# ֲʽWjϢMany Could Be Better Than All x

2607 x 2018-12-14 16:41 |˷:йPӛ|ϵy:ՓĽ| sWj, ֲʽۙ, Ŀ˸ۙ, V, SC

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@һFɷQ֮飺Many Could Be Better Than AllLess-is-MoreH@һF󲢲ҊJ֪ƌW/cognitive scienceGigerenzer, G., Brighton, H., 2009. Homo heuristicus: Why biased minds make better inferences. Topics in Cognitive Science, 1(1):107C143.񽛾Wj/neural networksZhi-Hua Zhou, Jianxin Wu, Wei Tang, Ensembling neural networks: Many could be better than all, In Artificial Intelligence, Vol. 137, Issues 1C2, 2002, Pages 239-263F

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Ļڸ˹FPHDVMshµĶĿ̽ycӋʾ@һlFˡһԡPartial Consensusĸ_ɣһҪ(_)ȫһͬrSCPHDһϢںϷʽϽoһЩ̽˼؄e_ͱ^ˣ΅sҕģgƽArithmetic Averageͣǰ׺ƽGeometric Averageą^e

• 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

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

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.

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

(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)

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## Local-Diffusion-based Distributed SMC-PHD Filtering Using Sensors with Limited Sensing Range

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

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