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Partial Consensus and Conservative Fusion of Gaussian Mixtures for Distributed PHD Fusion

机译：高斯混合物的部分共识和保守融合对分布式PHD融合的影响

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摘要

We propose a novel consensus notion, called "partial consensus," for distributed Gaussian mixture probability hypothesis density fusion based on a decentralized sensor network, in which only highly weighted Gaussian components (GCs) are exchanged and fused across neighbor sensors. It is shown that this not only gains high efficiency in both network communication and fusion computation, but also significantly compensates the effects of clutter and missed detections. Two "conservative" mixture reduction schemes are devised for refining the combined GCs. One is given by pairwise averaging GCs between sensors based on Hungarian assignment and the other merges close GCs for trace minimal, yet, conservative covariance. The close connection of the result to the two approaches, known as covariance union and arithmetic averaging, is unveiled. Simulations based on a sensor network consisting of both linear and nonlinear sensors, have demonstrated the advantage of our approaches over the generalized covariance intersection approach.
机译：我们为基于分布式传感器网络的分布式高斯混合概率假设密度融合提出了一种新颖的共识概念，称为“部分共识”，其中只有高权重的高斯分量（GC）在相邻传感器之间进行交换和融合。结果表明，这不仅在网络通信和融合计算中都获得了很高的效率，而且还大大补偿了混乱和漏检的影响。设计了两种“保守”的混合气还原方案，以完善组合的气相色谱仪。一种是通过基于匈牙利分配的传感器之间的成对平均GC给出的，另一种是合并封闭GC以获得痕量最小但保守的协方差。揭示了结果与两种方法（称为协方差并集和算术平均）的紧密联系。基于由线性和非线性传感器组成的传感器网络进行的仿真证明了我们的方法优于广义协方差相交方法的优势。

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