【摘要】：移動(dòng)傳感器網(wǎng)絡(luò)是由無(wú)線傳感器網(wǎng)絡(luò)節(jié)點(diǎn)配以移動(dòng)平臺(tái)組合而成，故而移動(dòng)傳感器網(wǎng)絡(luò)在具有網(wǎng)絡(luò)特性的同時(shí)增加了機(jī)動(dòng)性，這有效地?cái)U(kuò)大了傳感器節(jié)點(diǎn)的作用范圍，通過(guò)少數(shù)節(jié)點(diǎn)的不斷移動(dòng)就有望實(shí)現(xiàn)部署大量靜止節(jié)點(diǎn)達(dá)到的效果。移動(dòng)傳感器網(wǎng)絡(luò)在有毒/有害氣體泄露檢測(cè)、火源探測(cè)和災(zāi)后搜救等方面具有廣闊的應(yīng)用前景。 本文圍繞移動(dòng)傳感器網(wǎng)絡(luò)及其在氣體泄漏源定位中的應(yīng)用問(wèn)題，重點(diǎn)開(kāi)展了如下幾個(gè)方面的研究工作。 首先，根據(jù)移動(dòng)傳感器網(wǎng)絡(luò)對(duì)節(jié)點(diǎn)的體積小、價(jià)格便宜等實(shí)際要求，設(shè)計(jì)了適用于氣體源定位的移動(dòng)節(jié)點(diǎn)。并針對(duì)此移動(dòng)傳感器網(wǎng)絡(luò)，給出了節(jié)點(diǎn)的運(yùn)動(dòng)控制方法，具有自恢復(fù)功能的網(wǎng)絡(luò)協(xié)議以及可抑制零漂的目標(biāo)傳感器標(biāo)定措施。 其次，給出了一種分布式的基于聲音的移動(dòng)節(jié)點(diǎn)相對(duì)定位算法，并通過(guò)理論和實(shí)驗(yàn)證明：在聲音傳感范圍內(nèi)的多個(gè)節(jié)點(diǎn)，只要有兩個(gè)節(jié)點(diǎn)相繼發(fā)聲，即可實(shí)現(xiàn)所有節(jié)點(diǎn)之間的相互定位。所提算法的方向估計(jì)的過(guò)程中對(duì)卷積的加窗運(yùn)算有效地減小了計(jì)算量；同時(shí)聲源距離估計(jì)中采集信號(hào)之間的代數(shù)運(yùn)算有效地降低了環(huán)境中的隨機(jī)噪聲。 再次，提出了一種使用固定拓?fù)涞囊苿?dòng)傳感器網(wǎng)絡(luò)對(duì)氣體源進(jìn)行定位的方法，所提定位算法中移動(dòng)節(jié)點(diǎn)不需對(duì)風(fēng)信息進(jìn)行實(shí)時(shí)測(cè)量。該算法可以看作是一個(gè)通過(guò)有限移動(dòng)節(jié)點(diǎn)的傳感器網(wǎng)絡(luò)的動(dòng)態(tài)部署實(shí)現(xiàn)對(duì)氣體源進(jìn)行估計(jì)的過(guò)程。在時(shí)均風(fēng)恒定的環(huán)境下，每個(gè)估計(jì)周期分兩步，首先對(duì)環(huán)境中的氣體進(jìn)行一段時(shí)間的測(cè)量，根據(jù)測(cè)量的濃度均值使用最小二乘法對(duì)氣體源位置參數(shù)進(jìn)行估計(jì)；接著在朝向估計(jì)值的方向上選擇一點(diǎn)作為網(wǎng)絡(luò)的幾何中心，利用飽和控制的方法使節(jié)點(diǎn)再重新部署網(wǎng)絡(luò)。另外，，在風(fēng)隨機(jī)變化的環(huán)境但沒(méi)有風(fēng)傳感器的情況下，基于固定拓?fù)涞囊苿?dòng)節(jié)點(diǎn)采集得到的煙羽結(jié)構(gòu)信息，提出了一種煙羽跟蹤方法。所提方法的有效性通過(guò)仿真及實(shí)驗(yàn)得到了驗(yàn)證。 最后，提出了一種基于條件信息熵，使用網(wǎng)絡(luò)連接受限的移動(dòng)傳感器網(wǎng)絡(luò)進(jìn)行氣體源定位算法。該算法中使用分布式粒子濾波近似計(jì)算條件信息熵及其梯度。并使用條件信息熵梯度構(gòu)建了移動(dòng)節(jié)點(diǎn)的控制律，控制節(jié)點(diǎn)沿條件信息熵負(fù)梯度方向運(yùn)動(dòng)，從而減小條件信息熵，增加對(duì)氣體源位置估計(jì)的確定性。移動(dòng)節(jié)點(diǎn)使用該算法可以實(shí)現(xiàn)在風(fēng)不斷變化或者存在障礙物的環(huán)境下的氣體源定位。不同環(huán)境的仿真表明了該算法的有效性。
[Abstract]:The mobile sensor network is composed of wireless sensor network nodes and mobile platform, so the mobile sensor network not only has the network characteristics, but also increases the maneuverability, which effectively expands the scope of the sensor nodes. Through the continuous movement of a small number of nodes, it is expected to achieve the effect of deploying a large number of static nodes. Mobile sensor networks have broad application prospects in toxic / harmful gas leakage detection, fire source detection and post-disaster search and rescue. In this paper, focusing on the mobile sensor network and its application in gas leakage source location, the following research work has been carried out. First of all, according to the actual requirements of mobile sensor networks, such as small size and low price, a mobile node suitable for gas source location is designed. Aiming at this mobile sensor network, the motion control method of the node, the network protocol with self-recovery function and the calibration measure of the target sensor which can restrain the zero drift are given. Secondly, a distributed relative localization algorithm of mobile nodes based on sound is proposed, and it is proved by theory and experiment that as long as there are two nodes in the range of sound sensing, the mutual location between all nodes can be realized. In the process of direction estimation of the proposed algorithm, the windowing operation of convolution effectively reduces the amount of computation, and the algebra operation between the collected signals in the estimation of sound source distance effectively reduces the random noise in the environment. Thirdly, a fixed topology mobile sensor network is proposed to locate the gas source. In the proposed location algorithm, the mobile node does not need to measure the wind information in real time. The algorithm can be regarded as a process of gas source estimation through the dynamic deployment of sensor networks with limited mobile nodes. In the environment with constant time-averaged wind, each estimation period is divided into two steps. Firstly, the gas in the environment is measured for a period of time, and the position parameters of the gas source are estimated by the least square method according to the measured concentration mean value. Then, a point is selected as the geometric center of the network in the direction of the estimated value, and the node is redeployed by the method of saturation control. In addition, in the case of randomly changing wind environment but no wind sensor, a plume tracking method is proposed based on the fixed topology of the smoke structure information collected by the mobile node. The effectiveness of the proposed method is verified by simulation and experiments. Finally, a gas source location algorithm based on conditional information entropy and limited network connection is proposed. In this algorithm, distributed particle filtering is used to approximate the conditional information entropy and its gradient. The control law of the mobile node is constructed by using the conditional information entropy gradient, and the control node moves along the negative gradient direction of the conditional information entropy, thus reducing the conditional information entropy and increasing the certainty of the position estimation of the gas source. The mobile node can use this algorithm to locate the gas source in the environment where the wind is constantly changing or there are obstacles. The simulation results in different environments show the effectiveness of the algorithm.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TP212.9;TN929.5

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