This paper combines the distributed sensor fusion system with the signal detection under chaotic noise to realize the distributed sensor fusion detection from chaotic background. First, based on the short-term predictability of the chaotic signal and its sensitivity to small interference, the phase space reconstruction of the observation signal of each sensor is carried out. Second, the distributed sensor local linear autoregressive (DS-LLAR) model is constructed to obtain the one-step prediction error of each sensor. Then, we construct a Bayesian risk model and also obtain the corresponding conditional probability density function under each sensor’s hypothesis test which firstly needs to fit the distribution of prediction errors according to the parameter estimation. Finally, the fusion optimization algorithm is designed based on the Bayesian fusion criterion, and the optimal decision rule of each sensor and the optimal fusion rule of the fusion center are jointly solved to effectively detect the weak pulse signal in the observation signal. Simulation experiments show that the proposed method which used a distributed sensor combined with a local linear model can effectively detect weak pulse signals from chaotic background.

中文翻译：

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混沌背景下微弱脉冲信号的分布式传感器局部线性融合检测

本文将分布式传感器融合系统与混沌噪声下的信号检测相结合，实现了混沌背景下的分布式传感器融合检测。首先，基于混沌信号的短期可预测性及其对小干扰的敏感性，对每个传感器的观测信号进行相空间重构。其次，构建分布式传感器局部线性自回归（DS-LLAR）模型以获得每个传感器的一步预测误差。然后，我们构造贝叶斯风险模型，并在每个传感器的假设检验下获得相应的条件概率密度函数，该条件概率密度函数首先需要根据参数估计来拟合预测误差的分布。最后，根据贝叶斯融合准则设计融合优化算法，联合求解每个传感器的最优决策规则和融合中心的最优融合规则，以有效地检测观测信号中的弱脉冲信号。仿真实验表明，该方法结合分布式传感器和局部线性模型可以有效地检测混沌背景下的微弱脉冲信号。