基于慣性網(wǎng)絡(luò )的分布式信息融合算法

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基于慣性網(wǎng)絡(luò )的分布式信息融合算法
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作者單位:(1.南京航空航天大學(xué) 自動(dòng)化學(xué)院,南京 210016; ;2.南京航空航天大學(xué) 航天學(xué)院,南京 210016)
作者簡(jiǎn)介:黃 帥(1988-),男,江蘇南通人,碩士研究生,主要從事精密儀器及機械、導航定位及測控技術(shù)等方向的研究。 [FQ)]
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中圖分類(lèi)號:TP202
基金項目:江蘇省自然科學(xué)基金(KB2011729)；南京航空航天大學(xué)基本科研業(yè)務(wù)專(zhuān)項(NN2012036)。

A Distributed Information Fusion Algorithm Based on Inertial Network

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(1.College of Automation, NUAA, Nanjing 210016, China;  ;2.College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

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摘要:

分布式導航系統是飛機多傳感器導航系統設計的新概念,可以大幅提高系統導航性能和容錯水平,并能動(dòng)態(tài)配置傳感器功能,但是目前并無(wú)完善的信息融合算法與之對應;文章在構建慣性傳感器網(wǎng)絡(luò )的基礎上,將多個(gè)低成本慣性傳感器系統配置在飛機的多個(gè)位置以作為網(wǎng)絡(luò )節點(diǎn),設計了分階段處理的分布式信息融合算法,綜合利用各節點(diǎn)所測量的慣性信息,最后得到本節點(diǎn)的局部狀態(tài)估計;通過(guò)仿真實(shí)驗表明,采用此方法,有效降低了導航濾波估計誤差,因此,系統導航性能及容錯能力得到大幅提高。

Abstract:

Distributed navigation system is a new concept designed for the aircraft multi-sensor navigation system. And it can significantly improve the performance and fault-tolerance level of the system. What’s more, distributed navigation system can configure the sensors dynamically. However, there is no corresponding information fusion algorithm to deal with the new method. To solve the problem, a two-step distributed information fusion algorithm is presented in this paper. The distributed information fusion algorithm is based on the structure of inertial network, which consists of IMU (Inertial measurement unit) nodes installed in different parts of the carrier to provide redundant navigation information. With this algorithm, each node will take full use of the other nodes’ inertial information. The simulation results show that using this method, the navigation filtering estimation is effectively reduced, therefore, the performance of navigation system and fault tolerant capability is improved greatly. 

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黃帥,劉海穎,楊毅鈞,吳風(fēng)喜.基于慣性網(wǎng)絡(luò )的分布式信息融合算法計算機測量與控制[J].,2014,22(6):1853-1855,1867.

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收稿日期:2013-11-21
最后修改日期:2014-01-07
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在線(xiàn)發(fā)布日期: 2014-11-12
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