WSN中机场噪声压缩感知算法研究
发布时间：2018-11-08 19:56
【摘要】：机场噪声监测环境中,传统监测模式成本高、安装环境要求高、监测点较少,无法实现对机场噪声的全面精确测量。无线传感器网络是由分布在监测区域的大量廉价的传感器节点组成的一个多跳自组织网络,可实现对目标的全方位、全天候的监测。与传统的网络相比,无线传感器网络是一个能量有限的网络。如何在保证数据准确性的前提下尽可能地减少网络的资源消耗,是首先需要关注的问题。数据融合技术作为一种可以降低网络能耗,延长网络生存时间的技术,是近几年研究的热点。压缩感知理论作为一种新型的数据融合技术,打破了传统的信号采样定律的限制,提供了一种从较少的采样数据中高概率地恢复原始信号的方法,可以大大减少感知数据的冗余信息,从而减少网络传输数据量,被广泛应用于多个领域。论文首先对几种经典的信号重构贪婪算法:正交匹配追踪算法、子空间追踪算法、稀疏自适应匹配追踪算法、前后追踪算法进行比较分析,并在前后追踪算法基础上,提出了一种改进的线性变步长前后追踪算法,该算法结合稀疏自适应匹配追踪算法的分阶段、变步长的思想,将算法运行时间分为大步长靠近最优解与小步长逼近最优解两阶段,在不同的阶段使用不同的步长,大步长可以降低算法运行时间,小步长可以提高算法重构性能。针对机场环境下的噪声监测问题,论文提出一种基于时空相关性的基站分类聚簇压缩感知算法。在该算法中,基站结合机场特殊环境及节点位置信息,采用均衡分类技术进行迭代分簇并广播分簇信息;节点根据感知信号在时域上的稀疏性进行压缩,簇首节点根据簇内节点的空间相关性进一步压缩并转发;基站采用线性变步长前后追踪算法对信号进行重构,还原原始信号。仿真结果表明,该算法能够明显提升网络的聚簇性能,均衡网络分簇大小,减少网络数据量,平衡网络传输能耗,提升网络生存时间。
[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.
【学位授予单位】：南京航空航天大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP212.9;TN929.5;X839.1
，
本文编号：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.
【学位授予单位】：南京航空航天大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP212.9;TN929.5;X839.1
，
本文编号：2319502
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