發(fā)布時(shí)間：2018-06-15 10:32

  本文選題：無(wú)線傳感器網(wǎng)絡(luò) + 自適應(yīng)卡爾曼濾波器��； 參考：《南京大學(xué)》2017年博士論文

【摘要】：在無(wú)線傳感器網(wǎng)絡(luò)的許多應(yīng)用中,例如環(huán)境監(jiān)測(cè)和室內(nèi)定位等,無(wú)線傳感器節(jié)點(diǎn)需要被部署在一個(gè)復(fù)雜的環(huán)境中。無(wú)線傳感器網(wǎng)絡(luò)是一種自組織類型的網(wǎng)絡(luò),節(jié)點(diǎn)之間通常沒(méi)有固定的通信鏈路,它們是以隨機(jī)多跳的方式將采集的數(shù)據(jù)傳回給匯聚節(jié)點(diǎn)的。對(duì)于隨機(jī)部署的無(wú)線傳感器網(wǎng)絡(luò)而言,通常不知道采集數(shù)據(jù)節(jié)點(diǎn)的準(zhǔn)確位置。因此,定位技術(shù)是無(wú)線傳感器網(wǎng)絡(luò)研究領(lǐng)域的熱點(diǎn)之一。然而,由于無(wú)線傳感器網(wǎng)絡(luò)部署環(huán)境的復(fù)雜性,獲得準(zhǔn)確的節(jié)點(diǎn)位置仍然是一項(xiàng)具有挑戰(zhàn)性的工作。本文圍繞復(fù)雜環(huán)境下無(wú)線傳感器網(wǎng)絡(luò)定位研究了基于噪聲自適應(yīng)卡爾曼濾波的傳感網(wǎng)定位精化、魯棒的傳感網(wǎng)噪聲自適應(yīng)卡爾曼濾波定位和多徑環(huán)境下傳感網(wǎng)噪聲自適應(yīng)卡爾曼濾波指紋定位優(yōu)化這三個(gè)問(wèn)題。(1)考慮到無(wú)線傳感器網(wǎng)絡(luò)是一種能量、計(jì)算能力、通信帶寬、成本等資源受限的網(wǎng)絡(luò),通常,在節(jié)點(diǎn)之間最容易獲取的接收信號(hào)強(qiáng)度被用來(lái)確定無(wú)線傳感器網(wǎng)絡(luò)中節(jié)點(diǎn)之間的距離,進(jìn)而再根據(jù)多個(gè)節(jié)點(diǎn)之間的相對(duì)距離獲得節(jié)點(diǎn)在網(wǎng)絡(luò)中的相對(duì)位置。然而,接收信號(hào)強(qiáng)度的測(cè)量噪聲會(huì)使這種方法的定位精度變差。為了降低測(cè)量噪聲對(duì)定位精度的影響,卡爾曼濾波器被利用來(lái)精化定位的結(jié)果。由于在實(shí)際的部署環(huán)境中,噪聲的統(tǒng)計(jì)特性往往是未知或時(shí)變的,所以我們首先提出一種基于現(xiàn)有自適應(yīng)擴(kuò)展卡爾曼濾波器的無(wú)線傳感器網(wǎng)絡(luò)多維定標(biāo)定位精化算法。然后,為了進(jìn)一步提高精化的效果,我們從自適應(yīng)擴(kuò)展卡爾曼濾波器推導(dǎo)出了一種新的自適應(yīng)無(wú)跡卡爾曼濾波器,并且基于這種新的濾波器提出了精度更高的無(wú)線傳感器網(wǎng)絡(luò)多維定標(biāo)定位精化算法。前一種算法具有相對(duì)更小的計(jì)算復(fù)雜度,而后一種算法則具有相對(duì)更高的定位精度。同時(shí),我們還提出了一種專門用于精化噪聲環(huán)境下無(wú)線傳感器網(wǎng)絡(luò)指紋定位結(jié)果的自適應(yīng)指紋卡爾曼濾波器。廣泛的實(shí)驗(yàn)結(jié)果表明不論噪聲是已知還是未知、是否隨時(shí)間改變,我們提出的算法均能夠較好地改善傳統(tǒng)卡爾曼濾波器對(duì)無(wú)線傳感器網(wǎng)絡(luò)定位結(jié)果的精化效果。(2)卡爾曼濾波器是通過(guò)無(wú)線傳感器節(jié)點(diǎn)的過(guò)程模型和測(cè)量模型來(lái)估計(jì)節(jié)點(diǎn)的位置、速度等狀態(tài)信息。通常,由于一些外部和內(nèi)部的干擾因素,在建立的過(guò)程模型和測(cè)量模型里分別會(huì)包含表示過(guò)程噪聲和測(cè)量噪聲的隨機(jī)變量。目前,如果這兩種噪聲的統(tǒng)計(jì)特性同時(shí)隨時(shí)間改變時(shí),現(xiàn)有自適應(yīng)卡爾曼濾波器估計(jì)的結(jié)果會(huì)出現(xiàn)較大的偏差,甚至出現(xiàn)濾波器無(wú)法工作失去魯棒性的情況。為了提高基于自適應(yīng)卡爾曼濾波器的無(wú)線傳感器網(wǎng)絡(luò)定位算法的精度和魯棒性,我們首先提出了一種魯棒的自適應(yīng)擴(kuò)展卡爾曼濾波器。然后,在這個(gè)魯棒濾波器的基礎(chǔ)上,進(jìn)一步推導(dǎo)出了精度更高的魯棒自適應(yīng)無(wú)跡卡爾曼濾波器。另外,這兩種新提出濾波器的魯棒性被從理論上進(jìn)行了嚴(yán)格地證明。在過(guò)程噪聲和測(cè)量噪聲都時(shí)變的情況下,大量仿真實(shí)驗(yàn)的結(jié)果表明我們提出的這兩種卡爾曼濾波器能夠確保無(wú)線傳感器網(wǎng)絡(luò)中的節(jié)點(diǎn)位置被魯棒和準(zhǔn)確地估計(jì)。(3)目前基于自適應(yīng)卡爾曼濾波器的無(wú)線傳感器網(wǎng)絡(luò)指紋定位研究主要是關(guān)注如何提高算法的精度和魯棒性,還很少有關(guān)于指紋定位在噪聲和多徑的環(huán)境下如何獲得最優(yōu)節(jié)點(diǎn)位置估計(jì)的研究。因此,我們首先利用自適應(yīng)卡爾曼濾波器和多目標(biāo)演化算法優(yōu)化了噪聲環(huán)境下的指紋定位結(jié)果。由于自適應(yīng)卡爾曼濾波器只能過(guò)濾掉一部分接收信號(hào)強(qiáng)度的測(cè)量噪聲,所以過(guò)濾后的接收信號(hào)強(qiáng)度仍然殘留一個(gè)小的噪聲,這會(huì)影響指紋定位精化的效果。為了將殘留噪聲對(duì)定位結(jié)果的影響降低到最小,我們又使用多目標(biāo)演化算法進(jìn)一步優(yōu)化了噪聲環(huán)境下的指紋定位結(jié)果。由于在多徑環(huán)境下現(xiàn)有RSSI(received signal strength indication)測(cè)距模型會(huì)導(dǎo)致優(yōu)化過(guò)程中用于計(jì)算節(jié)點(diǎn)位置估計(jì)的指紋權(quán)重與位置權(quán)重不匹配,所以為了獲得更好的指紋定位優(yōu)化結(jié)果,我們根據(jù)多徑環(huán)境下的信號(hào)強(qiáng)度與距離關(guān)系表達(dá)式推導(dǎo)出了多信道加權(quán)RSSI的測(cè)距模型。廣泛的實(shí)驗(yàn)結(jié)果表明新建立的多目標(biāo)演化模型和多信道加權(quán)RSSI測(cè)距模型能夠使自適應(yīng)卡爾曼濾波指紋定位在噪聲和多徑環(huán)境下獲得更優(yōu)的節(jié)點(diǎn)位置估計(jì)結(jié)果。
