WSN中機場噪聲壓縮感知算法研究
發(fā)布時間：2018-11-08 19:56
【摘要】：機場噪聲監(jiān)測環(huán)境中,傳統(tǒng)監(jiān)測模式成本高、安裝環(huán)境要求高、監(jiān)測點較少,無法實現(xiàn)對機場噪聲的全面精確測量。無線傳感器網(wǎng)絡是由分布在監(jiān)測區(qū)域的大量廉價的傳感器節(jié)點組成的一個多跳自組織網(wǎng)絡,可實現(xiàn)對目標的全方位、全天候的監(jiān)測。與傳統(tǒng)的網(wǎng)絡相比,無線傳感器網(wǎng)絡是一個能量有限的網(wǎng)絡。如何在保證數(shù)據(jù)準確性的前提下盡可能地減少網(wǎng)絡的資源消耗,是首先需要關注的問題。數(shù)據(jù)融合技術作為一種可以降低網(wǎng)絡能耗,延長網(wǎng)絡生存時間的技術,是近幾年研究的熱點。壓縮感知理論作為一種新型的數(shù)據(jù)融合技術,打破了傳統(tǒng)的信號采樣定律的限制,提供了一種從較少的采樣數(shù)據(jù)中高概率地恢復原始信號的方法,可以大大減少感知數(shù)據(jù)的冗余信息,從而減少網(wǎng)絡傳輸數(shù)據(jù)量,被廣泛應用于多個領域。論文首先對幾種經(jīng)典的信號重構貪婪算法:正交匹配追蹤算法、子空間追蹤算法、稀疏自適應匹配追蹤算法、前后追蹤算法進行比較分析,并在前后追蹤算法基礎上,提出了一種改進的線性變步長前后追蹤算法,該算法結合稀疏自適應匹配追蹤算法的分階段、變步長的思想,將算法運行時間分為大步長靠近最優(yōu)解與小步長逼近最優(yōu)解兩階段,在不同的階段使用不同的步長,大步長可以降低算法運行時間,小步長可以提高算法重構性能。針對機場環(huán)境下的噪聲監(jiān)測問題,論文提出一種基于時空相關性的基站分類聚簇壓縮感知算法。在該算法中,基站結合機場特殊環(huán)境及節(jié)點位置信息,采用均衡分類技術進行迭代分簇并廣播分簇信息;節(jié)點根據(jù)感知信號在時域上的稀疏性進行壓縮,簇首節(jié)點根據(jù)簇內節(jié)點的空間相關性進一步壓縮并轉發(fā);基站采用線性變步長前后追蹤算法對信號進行重構,還原原始信號。仿真結果表明,該算法能夠明顯提升網(wǎng)絡的聚簇性能,均衡網(wǎng)絡分簇大小,減少網(wǎng)絡數(shù)據(jù)量,平衡網(wǎng)絡傳輸能耗,提升網(wǎng)絡生存時間。
[Abstract]:In the airport noise monitoring environment, the traditional monitoring mode cost is high, the installation environment is high, the monitoring points are small, and the comprehensive measurement of the airport noise cannot be realized. The wireless sensor network is a multi-hop self-organizing network composed of a large number of cheap sensor nodes distributed in the monitoring area, and can realize all-round and all-weather monitoring of the target. The wireless sensor network is an energy-limited network, as compared to a conventional network. How to reduce the resource consumption of the network as much as possible under the premise of ensuring the data accuracy is the first concern. As a technology that can reduce network energy consumption and extend network life time, data fusion technology is a hot topic in recent years. As a new type of data fusion technology, the compression-sensing theory breaks the limitation of the traditional signal sampling law, and provides a method for recovering the original signal with high probability from the less sampling data, which can greatly reduce the redundant information of the sensing data, so as to reduce the transmission data amount of the network and is widely applied to a plurality of fields. Firstly, the greedy algorithm for several classical signal reconstruction: the orthogonal matching tracking algorithm, the subspace tracking algorithm, the sparse adaptive matching tracking algorithm, the front and back tracking algorithm is compared and analyzed, and on the basis of the front and back tracking algorithms, An improved front-and-back tracking algorithm for linear variable step is proposed, which combines the idea of phase and variable step of the sparse adaptive matching and tracking algorithm, and divides the running time of the algorithm into two stages, which are close to the optimal solution and the small step length to approximate the optimal solution. Different step sizes can be used in different stages, and the step length can reduce the running time of the algorithm, and the small step length can improve the reconstruction performance of the algorithm. Aiming at the problem of noise monitoring in the airport environment, the paper presents a time-space-related base station classification clustering compression-sensing algorithm. in that algorithm, the base station combine the special environment of the airport and the position information of the node, The cluster head node is further compressed and forwarded according to the spatial correlation of the intra-cluster nodes, and the base station reconstructs the signal by adopting a linear variable step front-back tracking algorithm to restore the original signal. The simulation results show that the algorithm can obviously improve the cluster performance of the network, balance the network cluster size, reduce the amount of network data, balance the energy consumption of network transmission, and improve the network survival time.
【學位授予單位】：南京航空航天大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP212.9;TN929.5;X839.1
，
本文編號：2319502
[Abstract]:In the airport noise monitoring environment, the traditional monitoring mode cost is high, the installation environment is high, the monitoring points are small, and the comprehensive measurement of the airport noise cannot be realized. The wireless sensor network is a multi-hop self-organizing network composed of a large number of cheap sensor nodes distributed in the monitoring area, and can realize all-round and all-weather monitoring of the target. The wireless sensor network is an energy-limited network, as compared to a conventional network. How to reduce the resource consumption of the network as much as possible under the premise of ensuring the data accuracy is the first concern. As a technology that can reduce network energy consumption and extend network life time, data fusion technology is a hot topic in recent years. As a new type of data fusion technology, the compression-sensing theory breaks the limitation of the traditional signal sampling law, and provides a method for recovering the original signal with high probability from the less sampling data, which can greatly reduce the redundant information of the sensing data, so as to reduce the transmission data amount of the network and is widely applied to a plurality of fields. Firstly, the greedy algorithm for several classical signal reconstruction: the orthogonal matching tracking algorithm, the subspace tracking algorithm, the sparse adaptive matching tracking algorithm, the front and back tracking algorithm is compared and analyzed, and on the basis of the front and back tracking algorithms, An improved front-and-back tracking algorithm for linear variable step is proposed, which combines the idea of phase and variable step of the sparse adaptive matching and tracking algorithm, and divides the running time of the algorithm into two stages, which are close to the optimal solution and the small step length to approximate the optimal solution. Different step sizes can be used in different stages, and the step length can reduce the running time of the algorithm, and the small step length can improve the reconstruction performance of the algorithm. Aiming at the problem of noise monitoring in the airport environment, the paper presents a time-space-related base station classification clustering compression-sensing algorithm. in that algorithm, the base station combine the special environment of the airport and the position information of the node, The cluster head node is further compressed and forwarded according to the spatial correlation of the intra-cluster nodes, and the base station reconstructs the signal by adopting a linear variable step front-back tracking algorithm to restore the original signal. The simulation results show that the algorithm can obviously improve the cluster performance of the network, balance the network cluster size, reduce the amount of network data, balance the energy consumption of network transmission, and improve the network survival time.
【學位授予單位】：南京航空航天大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP212.9;TN929.5;X839.1
，
本文編號：2319502
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