本文選題：無(wú)線傳感器網(wǎng)絡(luò)　切入點(diǎn)：目標(biāo)跟蹤　出處：《南京郵電大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：無(wú)線傳感器網(wǎng)絡(luò)是由隨機(jī)部署在監(jiān)控區(qū)域內(nèi)的大量廉價(jià)微型傳感器節(jié)點(diǎn),通過(guò)無(wú)線通信方式組成的多跳自組織網(wǎng)絡(luò)系統(tǒng),它可以實(shí)時(shí)采集、處理和傳輸網(wǎng)絡(luò)覆蓋區(qū)域內(nèi)被感知對(duì)象的信息,并把這些信息發(fā)送給用戶。WSN被廣泛應(yīng)用于軍事、智能交通、環(huán)境監(jiān)控和醫(yī)療護(hù)理等多個(gè)領(lǐng)域,其中目標(biāo)跟蹤是無(wú)線傳感器網(wǎng)絡(luò)最具代表性的應(yīng)用之一。由于傳感器節(jié)點(diǎn)稠密分布,如果將檢測(cè)到目標(biāo)的所有傳感器節(jié)點(diǎn)都用于目標(biāo)跟蹤,雖然跟蹤精度很高,但是能量消耗降低了網(wǎng)絡(luò)生存周期。傳感器節(jié)點(diǎn)選擇算法就是從候選傳感器集合中選擇節(jié)點(diǎn)子集,在滿足目標(biāo)跟蹤性能的同時(shí)盡可能降低網(wǎng)絡(luò)能量消耗,延長(zhǎng)網(wǎng)絡(luò)生存周期。針對(duì)不同目標(biāo)跟蹤環(huán)境,提出幾種節(jié)點(diǎn)選擇算法。主要工作和貢獻(xiàn)如下:1.對(duì)機(jī)動(dòng)性不強(qiáng)的運(yùn)動(dòng)目標(biāo),提出基于擴(kuò)展Kalman濾波的多步預(yù)測(cè)的節(jié)點(diǎn)選擇算法。以多步狀態(tài)預(yù)測(cè)誤差協(xié)方差矩陣行列式的加權(quán)為目標(biāo)函數(shù),從候選傳感器節(jié)點(diǎn)集合選擇一組節(jié)點(diǎn)最大化該目標(biāo)函數(shù)。在均方根誤差和平均剩余能量方面,通過(guò)仿真比較該算法與基于擴(kuò)展Kalman濾波一步預(yù)測(cè)的節(jié)點(diǎn)選擇算法。2.針對(duì)目標(biāo)機(jī)動(dòng)特性和傳感器測(cè)量噪聲統(tǒng)計(jì)特性未知情況下的目標(biāo)跟蹤問(wèn)題,利用擴(kuò)展H∞濾波算法與擴(kuò)展Kalman濾波算法的形式相類似,提出擴(kuò)展H∞濾波的類Cramer-Rao下界的節(jié)點(diǎn)選擇算法。并在跟蹤性能和能量消耗方面將該算法與隨機(jī)節(jié)點(diǎn)選擇算法和基于目標(biāo)預(yù)測(cè)位置最近鄰的節(jié)點(diǎn)選擇方法進(jìn)行仿真比較。3.在集中式無(wú)線傳感器網(wǎng)絡(luò)目標(biāo)跟蹤中,簇頭節(jié)點(diǎn)在跟蹤過(guò)程中消耗大量通信和計(jì)算能量,容易造成頭節(jié)點(diǎn)的失效問(wèn)題,基于擴(kuò)展Kalman濾波提出順序處理的目標(biāo)跟蹤算法,將數(shù)據(jù)處理和通信分配到跟蹤簇集內(nèi)各個(gè)傳感器上,不需要頭節(jié)點(diǎn)接收和集中處理其他成員傳感器節(jié)點(diǎn)的測(cè)量數(shù)據(jù)。在算法計(jì)算時(shí)間和跟蹤性能兩方面比較算法優(yōu)劣�？傊�,本文圍繞無(wú)線傳感器網(wǎng)絡(luò)目標(biāo)跟蹤中的節(jié)點(diǎn)選擇問(wèn)題展開(kāi)研究,所得結(jié)果不僅具有重要的理論價(jià)值,而且具有廣泛的實(shí)際應(yīng)用價(jià)值。
[Abstract]:Wireless sensor network (WSN) is a multi-hop ad hoc network system, which is composed of a large number of cheap sensor nodes deployed randomly in the monitoring area, and can be collected in real time. It processes and transmits the information of perceived objects in the area covered by the network, and sends the information to the user. WSN is widely used in many fields, such as military, intelligent transportation, environmental monitoring and medical care, etc. Target tracking is one of the most representative applications in wireless sensor networks. Because of the dense distribution of sensor nodes, if all sensor nodes detected the target are used for target tracking, although the tracking accuracy is very high, However, the energy consumption reduces the lifetime of the network. The sensor node selection algorithm is to select the node subset from the candidate sensor set, which can not only meet the target tracking performance, but also reduce the network energy consumption as much as possible. Several node selection algorithms are proposed for different target tracking environments. The main work and contributions are as follows: 1. A node selection algorithm for multistep prediction based on extended Kalman filter is proposed. The objective function is the weighting of the determinant of multistep state prediction error covariance matrix. Select a set of nodes from the set of candidate sensor nodes to maximize the objective function. The algorithm is compared with the node selection algorithm based on one-step prediction of extended Kalman filter by simulation. 2. Aiming at the target tracking problem when the target maneuvering characteristics and the statistical characteristics of sensor measurement noise are unknown, The form of extended H 鈭，

本文編號(hào)：1632352