基于能量比的水下分布式传感器网络目标跟踪

发布时间：2018-05-06 18:26

本文选题：水下分布式无线传感器网络 + 传感器能量比　；参考：《浙江大学》2016年硕士论文

【摘要】：基于水下传感器网络的目标跟踪是水声工程应用研究的一个重要方向,水下分布传感器网络目标跟踪技术涉及目标检测、定位和跟踪、水声通信等技术。本文根据水下声传播特点,在基于能量定位方法和贝叶斯滤波跟踪算法基础上,开展了基于能量比的水下分布式传感器网络目标跟踪技术的研究。本文在水下分布传感器网络框架下进行了以下分析与研究：1.研究并推导了水下声能衰减模型,拟合了舟山浅海环境下的声能衰减系数。利用Kraken模型仿真了分布式传感器网络节点布放于舟山浅海海域的海底,声源在水下等深度运动时,声源辐射宽带信号100-1000Hz时的传播特性,拟合得到声能衰减规律。2.在各个传感器节点内,对接收到的目标声能实现能量检测。首先将信号和噪声都建模为高斯过程,讨论了高斯白噪声中的随机高斯信号的检测问题,导出了似然比检验统计量,并给出了检测性能的分析。为了进一步降低虚警概率的影响,提出了双阈值能量检测器：在似然比检验之后增加一级过门限能量的计数阈值比较。3.利用网络内各节点获取的声能采样数据构造能量比数据,并用最小二乘方法实现了对水下运动目标的定位。仿真分析了不同传感器个数、不同分布区域以及不同能量比构造对于定位精度的影响。4.将能量比数据作为观测量,导出了非线性观察方程,并对观察方程做线性化处理,构造了基于能量比的状态一空间模型。采用扩展卡尔曼滤波实现对匀速直线运动目标的跟踪,获得较最小二乘方法更优的定位结果。
[Abstract]:Target tracking based on underwater sensor network is an important direction of underwater acoustic engineering application research. Underwater distributed sensor network target tracking technology involves target detection, location and tracking, underwater acoustic communication and other technologies. According to the characteristics of underwater acoustic propagation, based on the energy localization method and Bayesian filter tracking algorithm, the target tracking technology of underwater distributed sensor networks based on energy ratio is studied in this paper. In this paper, the following analysis and research are carried out under the framework of underwater distributed sensor networks: 1. The underwater sound energy attenuation model is studied and deduced, and the sound energy attenuation coefficient in the shallow water environment of Zhoushan is fitted. Kraken model is used to simulate the propagation characteristics of the distributed sensor network nodes in the seabed of the shallow sea area of Zhoushan. When the sound source is moving at the same depth under water, the propagation characteristics of the sound source radiating the broadband signal 100-1000Hz are simulated, and the attenuation law of sound energy is obtained. In each sensor node, the received target sound energy can be detected. Firstly, the signal and noise are modeled as Gao Si process, and the detection problem of the random Gao Si signal is discussed. The likelihood ratio test statistic is derived, and the detection performance is analyzed. In order to further reduce the influence of false alarm probability, a dual-threshold energy detector is proposed. The energy ratio data are constructed from the sound energy sampling data obtained from each node in the network, and the location of underwater moving targets is realized by using the least square method. The effects of the number of sensors, different distribution regions and different energy ratio on the positioning accuracy are analyzed by simulation. Taking the energy ratio data as the observation data, the nonlinear observation equation is derived, and the observation equation is linearized, and a state-space model based on the energy ratio is constructed. The extended Kalman filter is used to track the moving target with uniform velocity and the result is better than that of the least square method.
【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TN929.3;TP212.9

【参考文献】