【摘要】：無線傳感器網(wǎng)絡(luò)(WSN)是由灑布在目標(biāo)監(jiān)測(cè)范圍內(nèi)大量微型智能傳感器節(jié)點(diǎn)組成,具有功耗低、體積小、成本低、分布式以及自組織等特點(diǎn)。由于網(wǎng)絡(luò)中傳感器節(jié)點(diǎn)攜帶能量有限,并且在大多數(shù)情況下節(jié)點(diǎn)不能充電或更換,因此低能耗成為無線傳感器網(wǎng)絡(luò)設(shè)計(jì)的主要原則之一,促使不使用訓(xùn)練序列的盲均衡、盲檢測(cè)技術(shù)成為無線傳感器網(wǎng)絡(luò)信號(hào)檢測(cè)技術(shù)的研究方向之一。在項(xiàng)目組前期成果基礎(chǔ)上,本論文主要?jiǎng)?chuàng)新工作如下:(1)本文在研究經(jīng)典基于二階統(tǒng)計(jì)量的線性預(yù)測(cè)算法及其改進(jìn)算法基礎(chǔ)上,結(jié)合遞歸最小二乘算法,提出了一種RLS-MSPA算法,該算法不僅避免了線性預(yù)測(cè)算法中的輸出相關(guān)矩陣求逆問題;并且實(shí)驗(yàn)表明,相比較于線性預(yù)測(cè)算法,該算法具有更優(yōu)的盲檢測(cè)性能。(2)現(xiàn)有文獻(xiàn)給出的無線傳感器網(wǎng)絡(luò)盲檢測(cè)系統(tǒng)的缺陷之一就是速度過慢,簇內(nèi)基準(zhǔn)傳感器節(jié)點(diǎn)盲檢測(cè)速度對(duì)整個(gè)系統(tǒng)至關(guān)重要,為提高系統(tǒng)計(jì)算速度,結(jié)合提出的RLS-MSPA算法,本文構(gòu)建了一種新的RLS-MSPA虛擬MIMO無線傳感器網(wǎng)絡(luò)盲檢測(cè)模型:將RLS-MSPA算法應(yīng)用于簇內(nèi)基準(zhǔn)傳感器節(jié)點(diǎn)和簇外sink節(jié)點(diǎn),簇內(nèi)其它非基準(zhǔn)傳感器節(jié)點(diǎn)則采用互相關(guān)算法恢復(fù)原信號(hào),從系統(tǒng)模型整體角度出發(fā),通過簇內(nèi)和簇外兩層信號(hào)盲檢測(cè)恢復(fù)無線傳感器網(wǎng)絡(luò)中各節(jié)點(diǎn)的發(fā)送數(shù)據(jù)。(3)由于RLS-MSPA是基于二階統(tǒng)計(jì)量的盲檢測(cè)算法,對(duì)含公零點(diǎn)的信道不適用,為進(jìn)一步提高系統(tǒng)對(duì)信道適應(yīng)能力和盲檢測(cè)性能,本文通過引入改進(jìn)的蟻群算法SSAV-QACO應(yīng)用于基準(zhǔn)傳感器節(jié)點(diǎn)信號(hào)盲檢測(cè),提出了改進(jìn)的蟻群算法SSAV-QACO虛擬MIMO無線傳感器網(wǎng)絡(luò)盲檢測(cè)系統(tǒng)。相比較于基準(zhǔn)傳感器節(jié)點(diǎn)采用的RLS-MSPA算法,使用SSAV-QACO算法能夠獲得更優(yōu)的盲檢測(cè)性能,并且能適用于更多傳輸信道,但時(shí)間和空間復(fù)雜度都遠(yuǎn)高于RLS-MSPA算法。(4)為權(quán)衡系統(tǒng)的信道適應(yīng)能力、復(fù)雜度和性能,結(jié)合超混沌預(yù)編碼提出了DS-NSCNN超混沌預(yù)編碼虛擬MIMO無線傳感網(wǎng)盲檢測(cè)系統(tǒng),相比較于RLS-MSPA虛擬MIMO無線傳感網(wǎng)盲檢測(cè)系統(tǒng),該系統(tǒng)能適應(yīng)多數(shù)信道;相比較于SSAV-QACO虛擬MIMO無線傳感網(wǎng)盲檢測(cè)系統(tǒng),該系統(tǒng)具有較快的收斂速度和較低的復(fù)雜度;并且性能較二者都有所提升。
[Abstract]:Wireless sensor network (WSN) is composed of a large number of micro-smart sensor nodes which are sprinkled in the monitoring range of target. WSN has the characteristics of low power consumption, small size, low cost, distributed and self-organization. Because of the limited energy carried by sensor nodes and the fact that nodes can not be recharged or replaced in most cases, low energy consumption becomes one of the main principles of wireless sensor network design, and promotes blind equalization without training sequence. Blind detection technology has become one of the research directions of wireless sensor network signal detection technology. On the basis of the previous achievements of the project team, the main innovations of this paper are as follows: (1) based on the study of the classical linear prediction algorithm based on second-order statistics and its improved algorithm, a new RLS-MSPA algorithm is proposed based on the recursive least squares algorithm. The algorithm not only avoids the inverse problem of the output correlation matrix in the linear prediction algorithm, but also shows that compared with the linear prediction algorithm, The algorithm has better blind detection performance. (2) one of the shortcomings of the blind detection system in wireless sensor networks is that the speed is too slow. The blind detection speed of the reference sensor nodes in the cluster is very important to the whole system. In order to improve the computing speed of the system, a new blind detection model for RLS-MSPA virtual wireless sensor networks is proposed. The RLS-MSPA algorithm is applied to the intra-cluster reference sensor nodes and the out-of-cluster sink nodes. Other non-reference sensor nodes in the cluster use cross-correlation algorithm to recover the original signal, and proceed from the system model as a whole. The transmit data of each node in wireless sensor network is recovered by blind detection of two layers of signals inside and outside the cluster. (3) because RLS-MSPA is a blind detection algorithm based on second-order statistics, it is not suitable for channels with common zeros. In order to improve the channel adaptability and blind detection performance of the system, an improved ant colony algorithm (SSAV-QACO) is introduced to blind signal detection of reference sensor nodes. An improved ant colony algorithm (SSAV-QACO) blind detection system for wireless sensor networks is proposed. Compared with the RLS-MSPA algorithm used in the reference sensor node, the SSAV-QACO algorithm can achieve better blind detection performance and can be applied to more transmission channels. But the complexity of time and space is much higher than that of RLS-MSPA algorithm. (4) in order to balance the channel adaptability, complexity and performance of the system, a DS-NSCNN hyperchaotic precoding virtual MIMO wireless sensor network blind detection system is proposed. Compared with RLS-MSPA virtual MIMO wireless sensor network blind detection system, this system can adapt to most channels, compared with SSAV-QACO virtual MIMO wireless sensor network blind detection system, the system has faster convergence speed and lower complexity. And the performance is improved compared with both.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP212.9;TN929.5

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本文編號(hào)：2134726