Parallel Filtering-Communication mode: 网络通讯/融合与节点滤波计算同步进行 网络协同“一致性”研究如火如荼十余载，源起复杂网络控制，后发展至分布式滤波和跟踪。但是现有所有的分布式滤波基本都是“滤波-融合-再滤波-再融合”这种“你方唱罢我登台，轮番上阵”的串行方式，或者说“鸡生蛋，蛋孵鸡”这种相互依赖迭代模式：所融合的对象就是滤波结果，而下一轮滤波的先验就是融合结果。 0-1的突破： 提出了 “一边滤波一边通讯融合”并行模式（即网络通讯融合与节点滤波计算同步进行， Parallel Filtering-Communication mode ） ，难点在于：没有鸡(滤波)怎么来的蛋(融合)？没有蛋(融合)又怎么来的鸡(滤波)？这就是我们Engineer们 施展拳脚 了。。。 团队近期两篇论文 （全世界独此两篇?） ： 第1篇基于粒子滤波，借助Importance Sampling方法，巧妙实现了 高度 Parallel Filtering-Communication ； 第2篇基于高斯混合GM滤波， Parallel Filtering-Communication 实现难度较大，所实现的融合对象仅仅是目标数cardinality的估计，融合层次教浅有待进一步研究。 两个工作分别对最具代表性的两类滤波后验近似形式进行了“滤波与通信并行”机制设计，可以推广到诸多以粒子滤波和高斯混合为基底的其他多传感实时滤波器设计。 网络通讯与节点滤波计算同步进行的优势自不必说， 甚至有些场景下是唯一选择 ！比 如局部节点的滤波计算占据了整个传感器扫描周期，根本没有剩余时间去搞通讯和信息融合 ---- 而这将伴随着传感器扫描周期越来越快变得普遍。。即使有些剩余时间，因为平行通讯可以完成更多周期的信息交换，传播更远，网络收益更大。。 1 A Distributed Particle-PHD Filter with Arithmetic-Average PHD Fusion Tiancheng Li , Franz Hlawatsch (Submitted on 17 Dec 2017 ( v1 ), last revised 20 Dec 2018 (this version, v2)) We propose a particle-based distributed PHD filter for tracking an unknown, time-varying number of targets. To reduce communication, the local PHD filters at neighboring sensors communicate Gaussian mixture (GM) parameters. In contrast to most existing distributed PHD filters, our filter employs an `arithmetic average' fusion. For particles--GM conversion, we use a method that avoids particle clustering and enables a significance-based pruning of the GM components. For GM--particles conversion, we develop an importance sampling based method that enables a parallelization of filtering and dissemination/fusion operations . The proposed distributed particle-PHD filter is able to integrate GM-based local PHD filters. Simulations demonstrate the excellent performance and small communication and computation requirements of our filter. Comments: 13 pages, codes available upon e-mail request Subjects: Systems and Control (cs.SY) ; Distributed, Parallel, and Cluster Computing (cs.DC) Cite as: arXiv:1712.06128 (or arXiv:1712.06128v2 for this version) submission Information Fusion，under revision 2. A Parallel Filtering-Communication based Cardinality Consensus Approach for Real-time Distributed PHD Filtering Tiancheng Li ; Mahendra Mallick ; Quan Pan Abstract: This paper proposes a new cardinality consensus (CC) approach called “prior-CC” to distributed probability hypothesis density (PHD) filtering based on a decentralized sensor network. In our approach, network-wide average consensus is sought with respect to the prior cardinality estimate. Unlike existing serial filtering-communication approaches, the prior-CC scheme allows the internode communication to be performed in parallel with the local filter calculation and requires only a small amount of interfacing fusion calculation and communication. This enables real-time filtering that minimizes data delay and is of great significance in realistic tracking systems. We provide details of the Gaussian mixture implementation of the proposed prior-CC-PHD filter based on a diffuse target birth model and analyze the filtering-communication parallelization. In addition, we evaluate the gain of the prior-CC scheme with respect to the filtering accuracy in comparison with the standard CC scheme via simulations using a stationary linear sensor network and a dynamic nonlinear sensor network, respectively. Published in: IEEE Sensors Journal ( Early Access ) Date of Publication: 22 June 2020 DOI: 10.1109/JSEN.2020.3004068 相关博文链接： 多源信息融合的Best Fit of Mixture原则 多目标信息融合问题 分布式网络信息共享：Many Could Be Better Than All 轻松多传感器多目标探测与跟踪！ 