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
论文： A Robust Multi-Sensor PHD Filter Based on Multi-Sensor Measurement Clustering 作者：Tiancheng Li ; Javier Prieto ; Hongqi Fan ; Juan M. Corchado Published in: IEEE Communications Letters (Volume: 22, Issue: 10 , Oct. 2018) Page(s): 2064 - 2067 连接： https://ieeexplore.ieee.org/document/8425712/ This letter presents a novel multi-sensor probability hypothesis density (PHD) filter for multi-target tracking by means of multiple or even massive sensors that are linked by a fusion center or by a peer-to-peer network. As the challenge we confront, little is known about the statistical properties of the sensors in terms of their measurement noise, clutter, target detection probability and even potential cross-correlation. Our approach converts the collection of the measurements of different sensors to a set of proxy, homologous measurements. These synthetic measurements overcome the problems of false and missing data and of unknown statistics and facilitate linear PHD updating that amounts to the standard PHD filtering with no false and missing data. Simulation has demonstrated the advantages and limitations of our approach in comparison to the cutting-edge multi-sensor/distributed PHD filters. 本文提出了一种新的多传感器概率假设密度（PHD）滤波器，用于集中式式或点对点分布式网络链接的多个甚至大量传感器下的多目标跟踪。 本文主要解决的一个挑战是系统缺乏传感器的统计特性如测量噪声、杂波率、目标检测概率甚至潜在的传感器相互关联等。 我们的方法是将不同传感器的测量数据集合、聚类转换为一组合成的代理、同源测量数据。 这些合成测量取代原始量测数据可以实现线性PHD更新，克服了虚警和漏检数据以及未知传感器统计信息的挑战。这一过程相当于一个没有漏检和虚警环境的标准PHD更新。 仿真验证了我们的方法与当前主流的集中式多传感器/分布式PHD滤波器相比的优势和局限性。 T. Li, J. Prieto, H. Fan and J. M. Corchado, A Robust Multi-Sensor PHD Filter Based on Multi-Sensor Measurement Clustering, in IEEE Communications Letters , vol. 22, no. 10, pp. 2064-2067, Oct. 2018. doi: 10.1109/LCOMM.2018.2863387
标签: 多目标跟踪