[Abstract]:In many applications of wireless sensor networks, such as environmental monitoring and indoor positioning, wireless sensor nodes need to be deployed in a complex environment. Wireless sensor networks are self-organized networks with no fixed communication links between nodes, and they transmit the collected data in a random multi hop manner. Back to converging nodes. For random deployed wireless sensor networks, it is usually not known to acquire the exact location of data nodes. Therefore, location technology is one of the hotspots in the research field of wireless sensor networks. However, due to the complexity of the deployment environment of wireless sensor networks, the acquisition of accurate node location is still a one. Challenging work. This paper focuses on the localization refinement of sensor networks based on noise adaptive Calman filtering, robust sensing network noise adaptive Calman filtering positioning and adaptive Calman filter fingerprint localization optimization of sensing network noise under multi path environment around the complex environment of wireless sensor network location. (1) consideration of the three problems. Wireless sensor networks (WSN) is a network with limited resources, such as energy, computing power, communication bandwidth, cost, and so on. Usually, the most easily acquired signal intensity between nodes is used to determine the distance between nodes in the wireless sensor network, and then the relative distance between multiple nodes is obtained by the relative distance of multiple nodes in the network. However, the measurement noise that receives the signal intensity will make the positioning accuracy of this method worse. In order to reduce the effect of measurement noise on positioning accuracy, Calman filter is used to refinement the result of positioning. Because the statistical characteristics of noise are often unknown or time-varying in the actual deployment environment, we first propose a one. Based on the existing adaptive extended Calman filter, a multi-dimensional scaling localization algorithm for wireless sensor networks is proposed. Then, in order to further improve the effect of the refinement, we derive a new adaptive Untraced Calman filter from the adaptive extended Calman filter, and the precision is more accurate based on this new filter. The previous algorithm has a relatively smaller computational complexity and the latter has a relatively higher positioning accuracy. At the same time, we also propose an adaptive fingerprint Calman filter for the fingerprint localization results of wireless sensor networks in the noise environment. A wide range of experimental results show that whether the noise is known or unknown or not, our proposed algorithm can