本文關(guān)鍵詞：基于CKF的WSN目標(biāo)定位跟蹤技術(shù)研究　出處：《西南交通大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 無線傳感器網(wǎng)絡(luò) 定位 目標(biāo)跟蹤 容積卡爾曼濾波

【摘要】：目標(biāo)定位跟蹤技術(shù)是無線傳感器網(wǎng)絡(luò)的關(guān)鍵技術(shù)之一�，F(xiàn)有WSN目標(biāo)定位跟蹤算法存在精度不高、能耗過大、計(jì)算復(fù)雜性過高等缺點(diǎn),如何提高定位跟蹤精度、降低計(jì)算復(fù)雜性是當(dāng)前國(guó)內(nèi)外研究的熱點(diǎn)。WSN節(jié)點(diǎn)定位技術(shù)是目標(biāo)跟蹤技術(shù)的基礎(chǔ),只有當(dāng)WSN的所有節(jié)點(diǎn)定位出自身位置,才能利用WSN有效地進(jìn)行目標(biāo)跟蹤。為此,本文重點(diǎn)針對(duì)基于CKF的WSN節(jié)點(diǎn)定位算法和目標(biāo)跟蹤算法展開了深入的研究。首先,本文全面分析和總結(jié)了靜止WSN和移動(dòng)WSN目標(biāo)定位跟蹤技術(shù)的相關(guān)國(guó)內(nèi)外研究現(xiàn)狀,分析了定位算法和目標(biāo)跟蹤算法的性能評(píng)價(jià)指標(biāo),為本文的后續(xù)研究奠定了扎實(shí)基礎(chǔ)。其次,詳細(xì)對(duì)比分析了擴(kuò)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(jì)精度,并通過仿真分析了UKF和CKF算法,結(jié)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(duì)無線傳感器網(wǎng)絡(luò)節(jié)點(diǎn)定位技術(shù),為提高定位精度、降低計(jì)算復(fù)雜性,提出了一種基于CKF的無線傳感器網(wǎng)絡(luò)分布式定位算法,同時(shí)在考慮節(jié)點(diǎn)移動(dòng)性的基礎(chǔ)上,提出了基于CKF的移動(dòng)無線傳感器網(wǎng)絡(luò)分布式定位算法。仿真結(jié)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(dāng),高于極大似然估計(jì)定位法,計(jì)算復(fù)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(yùn)動(dòng)、勻加速運(yùn)動(dòng)和恒速轉(zhuǎn)彎三種目標(biāo)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(wǎng)絡(luò)目標(biāo)跟蹤算法,并通過仿真實(shí)驗(yàn)驗(yàn)證了該算法的有效性。最后,總結(jié)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(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TN929.5;TP212.9

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本文編號(hào)：1415511