田里橙子分享 http://blog.sciencenet.cn/u/JRoy 我爱生命,更爱生活

博文

不用滤波器的滤波!

已有 9066 次阅读 2017-1-20 09:19 |个人分类:科研笔记|系统分类:论文交流| 跟踪, 估计, 滤波

信号和信息科学领域,非线性估计与滤波是一个持久不衰的主题,而主流Markov-Bayes理论统治这个领域太久太流行了。。。然而科学不是一成不变的真理条条,研的兴趣在于不牢记教条、 尝试突破不断超越 兴许别有洞天。尤其是随着 时代进步,硬件条件升级,也会促进理论格局变化。。 比如很简单:一个百万像素级别的高速摄像机需要解决的传感问题跟一个KB级别的黑白相机能一样吗?


下文给出一种不用滤波器的滤波去杂方法(思想简单,但是意味深远),承接上文:Effectiveness of Bayesian filters: An information fusion perspective



链接:Clustering for filtering: Multi-bject detection and estimation using multiple/massive sensors


Highlights


Multi-sensor multi-object detection and estimation is solved by a clustering approach.

Accommodate little prior information about targets, background and sensors.

Neither sophisticated modeling nor unrealistic assumption is required.

Outperform state-of-the-art filters in average multi-sensor cases.


Abstract

Advanced multi-sensor systems are expected to combat the challenges that arise in object recognition and state estimation in harsh environments with poor or even no prior information, while bringing new challenges mainly related to data fusion and computational burden. Unlike the prevailing Markov-Bayes framework that is the basis of a large variety of stochastic filters and the approximate, we propose a clustering-based methodology for multi-sensor multi-object detection and estimation (MODE), named clustering for filtering (C4F), which abandons unrealistic assumptions with respect to the objects, background and sensors. Rather, based on cluster analysis of the input multi-sensor data, the C4F approach needs no prior knowledge about the latent objects (whether quantity or dynamics), can handle time-varying uncertainties regarding the background and sensors such as noises, clutter and misdetection, and does so computationally fast. This offers an inherently robust and computationally efficient alternative to conventional Markov–Bayes filters for dealing with the scenario with little prior knowledge but rich observation data. Simulations based on representative scenarios of both complete and little prior information have demonstrated the superiority of our C4F approach.


进一步的研究:能够不需要任何传感器参数和场景假设的 【参数自学习】的多源传感数据聚类

Multi-source Homogeneous Data Clustering for Multi-target Detection from Cluttered Background with Misdetection





http://wap.sciencenet.cn/blog-388372-1028756.html

上一篇:给你说过,俺是条汉子!
下一篇:分布式网络信息共享Flooding的收敛性和分布式Bayes滤波应用

7 罗汉江 陆泽橼 徐令予 杨正瓴 彭真明 徐志刚 翟自洋

该博文允许注册用户评论 请点击登录 评论 (10 个评论)

数据加载中...
扫一扫,分享此博文

Archiver|手机版|科学网 ( 京ICP备07017567号-12 )

GMT+8, 2021-11-28 21:55

Powered by ScienceNet.cn

Copyright © 2007- 中国科学报社

返回顶部