基于宽带认知雷达的自适应波形选择算法研究

发布时间：2018-02-21 22:58

本文关键词： 认知雷达自适应能力波形选择波形优化低截获性能　出处：《电子科技大学》2016年硕士论文　论文类型：学位论文

【摘要】：宽带认知雷达是雷达智能化的结果。相比于传统雷达,它可以对目标环境进行自适应的变换发射波形以适应目标环境的变化。认知雷达的灵活性和自适应性使得其受到雷达领域多方的关注,并成为近年来雷达领域研究的热点。认知是认知雷达工作的基础,它通过对目标特性和环境特性的认知,确定环境状态并记录环境状态;自适应算法是认知雷达的核心,在得到环境状态后,认知雷达将根据环境状态自适应地选择波形发射,以求达到最好的检测效果。本文将针对以上两个方面对宽带认知雷达进行研究,并重点研究自适应算法。本文主要工作内容有:1.本文在基于认知雷达原理的基础上,首先对认知雷达的杂波环境进行了研究,并建立了能够表达其特性的杂波模型;然后本文还对认知雷达中的自适应波形选择的相关理论进行了研究,包括动态规划理论及自适应波形选择模型。2.本文首先对经典的目标检测算法和基于样本信息累积分布的检测算法的性能进行了研究和仿真分析。然后,本文基于动态规划理论,展开了对常规自适应波形选择算法的研究,主要研究了价值迭代算法、简化价值迭代算法和Q学习算法,并在这些研究基础上研究了一种迭代步长可变的Q学习算法。同样,我们也通过仿真对比了不同算法的波形选择准确度,分析了自适应算法性能的优劣。3.本文针对雷达对波形低截获性能的要求,研究了面向雷达低截获性能的自适应波形优化算法。我们首先分析了影响雷达低截获性能的因素和影响截获因子的参数,并且我们利用对不同基本信号的组合和编码长度的延长等方法降低截获因子,提高雷达的低截获性能。经过仿真,我们对比了优化前后信号的模糊函数图和截获因子的变化情况,说明了算法对波形优化的有效性。4.本文针对认知雷达对辅助知识信息的实时要求,研究了基于波形参数自适应优化的波形设计算法。算法根据前一组回波信号,分析出雷达的环境情况和目标情况,并通过优化下一组波形的参数来达到对波形优化的目的。经过分析,我们发现了信号相位向量和信干噪比(SINR)的关系并选取相位参数作为优化参数。经过仿真,我们对比分析经过相位参数优化的信号表现出的检测性能与优化前信号检测性能区别。
[Abstract]:Broadband radar is a radar intelligent cognitive results. Compared with the traditional radar, it can be adaptive to the target environment transform waveform in order to adapt to changes in the target environment. Radar cognitive flexibility and adaptability which received much attention and become a hot field of radar and radar field in recent years. Cognition is a cognitive basis for radar work, it based on the target characteristics and environment characteristics of cognition, determine the state of the environment and record the state of the environment; the adaptive algorithm is the core of cognitive radar, the state of the environment, cognitive radar will be adaptively selected according to environmental conditions in order to achieve the emission waveform, the best detection result. In this paper, in view of the above two aspects of research for broadband cognitive radar, and focuses on the adaptive algorithm. The main contents of this paper are: 1. based on the principle of cognitive radar Firstly, the clutter environment of cognitive radar is studied, and established the clutter model the expression characteristics; then the related theory of adaptive waveform selection in cognitive radar are studied, including dynamic programming theory and adaptive waveform selection model based on the classical.2. algorithm of target detection and performance testing the algorithm is based on the cumulative distribution of sample information is researched and simulated. Then, this paper based on the dynamic programming theory, researched on the conventional adaptive waveform selection algorithm, mainly studies the value iteration algorithm, simplifies the value iteration algorithm and Q learning algorithm, and based on the study of a variable step size the Q learning algorithm. Also, we are comparing different algorithms of waveform selection accuracy, analyzes the advantages and disadvantages of.3. the performance of the adaptive algorithm in this paper needle The requirements for the waveform performance of LPI radar, the study of adaptive waveform optimization algorithm for radar low interception performance. We first analyze the factors influencing the performance of LPI radar and parameters affecting the intercept factor, and we use different basic signal combinations and encoding length extension method to reduce the interception factor, improve the low probability of intercept the performance of the radar. After simulation, we compared the change of signal ambiguity function and the interception factor before and after optimization, the algorithm of waveform optimization effectiveness of.4. according to the real-time requirements of the auxiliary cognitive radar information and knowledge, on the waveform design algorithm based on adaptive waveform parameter optimization algorithm. According to the former group echo signal analysis, the environmental situation and the target of radar, and the parameter optimization of a set of waveforms to achieve the waveform optimization purposes. After analysis, we found that the signal phase vector and SINR (SINR) and the relationship between selected phase parameters as optimization parameters. By simulation, we compare the detection performance after showing phase parameter optimization and optimization of the signal before the signal detection performance difference.

【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TN958

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