通讯量最小的分布式多目标跟踪器 基于多传感器观测聚类的鲁邦多传感器PHD滤波 基于受限测距传感网的分布式多目标跟踪 基于算术均值一致性的分布式伯努利滤波目标联合探测与跟踪 基于算术均值一致性的高效、分布式、联合传感定位与多目标跟踪
“一致性”研究如火如荼十余载，源起分布式复杂网络控制，后发展至滤波和跟踪。 现有所有的分布式滤波基本都是“滤波-融合-再滤波-再融合”这种“你方唱罢我登台，轮番上阵”的串行方式，或者说“鸡生蛋，蛋孵鸡”这种相互依赖迭代模式。 下文 提出了 一种 “一边滤波一边融合”并行模式（即网络通讯与节点滤波计算同步进行） ，大家可能会问：没有鸡(滤波)怎么来的蛋(融合)？没有蛋(融合)又怎么来的鸡(滤波)？这就是我们Engineer们需要发挥聪明才智的时候了。。。请看下文（借助了简单而强大的Importance Sampling方法）： 网络通讯与节点滤波计算同步进行的优势自不必说， 甚至有些场景下是唯一选择 ！比如局部节点的滤波计算占据了整个传感器扫描周期，根本没有剩余时间去搞通讯和信息融合 ---- 而这将伴随着传感器扫描周期越来越快变得普遍。。即使有些剩余时间，因为平行通讯可以完成更多周期的信息交换，传播更远，网络收益更大。。 A Distributed Particle-PHD Filter with Arithmetic-Average PHD Fusion Tiancheng Li , Franz Hlawatsch (Submitted on 17 Dec 2017 ( v1 ), last revised 20 Dec 2018 (this version, v2)) We propose a particle-based distributed PHD filter for tracking an unknown, time-varying number of targets. To reduce communication, the local PHD filters at neighboring sensors communicate Gaussian mixture (GM) parameters. In contrast to most existing distributed PHD filters, our filter employs an `arithmetic average' fusion. For particles--GM conversion, we use a method that avoids particle clustering and enables a significance-based pruning of the GM components. For GM--particles conversion, we develop an importance sampling based method that enables a parallelization of filtering and dissemination/fusion operations . The proposed distributed particle-PHD filter is able to integrate GM-based local PHD filters. Simulations demonstrate the excellent performance and small communication and computation requirements of our filter. Comments: 13 pages, codes available upon e-mail request Subjects: Systems and Control (cs.SY) ; Distributed, Parallel, and Cluster Computing (cs.DC) Cite as: arXiv:1712.06128 (or arXiv:1712.06128v2 for this version)
Local-Diffusion-based Distributed SMC-PHD Filtering Using Sensors with Limited Sensing Range Tiancheng Li ; Víctor Elvira ; Hongqi Fan ; Juan M. Corchado IEEE Sensors Journal Abstract: We investigate the problem of distributed multitarget tracking by using a set of spatially dispersed, collaborative sensors with limited sensing range (LSR), where each sensor runs a sequential Monte Carlo-probability hypothesis density filter and exchanges relevant posterior information with its neighbors. The key challenge stems from the LSR of neighbor sensors whose fields of view (FoVs) are partially/non-overlapped and therefore they may observe different targets at the same time. With regard to the local common FoVs among neighbor sensors, the proposed distributed fusion scheme, called local diffusion, performs one iteration of neighbor communication per filtering step in either of two means. One is given by immediate particle exchange, in which a reject-control operation is devised to reduce the number of communicating particles. The