【摘要】：無線傳感器網(wǎng)絡(WSN)是由灑布在目標監(jiān)測范圍內(nèi)大量微型智能傳感器節(jié)點組成,具有功耗低、體積小、成本低、分布式以及自組織等特點。由于網(wǎng)絡中傳感器節(jié)點攜帶能量有限,并且在大多數(shù)情況下節(jié)點不能充電或更換,因此低能耗成為無線傳感器網(wǎng)絡設計的主要原則之一,促使不使用訓練序列的盲均衡、盲檢測技術(shù)成為無線傳感器網(wǎng)絡信號檢測技術(shù)的研究方向之一。在項目組前期成果基礎上,本論文主要創(chuàng)新工作如下:(1)本文在研究經(jīng)典基于二階統(tǒng)計量的線性預測算法及其改進算法基礎上,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測算法中的輸出相關(guān)矩陣求逆問題;并且實驗表明,相比較于線性預測算法,該算法具有更優(yōu)的盲檢測性能。(2)現(xiàn)有文獻給出的無線傳感器網(wǎng)絡盲檢測系統(tǒng)的缺陷之一就是速度過慢,簇內(nèi)基準傳感器節(jié)點盲檢測速度對整個系統(tǒng)至關(guān)重要,為提高系統(tǒng)計算速度,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)絡盲檢測模型:將RLS-MSPA算法應用于簇內(nèi)基準傳感器節(jié)點和簇外sink節(jié)點,簇內(nèi)其它非基準傳感器節(jié)點則采用互相關(guān)算法恢復原信號,從系統(tǒng)模型整體角度出發(fā),通過簇內(nèi)和簇外兩層信號盲檢測恢復無線傳感器網(wǎng)絡中各節(jié)點的發(fā)送數(shù)據(jù)。(3)由于RLS-MSPA是基于二階統(tǒng)計量的盲檢測算法,對含公零點的信道不適用,為進一步提高系統(tǒng)對信道適應能力和盲檢測性能,本文通過引入改進的蟻群算法SSAV-QACO應用于基準傳感器節(jié)點信號盲檢測,提出了改進的蟻群算法SSAV-QACO虛擬MIMO無線傳感器網(wǎng)絡盲檢測系統(tǒng)。相比較于基準傳感器節(jié)點采用的RLS-MSPA算法,使用SSAV-QACO算法能夠獲得更優(yōu)的盲檢測性能,并且能適用于更多傳輸信道,但時間和空間復雜度都遠高于RLS-MSPA算法。(4)為權(quán)衡系統(tǒng)的信道適應能力、復雜度和性能,結(jié)合超混沌預編碼提出了DS-NSCNN超混沌預編碼虛擬MIMO無線傳感網(wǎng)盲檢測系統(tǒng),相比較于RLS-MSPA虛擬MIMO無線傳感網(wǎng)盲檢測系統(tǒng),該系統(tǒng)能適應多數(shù)信道;相比較于SSAV-QACO虛擬MIMO無線傳感網(wǎng)盲檢測系統(tǒng),該系統(tǒng)具有較快的收斂速度和較低的復雜度;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.
【學位授予單位】：南京郵電大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP212.9;TN929.5

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