies of these machines is then treated as an approximate measure of information contribution by the respective sensors. The x-Markov models represent the conditional temporal statistics given the network information state. The proposed method has been validated on experimental data collected from a local area network of passive sensors for target detection, where the statistical characteristics of environmental disturbances are similar to those of the target signal in the sense of time scale and texture. A distinctive feature of the proposed algorithm is that the network decisions are independent of the behavior and identity of the individual sensors, which is desirable from computational perspectives. Results are presented to demonstrate the proposed method’s efficacy to correctly identify the presence of a target with very low false-alarm rates. The performance of the underlying algorithm is compared with that of a recent data-driven, feature-level information fusion algorithm. It is shown that the proposed algorithm outperforms the other algorithm.

中文翻译：

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基于时间序列数据的马尔可夫模型的传感器网络信息理论性能分析

本文提出了用于运动目标检测的无源传感器网络的信息理论性能分析。所提出的方法在很大程度上属于传感器网络中的数据级信息融合。为此，在符号动力学框架中制定了传感器信息贡献的度量。网络信息状态大约表示为跨网络收集的时间序列的最大主成分。为了量化每个传感器对信息内容生成的贡献，构造了马尔可夫机器模型以及基于网络信息状态的x马尔可夫机器模型（发音为跨马尔可夫模型）。然后，将这些机器的条件熵之间的差异视为相应传感器对信息贡献的近似度量。x-Markov模型表示给定网络信息状态的条件时间统计量。该方法已经在从用于目标检测的无源传感器局域网中收集的实验数据上得到了验证，在时间尺度和纹理方面，环境干扰的统计特征与目标信号的统计特征相似。所提出算法的一个显着特征是网络决策与单个传感器的行为和身份无关，这从计算角度来看是理想的。提出的结果证明了所提出的方法能够正确地识别出虚警率极低的目标。将基础算法的性能与最近的数据驱动的特征级信息融合算法的性能进行了比较。结果表明，所提算法优于其他算法。