other is done by converting the particle distribution to Gaussian functions for parametric information exchange and fusion. The performance of both approaches has been experimentally investigated via simulation for different LSR situations and compared with cutting-edge approaches. T. Li, V. Elvira, H. Fan and J. M. Corchado, Local-Diffusion-Based Distributed SMC-PHD Filtering Using Sensors With Limited Sensing Range, in IEEE Sensors Journal , vol. 19, no. 4, pp. 1580-1589, 15 Feb.15, 2019. doi: 10.1109/JSEN.2018.2882084 @ARTICLE{Li2019local, author={T. Li and V. Elvira and H. Fan and J. M. Corchado}, journal={IEEE Sensors Journal}, title={Local-Diffusion-Based Distributed \\cal{SMC-PHD} Filtering Using Sensors With Limited Sensing Range}, year={2019}, volume={19}, number={4}, pages={1580-1589}, doi={10.1109/JSEN.2018.2882084}, ISSN={1530-437X}, month={Feb.},}
( 复杂 ) 网络涉及到一个基础的信息分享问题，即网络节点之间通过信息分享与融合，最终达成“一致” /Consensus ，及网络一致性。特别是相比于基于含有一个网络中间节点的中心式 /Centralized 网络，分布式网络中只通过节点与节点连接（相互称为邻居节点）进行通信，而没有中心节点，所以网络结构更为稳定（不会因为某一节点的破坏等而造成网络瘫痪），易于扩展（网络节点的性质一致，所以任何节点都可以再增加邻居节点）等，也实际上是很多物理网络（如监控传感网、社交网络等）的本质特征。 然而，在多目标跟踪多传感器信息融合里面却存在一个有趣的发现：传感器邻居节点相互之间分享的信息并不一定越多对于大家越有利，这里的“利”特指提高传感器节点估计的精度。这一点初感违背我们的直觉，因为一般的来讲：信息越多（应该）越有利。 那么为什么呐？ 物理传感器往往都遭受两类问题：一类是漏检，一类是虚警。前者是传感器没能获得目标的观测数据所造成，即 missingdata 问题。而后者是传感器遭遇干扰，获得观测数据不属于任何目标，是假信号，即 falsedata 问题。如此情况下，信息的分享就不见得越多越好，因为有些信号可能是 falsedata 相关，其对于邻居节点没有益处，反而可能造成误导。 这一现象可称之为： Many Could Be Better Than All ，或者 Less-is-More 。实际这一现象并不罕见，如在认知科学/cognitive science （Gigerenzer, G., Brighton, H., 2009. Homo heuristicus: Why biased minds make better inferences. Topics in Cognitive Science, 1(1):107–143.） 和神经网络/neural networks（ Zhi-Hua Zhou, Jianxin Wu, Wei Tang, Ensembling neural networks: Many could be better than all, In Artificial Intelligence, Vol. 137, Issues 1–2, 2002, Pages 239-263。 ） 因此适当的控制信息分享量（更宽泛的是，只分享有利的信息，而尽量减少误导性或者干扰性的信息），不但显然有利于降低通讯开支（这一点在现实中往往很重要，甚至是网络的重要限制。特别是分布式传感器网络往往都是 low-powered/ 低耗的传感器构成，以减少通讯和造价开支等） , 反而还可能更利于获得更高估计精度。 如下两篇论文分别基于高斯混合 /Gaussian Mixtures 和 序贯蒙特卡洛 /Sequential Monte Carlo( 也称粒子滤波 ) 实现 PHD 滤波进行多目标探测估计揭示这一发现，提出了“部分一致性” Partial Consensus 的概念。同时在随机集 PHD 一致性信息融合方式上给出了一些探索性思考，特别明确和比较了（简单却被忽视的）算术平均 ArithmeticAverage 和（当前主流）几何平均 GeometricAverage 的区别和相比优势，提出了实时一致性 RealTime Consensus （即网络通信在理论上不对滤波器造成延迟，两者在一定程度上可以实现并行进行）的概念。 T. Li, J.M. Corchado and S. Sun, Partial Consensus and Conservative Fusion of Gaussian Mixtures for Distributed PHD Fusion, IEEE Trans. Aeros. Electr. Syst., 2018, DOI: 10.1109/TAES.2018.2882960. 连接：https://ieeexplore.ieee.org/document/8543158 Partial Consensus and Conservative Fusion of Gaussian Mixtures for Distributed PHD Fusion Tiancheng Li , Juan M Corchado , Shudong Sun Link : arXiv:1711.10783 We propose a novel consensus notion, called partialconsensus, for distributed GM-PHD (Gaussian mixture probabilityhypothesis density) fusion based on a peer-to-peer (P2P) sensor network, inwhich only highly-weighted posterior Gaussian components (GCs) are disseminatedin the P2P communication for fusion while the insignificant GCs are notinvolved. The partial consensus does not only enjoy high efficiency in both network communication and local fusion