田里橙子分享 http://www.ueservicedoffices.com/u/JRoy 我愛生命,更愛生活

博文

分布式網絡信息共享:Many Could Be Better Than All 精選

已有 2607 次閱讀 2018-12-14 16:41 |個人分類:科研筆記|系統分類:論文交流| 復雜網絡, 分布式跟蹤, 目標跟蹤, 濾波, 隨機集

(復雜)網絡涉及到一個基礎的信息分享問題,即網絡節點之間通過信息分享與融合,最終達成“一致”/Consensus,及網絡一致性。特別是相比于基于含有一個網絡中間節點的中心式/Centralized網絡,分布式網絡中只通過節點與節點連接(相互稱為鄰居節點)進行通信,而沒有中心節點,所以網絡結構更為穩定(不會因為某一節點的破壞等而造成網絡癱瘓),易于擴展(網絡節點的性質一致,所以任何節點都可以再增加鄰居節點)等,也實際上是很多物理網絡(如監控傳感網、社交網絡等)的本質特征。

然而,在多目標跟蹤多傳感器信息融合里面卻存在一個有趣的發現:傳感器鄰居節點相互之間分享的信息并不一定越多對于大家越有利,這里的“利”特指提高傳感器節點估計的精度。這一點初感違背我們的直覺,因為一般的來講:信息越多(應該)越有利。

那么為什么吶? 物理傳感器往往都遭受兩類問題:一類是漏檢,一類是虛警。前者是傳感器沒能獲得目標的觀測數據所造成,即missingdata問題。而后者是傳感器遭遇干擾,獲得觀測數據不屬于任何目標,是假信號,即falsedata問題。如此情況下,一個直觀的邏輯是:因為有些信號可能是falsedata相關,其對于鄰居節點沒有益處,反而可能造成誤導。因此,信息的分享就不見得越多越好,

這一現象可稱之為:Many Could Be Better Than All,或者Less-is-More。實際這一現象并不罕見,如在認知科學/cognitive science(Gigerenzer, G., Brighton, H., 2009. Homo heuristicus: Why biased minds make better inferences. Topics in Cognitive Science, 1(1):107–143.)和神經網絡/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。都有所表現。

因此適當的控制信息分享量(更寬泛的是,只分享有利的信息,而盡量減少誤導性或者干擾性的信息),不但顯然有利于降低通訊開支(這一點在現實中往往很重要,甚至是網絡的重要限制。特別是分布式傳感器網絡往往都是low-powered/低耗的傳感器構成,以減少通訊和造價開支等)反而還可能更利于獲得更高估計精度。

下文基于高斯混合實現PHD濾波進行雜波環境下的多目標探測與估計揭示這一發現,提出了“部分一致性”Partial Consensus的概念:(達成)部分一致要優于(達成)完全一致。同時在隨機集PHD一致性信息融合方式上給出了一些探索性思考,特別明確和比較了(簡單卻被忽視的)算術平均Arithmetic Average和(當前主流)幾何平均Geometric Average的區別和相對優勢。

  • 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.  IEEE Xplore

連接:

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

Tiancheng Li Juan M. Corchado Shudong Sun

IEEE Transactions on Aerospace and Electronic Systems


Abstract:

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 does not only gain 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.



相關連接:

研究進一步擴展到采用隨機樣本(粒子濾波器)實現后驗分布下的分布式“部分一致性”PHD濾波。

A Distributed Particle-PHD Filter with Arithmetic-Average PHD Fusion

Tiancheng LiFranz 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 [cs.SY]

(or arXiv:1712.06128v2 [cs.SY] for this version)


研究進一步擴展到測距受限傳感網下的多目標跟蹤: 

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.




http://www.ueservicedoffices.com/blog-388372-1151544.html

上一篇:狀態估計的新框架?!
下一篇:并行一致性:網絡通訊與節點濾波計算同步進行!

5 楊正瓴 陸澤櫞 謝力 黃秀清 寧利中

該博文允許注冊用戶評論 請點擊登錄 評論 (3 個評論)

數據加載中...

Archiver|手機版|科學網 ( 京ICP備14006957 )

GMT+8, 2019-6-26 14:55

Powered by ScienceNet.cn

Copyright © 2007- 中國科學報社

返回頂部
时时彩平台