本文關鍵詞：基于CKF的WSN目標定位跟蹤技術研究　出處：《西南交通大學》2015年碩士論文　論文類型：學位論文

  更多相關文章： 無線傳感器網絡 定位 目標跟蹤 容積卡爾曼濾波

【摘要】：目標定位跟蹤技術是無線傳感器網絡的關鍵技術之一�，F有WSN目標定位跟蹤算法存在精度不高、能耗過大、計算復雜性過高等缺點,如何提高定位跟蹤精度、降低計算復雜性是當前國內外研究的熱點。WSN節(jié)點定位技術是目標跟蹤技術的基礎,只有當WSN的所有節(jié)點定位出自身位置,才能利用WSN有效地進行目標跟蹤。為此,本文重點針對基于CKF的WSN節(jié)點定位算法和目標跟蹤算法展開了深入的研究。首先,本文全面分析和總結了靜止WSN和移動WSN目標定位跟蹤技術的相關國內外研究現狀,分析了定位算法和目標跟蹤算法的性能評價指標,為本文的后續(xù)研究奠定了扎實基礎。其次,詳細對比分析了擴展卡爾曼濾波、無跡卡爾曼濾波、粒子濾波和容積卡爾曼濾波四種非線性濾波算法,仿真比較了四種算法的一維非線性估計精度,并通過仿真分析了UKF和CKF算法,結果表明：在三維以下,UKF算法優(yōu)于CKF；三維及以上,CKF算法優(yōu)于UKF。然后,針對無線傳感器網絡節(jié)點定位技術,為提高定位精度、降低計算復雜性,提出了一種基于CKF的無線傳感器網絡分布式定位算法,同時在考慮節(jié)點移動性的基礎上,提出了基于CKF的移動無線傳感器網絡分布式定位算法。仿真結果表明：基于CKF的WSN分布式定位算法的定位精度與UKF算法相當,高于極大似然估計定位法,計算復雜性低于UKF算法；基于CKF的MWSN定位算法的定位精度高于WSN定位算法。接著,介紹了勻速運動、勻加速運動和恒速轉彎三種目標跟蹤模型,研究了基于卡爾曼濾波的交互式多模型算法,提出了一種基于CKF的無線傳感器網絡目標跟蹤算法,并通過仿真實驗驗證了該算法的有效性。最后,總結本文開展的研究工作,并指出未來的研究方向。
[Abstract]:Target tracking technology is one of the key technologies in wireless sensor networks. The existing WSN algorithm of target tracking accuracy is not high, the energy consumption is too large, the computational complexity is too high shortcomings, how to improve the tracking accuracy, reduce the computational complexity of.WSN node localization technology is the current hot research at home and abroad is the basis of target tracking, only when all node localization WSN out of their own position, in order to effectively use WSN to track the target. Therefore, this paper focuses on the WSN localization algorithm and target tracking algorithm based on CKF is researched. Firstly, this paper analyzes and summarizes the tracking technology of static WSN and mobile target positioning WSN related analysis at home and abroad. The performance evaluation index of localization algorithm and target tracking algorithm, which lays a solid foundation for the follow-up research. Secondly, with the analysis The extended Calman filter, unscented particle filter and Calman filter, Calman filter volume four nonlinear filtering algorithm, simulation comparison of four algorithms in a one-dimensional nonlinear estimation accuracy are analyzed by simulation of UKF and CKF algorithm, the results show that: in the following three, UKF algorithm is better than CKF; three and above, the CKF algorithm is better than UKF. then, according to the wireless sensor network node positioning technology, to improve the positioning precision, reduce the computational complexity, proposes a distributed localization algorithm in wireless sensor network based on CKF, at the same time, considering the node mobility, the mobile wireless sensor network distributed localization algorithm based on CKF. The simulation results show that the positioning accuracy and UKF algorithm WSN distributed localization algorithm CKF is based on the above estimation method and the maximum likelihood, the computational complexity is lower than that of the UKF algorithm based on MWS CKF; The positioning accuracy of N positioning algorithm than WSN localization algorithm. Then, introduces the uniform motion, uniform acceleration and constant turn three target tracking model, study the interactive multiple model algorithm based on Calman filter, proposes an algorithm of wireless sensor networks target tracking based on CKF, and verify the effectiveness of the algorithm through the simulation experiment. Finally, summarize the research work carried out in this article, and pointed out the direction of future research.

【學位授予單位】：西南交通大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TN929.5;TP212.9

【參考文獻】

相關期刊論文 前1條

1 劉欽;劉崢;劉韻佛;謝榮;;多傳感器優(yōu)化部署下的機動目標協(xié)同跟蹤算法[J];系統(tǒng)工程與電子技術;2013年02期

，

本文編號：1415511