针对多目标检测的雷达传感器网信号波形研究

发布时间：2018-06-09 06:21

本文选题：雷达传感器网络 + 多目标分辨　；参考：《电子科技大学》2014年硕士论文

【摘要】：雷达传感器网络(Radar Sensor Network,RSN)是由多个雷达传感器(Radar Sensor,RS)组合而成,其模式可分为分布式RSN和集中式RSN系统,由于相对集中式,分布式具有工程简单、系统灵敏度高以及遮蔽效果较小等诸多优点,使其在军事以及交通等众多领域,引起了足够的重视,其未来应用前景也是非常值得期待的。多目标分辨检测能力是RSN的综合体现之一,直接关系到RSN系统灵敏度、准确度等多个重要工作指标,然而现有RSN系统中少有从RSN实际分布情况及发射波分辨角度来考虑RSN的多目标分辨问题。因此,针对RSN的多目标分辨,本文工作主要包括:1.本文首先介绍了雷达信号处理系统中所需要的基础知识,包括脉冲压缩、匹配滤波、不同波形的介绍及分析等;为了研究RSN中波形的多目标分辨,对每种信号的模糊函数进行分析和研究;分析RSN的信道特性,考虑信道衰落与噪声影响,建立RSN信道模型;介绍RSN信息融合的方法及理论,选择合适的融合方式。2.为研究RSN的多目标分辨问题,建立RSN分析模型,研究其多目标分辨情况。该模型考虑到雷达的发射波束(Beam),根据分布式RSN的工作特点,结合协同工作状态的RSs,给出RSN的多目标分辨条件,结果证明对于相同目标的情况,RSN具有更多的观测方位和角度,其系统的多目标分辨状态不单单是相对独立的RSs的集合,其分辨行性能要比单个RS优越很多。此外给定三种不同形式的雷达发射信号,分别对它们的多目标分辨能力进行了研究。3.建立RSN的多目标分辨仿真模型,在Path-loss及高斯白噪声的环境下,RSN融合中心利用最大似然比(LR)的判决准则,对RSN模型的系统检测和分辨性能做了综合仿真和对比,并对三种波形的多目标检测分辨能力进行了对比,并给出仿真结果;结果中显示雷达检测性能分别与信噪比SNR、RS个数以及目标个数的影响关系,LFM信号的检测分辨能力最好。4.建立一种多目标高距离分辨的RSN模型,分析RSN和目标角度与位置的几何关系,使用LFM信号,通过对信号模糊函数的推导,给出该模型下的多目标时域(即距离)分辨条件,仿真结果展示该模型的多目标分辨情况,并且对比了不同雷达和目标情况下该模型多目标分辨的结果。
[Abstract]:Radar Sensor Network (RSNN) is composed of several radar sensors (Radar Sensor RSs). Its modes can be divided into distributed RSN and centralized RSN systems. Because of its high sensitivity and low shielding effect, the system has attracted enough attention in many fields, such as military and traffic, and its future application prospect is also worth looking forward to. The ability of multi-target resolution detection is one of the comprehensive embodiment of RSN, which is directly related to the sensitivity and accuracy of RSN system. However, in existing RSN systems, the multi-target resolution problem of RSN is rarely considered from the actual distribution of RSN and the angle of emission wave resolution. Therefore, for the multiple target resolution of RSN, the work in this paper mainly includes: 1. This paper first introduces the basic knowledge needed in radar signal processing system, including pulse compression, matched filtering, introduction and analysis of different waveforms, etc. Analyze and study the ambiguity function of each signal, analyze the channel characteristics of RSN, consider the influence of channel fading and noise, establish the RSN channel model, introduce the method and theory of RSN information fusion, choose the appropriate fusion mode. 2. In order to study the multi-target resolution problem of RSN, a RSN analysis model is established and its multi-target resolution is studied. In this model, considering the beam-beam of radar, according to the working characteristics of distributed RSN, combined with RSsof cooperative working state, the multi-target resolution condition of RSN is given. The results show that RSN has more observation azimuth and angle for the same target. The multi-target resolution state of the system is not only the set of the relative independent RSs, but also the performance of the row resolution is much better than that of the single RS. In addition, given three different types of radar transmit signals, their multi-target resolution is studied respectively. The multi-target resolution simulation model of RSN is established. In the environment of Path-loss and Gao Si white noise, the system detection and resolution performance of RSN model is simulated and compared by using the maximum likelihood ratio (LRR) criterion. The detection and resolution ability of the three kinds of waveforms is compared and the simulation results show that the detection performance of radar depends on the number of SNR SNR RS and the number of targets respectively. The detection and resolution ability of LFM signal is the best. 4. A high range resolution RSN model with multiple targets is established, and the geometric relationship between RSN and target angle and position is analyzed. By using LFM signal, the time domain (distance) resolution condition of multiple targets is given by deducing the ambiguity function of the signal. The simulation results show the multi-target resolution of the model, and the results of multi-target resolution under different radar and target conditions are compared.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN957.51

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