【摘要】：隨著世界各國對(duì)海洋問題的逐步重視,水面高機(jī)動(dòng)無人艇獲得了空前的高速發(fā)展,并將會(huì)在將來海戰(zhàn)中扮演重要角色。為了適應(yīng)更復(fù)雜的未來海洋作戰(zhàn)環(huán)境,無人艇協(xié)同導(dǎo)航技術(shù)與傳統(tǒng)導(dǎo)航技術(shù)相比也面臨著巨大的挑戰(zhàn)。本課題將從協(xié)同導(dǎo)航網(wǎng)絡(luò)中的多傳感器信息融合技術(shù)入手,首先對(duì)目前各國無人艇技術(shù)研究狀況進(jìn)行了簡要介紹,通過對(duì)國內(nèi)關(guān)于無人艇發(fā)展的研究分析可知,我國雖然在近幾年獲得了較大的發(fā)展,但是與美國和以色列等無人艇技術(shù)強(qiáng)國之間還存在較大的差距。就無人艇的協(xié)同導(dǎo)航網(wǎng)絡(luò)而言,縮短其差距的方法不僅是要提高協(xié)同網(wǎng)絡(luò)中的傳感器精度,而且還要改進(jìn)信息融合中的最優(yōu)估計(jì)準(zhǔn)則。所以本文主要就協(xié)同導(dǎo)航網(wǎng)絡(luò)多傳感器信息融合的結(jié)構(gòu)、信息來源和融合準(zhǔn)則進(jìn)行了分析和仿真比較,得出針對(duì)不同噪聲環(huán)境和不同系統(tǒng)方程的融合準(zhǔn)則優(yōu)劣不同。其次,本文針對(duì)無人艇協(xié)同導(dǎo)航網(wǎng)絡(luò)中的信息來源進(jìn)行分析,并且根據(jù)信息特征建立了合適的數(shù)學(xué)模型進(jìn)行仿真分析。鑒于慣性導(dǎo)航系統(tǒng)在其協(xié)同導(dǎo)航網(wǎng)絡(luò)中的重要地位,本文對(duì)慣性導(dǎo)航系統(tǒng)的原理及建模和仿真進(jìn)行了較為詳盡的分析介紹。通過對(duì)慣性導(dǎo)航系統(tǒng)、GPS全球定位系統(tǒng)、DR航位推算系統(tǒng)和移動(dòng)長基線定位系統(tǒng)的建模和仿真,為后文中信息融合最優(yōu)估計(jì)準(zhǔn)則的算法實(shí)現(xiàn)提供了數(shù)據(jù)來源。最后,本文對(duì)系統(tǒng)導(dǎo)航網(wǎng)絡(luò)中信息融合最優(yōu)估計(jì)準(zhǔn)則進(jìn)行了仿真分析,并且根據(jù)根據(jù)進(jìn)行數(shù)據(jù)融合子系統(tǒng)的特點(diǎn)分別設(shè)計(jì)了不同的卡爾曼濾波器。GPS和慣性導(dǎo)航系統(tǒng)進(jìn)行信息融合時(shí),融合準(zhǔn)則采用線性的卡爾曼濾波器,并且通過仿真分析可以得出對(duì)于線性組合導(dǎo)航系統(tǒng),自適應(yīng)卡爾曼濾波器在計(jì)算量沒有大幅度增加的情況下,可以獲得良好的濾波效果。而對(duì)于GPS和DR信息融合系統(tǒng),則采用非線性的卡爾曼濾波器包括擴(kuò)展卡爾曼濾波器和無跡卡爾曼濾波器。在外部噪聲為高斯白噪聲的情況下,無跡卡爾曼濾波器獲得了良好的濾波效果。但是當(dāng)系統(tǒng)噪聲和量測噪聲的統(tǒng)計(jì)特性不滿足高斯白噪聲的時(shí)候,以上兩種非線性濾波器將出現(xiàn)較大的誤差,甚至導(dǎo)致濾波器的發(fā)散。經(jīng)過信息融合主要技術(shù)的研究和仿真分析可得,針對(duì)無人艇協(xié)同導(dǎo)航網(wǎng)絡(luò)而言,利用多傳感器信息融合技術(shù)可以極大改善其定位精度和導(dǎo)航信息質(zhì)量。
[Abstract]:With the gradual attention of the world to the ocean problem, the surface high motorized unmanned craft has obtained unprecedented high speed development, and will play an important role in the future naval battle. In order to adapt to the more complex future marine combat environment, the cooperative navigation technology of unmanned craft is facing a great challenge compared with the traditional navigation technology. This subject will start with the multi-sensor information fusion technology in the cooperative navigation network. Firstly, the research status of unmanned craft technology in various countries is briefly introduced, and through the research and analysis on the development of unmanned craft in China, we know that, Although China has made great progress in recent years, there is still a big gap between China and the United States and Israel. As far as the cooperative navigation network of unmanned craft is concerned, the method to shorten the gap is not only to improve the sensor accuracy in the cooperative network, but also to improve the optimal estimation criterion in information fusion. In this paper, the structure, information source and fusion criteria of multi-sensor information fusion in cooperative navigation network are analyzed and compared, and the results show that the fusion criteria for different noise environments and different system equations are different. Secondly, this paper analyzes the sources of information in the UAV cooperative navigation network, and establishes a suitable mathematical model for simulation analysis according to the characteristics of the information. In view of the important position of inertial navigation system in its cooperative navigation network, the principle, modeling and simulation of inertial navigation system are analyzed and introduced in detail in this paper. Through the modeling and simulation of inertial navigation system, GPS global positioning system, DR dead reckoning system and mobile long baseline positioning system, this paper provides a data source for the algorithm realization of information fusion optimal estimation criterion. Finally, the optimal estimation criterion of information fusion in system navigation network is simulated and analyzed. According to the characteristics of the data fusion subsystem, different Kalman filters are designed. When GPS and inertial navigation system are fused, the linear Kalman filter is used as the fusion criterion. The simulation results show that the adaptive Kalman filter can obtain a good filtering effect without a large increase in the computational complexity for the linear integrated navigation system. For GPS and DR information fusion systems, the nonlinear Kalman filter includes extended Kalman filter and unscented Kalman filter. When the external noise is Gao Si white noise, the unscented Kalman filter has a good filtering effect. However, when the statistical characteristics of system noise and measurement noise are not satisfied with Gao Si white noise, the above two nonlinear filters will have large errors and even lead to the divergence of filters. Through the research and simulation analysis of the main technology of information fusion, the positioning accuracy and the quality of navigation information can be greatly improved by using multi-sensor information fusion technology for unmanned craft cooperative navigation network.
【學(xué)位授予單位】：哈爾濱工程大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U666.1;TP202;TN96

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