在几乎所有（有关传感器的）参数和（有关目标的）模型条件均未知的情况下，怎么做到：采用一个无线传感网（节点之间还可能非相互独立）去探测、跟踪未知数目的一群目标？

传统上来说，就是采用一个传感器去跟踪估计一个目标，滤波器的设计也往往要基于准确的传感器参数（比如噪声统计特性、杂波率、漏检率等）和相对准确的目标模型信息（否则就需要构建多模型或者自适应模型进行近似或学习逼近），这些所涉及的参数和模型，任何一个未知都会给估计跟踪带来很大困难！比如常常借助于有效的系统辨识或者参数学习机制等等，滤波器才能够有效运行。

那么一堆传感器（特别是分布式网络链接起来）和一堆目标呐，什么属性都完全未知的时候呐？不仅仅是数量的升级，还可能带来传感器之间和目标之间的交互关联等复杂问题！这就使得多传感器多目标跟踪成为一个更为棘手的难题！大道至简，难到一定程度的问题也许可以用简单的方法解决！

请看下文所提出的一中 Lazy Networking Approach: 轻松网络协作方法，只需要Flooding 和 Clustering两个操作，就可以应对各种参数和模型未知，方法简单计算快、效果可以胜过提供了真实参数和模型信息的传统滤波器（也就是先不让滤波器去操心参数和模型未知的问题，给它们最理想的条件）！

Distributed Flooding-then-Clustering: A Lazy Networking Approach for Distributed Multiple Target Tracking

Authors: Tiancheng Li ;  Juan M Corchado ;  Huimin Chen

Abstract:

We propose a straightforward but efficient networking approach to distributed multi-target tracking, which is free of ingenious target model design. We confront two challenges: One is from the lack of statistical knowledge about the target appearance/disappearance and movement, and about the sensors, e.g., the rates of clutter and misdetection; The other is from the severely limited computing and communication capability of the low-powered sensors, which may prevent them from running a full-fledged tracker/filter. To overcome these challenges, a flooding-then-clustering (FTC) approach is proposed which comprises two components: a distributed flooding scheme for iteratively sharing the measurements between sensors and a clustering-for-filtering approach for target detection and position estimation from the local aggregated measurements. We compare the FTC approach with cutting edge distributed probability hypothesis density (PHD) filters that are modeled with appropriate statistical knowledge about the target motion and the sensors. A series of simulation studies using either linear or nonlinear sensors, have been presented to verify the effectiveness of the FTC approach.

Published in: https://ieeexplore.ieee.org/document/8455759

DOI: 10.23919/ICIF.2018.8455759