better improve the refinement effect of the traditional Calman filter on wireless sensor network location results. (2) the Calman filter is estimated by the process model and the measurement model of the wireless sensor nodes. The position, speed, and other state information of a node. Usually, due to some external and internal interference factors, the process model and the measurement model will contain random variables that represent process noise and measurement noise respectively. At present, if the statistical properties of these two kinds of noise are changed with time, the existing adaptive Calman filter can be used. In order to improve the accuracy and robustness of the wireless sensor network location algorithm based on adaptive Calman filter, we first propose a robust adaptive spread spreading Calman filter. Then, the robust filtering is used to improve the robustness and robustness of the wireless sensor network location algorithm. On the basis of the device, a more accurate robust adaptive Untraced Calman filter is derived. In addition, the robustness of the two new proposed filters is strictly proved in theory. The results of a large number of simulation experiments show that the two kinds of Calman filters have been proposed in the case of varying process noise and measurement noise. The wave device can ensure that the node location in the wireless sensor network is robust and accurate. (3) the current research on fingerprint location of wireless sensor networks based on adaptive Calman filter is mainly concerned with how to improve the accuracy and robustness of the algorithm, and few about how to get the best fingerprint location in the environment of noise and multipath. Therefore, we use adaptive Calman filter and multi-objective evolutionary algorithm to optimize the result of fingerprint localization in noisy environment. Since adaptive Calman filter can only filter a part of the measured noise of the received signal intensity, the intensity of the received signal is still remaining after the filter. In order to minimize the impact of residual noise on the positioning results, we use multi target evolution algorithm to further optimize the result of fingerprint localization in noisy environment. Because of the existing RSSI (received signal strength indication) range model in the multipath environment, it will lead to the result. In order to obtain better fingerprint location optimization results, we derive a range model for multi channel weighted RSSI based on the expression of signal intensity and distance in multipath environment. The evolutionary model and the multi channel weighted RSSI range finding model can make adaptive Calman filter fingerprint localization in the noise and multipath environment to obtain better node position estimation results.
【學(xué)位授予單位】：南京大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP212.9;TN929.5

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