computation but also significantly reduces the effect of potential false data (clutter) to the filter, leading to increased signal-to-noise ratio at local sensors. Two conservative mixture reduction schemes are advocated for fusing the shared GCs in a fully distributed manner. One is given by pairwise averaging GCs between sensorsbased on Hungarian assignment and the other is merging close GCs based a new GMmerging scheme. The proposed approaches have a close connection to theconservative fusion approaches known as covariance union and arithmetic meandensity. In parallel, average consensus is sought on the cardinalitydistribution (namely the GM weight sum) among sensors. Simulations for tracking either a single target or multiple targets that simultaneously appear are presented based on a sensor network where each sensor operates a GM-PHD filter, in order to compare our approaches with the benchmark generalized covariance intersection approach. The results demonstrate that the partial, arithmeticaverage, consensus outperforms the complete, geometric average, consensus. 上文正式发表在 : IEEE Transactions on Aerospace and Electronic Systems DOI: 10.1109/TAES.2018.2882960 Distributed SMC-PHD Fusion for Partial, Arithmetic Average Consensus Tiancheng Li Link : arXiv:1712.06128 We propose an average consensus approach for distributed SMC-PHD(sequential Monte Carlo-probability hypothesis density) fusion, in which local filters extract Gaussian mixtures (GMs) from their respective particle posteriors, share them (iteratively) with their neighbors and finally use the disseminated GM to update the particle weight. There are two distinguishable features of our approach compared to existing approaches. First, a computationally efficient particles-to-GM (P2GM) conversion scheme is developed based on the unique structure of the SMC-PHD updater in which the particle weight can be exactly decomposed with regard to the measurements and misdetection. Only significant components of higher weight are utilized for parameterization. The consensus, conditioned on partial information dissemination over the network, is called partial consensus.Second, importance sampling (IS) is employed to re-weight the local particles for integrating the received GM information, while the states of the particles remain unchanged. By this, the local prior PHD and likelihood calculation can be carried out in parallel to the dissemination \\ fusion procedure. To assess the effectiveness of the proposed P2GM parameterization approach and IS approach, two relevant yet new distributed SMC-PHD fusion protocols are introduced for comparison. One uses the same P2GM conversion and GMdissemination schemes as our approach but local particles are regenerated from the disseminated GMs at each filtering iteration - in place of the IS approach. This performs similar to our IS approach (as expected) but prevents any parallelization as addressed above. The other is disseminating the particles between neighbors - in place of the P2GM conversion. This avoids parameterization but is communicatively costly. The state-of-the-art exponential mixture density approach is also realized for comparison.
越是基础的东西越是重要，越是简单明了的东西有时候更实用 - 虽然不那么学术，玩起来不够酷不够炫。 Flooding信息分享就是这么一个技术！学术上被忽略了。非常有趣的“定义 - 定理 - 证明”科学官科八股文。 另外，Flooding这个词怎么翻译为中文合适呐？ Convergence of Distributed Flooding and Its Application for Distributed Bayesian Filtering Abstract: Distributed flooding is a fundamental information sharing method to obtaining network consensus via peer-to-peer communication. However, a unified consensus-oriented formulation of the algorithm and its convergence performance are not explicitly available in the literature. To fill this void in this paper, set-theoretic flooding rules are defined by encapsulating the information of interest in finite sets (one set per node), namely distributed set-theoretic information flooding (DSIF). This leads to a new type of consensus called collecting consensus which aims to ensure that all nodes get the same information. Convergence and optimality analyses are provided based on a consistent measure of the degree of consensus (DoC) of the network. Compared with the prevailing averaging consensus, the proposed DSIF protocol benefits from avoiding repeated use of any information and offering the highest converging efficiency for network consensus while being exposed to increasing node-storage requirements against communication iterations and higher communication load. The protocol has been advocated for distributed nonlinear Bayesian filtering, where each node operates a separate particle filter, and the collecting consensus is sought on the sensor data alone or jointly with intermediate local estimates. Simulations are provided to demonstrate the theoretical findings.
Il y a souvent des pb dans la langue francaise quand onutilise le verbe avoir pour les formes composé (ici avoir envoyer ). Ça ne s'accorde pas avec le sujet (il a envoyé mais aussi elle a envoyé - et pas elle a envoyée), par contre ça s'accorde avec l'objet. Donc ici je te les ai envoyé s Mais c'est vraiment du détail et c'est la faute de francais la plus présente chez tout le monde.
Experiment on the Thermal Issues of High Power and High Energy Parallel-serial LiFePO4 Lithium-Ion Battery Pack Tong Meng *,a,b Ouyang Minggao a , Lu Languang a , Wang Ying a , Shao Jingyue a , Deng longyang a , Li Zhe a , Yan Jun a , Tang Tao a ,Qingfeng Lin a ,Yongping Wu b , Jian Ma b , Shengjie Jiao b a. Department of Automobile Engineering, Tsinghua University, Beijing, China b. Department of Construction Mechanical, Chang An University, Xi an, China Corresponding author. Tel.: +86 13581890123, Email: drtongmeng【at】gmail.com Abstract A LiFePO 4 based Lithium-Ion ( LFP ) battery pack in parallel-series configuration with 208 11Ah cells has been tested to find the potential thermal issues, which are important to its performance, safety, and durability in battery electric vehicle (BEV) application. While temperature rise is only moderate for most cells in charge/discharge at C/3 or C/2 rate, overheating and uneven temperature distribution phenomenon in some cells are observed in discharging process. Quite a few of cells are seriously overheated in the initial discharging process, even with only a 30Ah discharge at C/3 rate. Temperature distribution and overheating is relevant to current distribution, internal resistance, electrical contacts resistance, and SOCs or discharging progress imbalances between cells in parallel or in series. Overdischarge and voltage reverse induced by SOCs imbalances, contact resistance difference, and SEI film effects are the major factors contributed to the overheating issues. Current waveform distortion phenomenon in single cells Experiment shows an agreement to existence of random breakdown of SEI film in LFP battery, which may greatly influence the current distribution among cells in parallel. The merits of the Lower internal resistance of the powerful LFP Battery pack inversely deteriorate the thermal issues due to increasing the negative influence of the contact resistance imbalance and SEIs random breaking. Balancing the modules (sub-stack) and group, adjusting the contact force apparently lessen the generated heat and overheating phenomenon. Measures, like equalization of all the series in the pack and monitoring temperatures of cells, are required to avoid the partially abuse of cells. Reasonable mechanical structure and electrical contacts design and assembly are equally important for the LFP Battery pack too. Keywords: lithium iron phosphate (LFP) battery; parallel-series connected battery pack; imbalance; overheat; SEI summitted paper, welcome to give review to me. Email: drtongmeng【at】gmail.com 欢迎索取正文，不过希望从各个方面提出批评意见，以便于在下改进工作，谢谢！提出意见详细者，我以后会将本人论文优先提供阁下审阅。
标签: 一致性
