本文關(guān)鍵詞： 認知雷達 自適應能力 波形選擇 波形優(yōu)化 低截獲性能　出處：《電子科技大學》2016年碩士論文　論文類型：學位論文

【摘要】：寬帶認知雷達是雷達智能化的結(jié)果。相比于傳統(tǒng)雷達,它可以對目標環(huán)境進行自適應的變換發(fā)射波形以適應目標環(huán)境的變化。認知雷達的靈活性和自適應性使得其受到雷達領(lǐng)域多方的關(guān)注,并成為近年來雷達領(lǐng)域研究的熱點。認知是認知雷達工作的基礎(chǔ),它通過對目標特性和環(huán)境特性的認知,確定環(huán)境狀態(tài)并記錄環(huán)境狀態(tài);自適應算法是認知雷達的核心,在得到環(huán)境狀態(tài)后,認知雷達將根據(jù)環(huán)境狀態(tài)自適應地選擇波形發(fā)射,以求達到最好的檢測效果。本文將針對以上兩個方面對寬帶認知雷達進行研究,并重點研究自適應算法。本文主要工作內(nèi)容有:1.本文在基于認知雷達原理的基礎(chǔ)上,首先對認知雷達的雜波環(huán)境進行了研究,并建立了能夠表達其特性的雜波模型;然后本文還對認知雷達中的自適應波形選擇的相關(guān)理論進行了研究,包括動態(tài)規(guī)劃理論及自適應波形選擇模型。2.本文首先對經(jīng)典的目標檢測算法和基于樣本信息累積分布的檢測算法的性能進行了研究和仿真分析。然后,本文基于動態(tài)規(guī)劃理論,展開了對常規(guī)自適應波形選擇算法的研究,主要研究了價值迭代算法、簡化價值迭代算法和Q學習算法,并在這些研究基礎(chǔ)上研究了一種迭代步長可變的Q學習算法。同樣,我們也通過仿真對比了不同算法的波形選擇準確度,分析了自適應算法性能的優(yōu)劣。3.本文針對雷達對波形低截獲性能的要求,研究了面向雷達低截獲性能的自適應波形優(yōu)化算法。我們首先分析了影響雷達低截獲性能的因素和影響截獲因子的參數(shù),并且我們利用對不同基本信號的組合和編碼長度的延長等方法降低截獲因子,提高雷達的低截獲性能。經(jīng)過仿真,我們對比了優(yōu)化前后信號的模糊函數(shù)圖和截獲因子的變化情況,說明了算法對波形優(yōu)化的有效性。4.本文針對認知雷達對輔助知識信息的實時要求,研究了基于波形參數(shù)自適應優(yōu)化的波形設(shè)計算法。算法根據(jù)前一組回波信號,分析出雷達的環(huán)境情況和目標情況,并通過優(yōu)化下一組波形的參數(shù)來達到對波形優(yōu)化的目的。經(jīng)過分析,我們發(fā)現(xiàn)了信號相位向量和信干噪比(SINR)的關(guān)系并選取相位參數(shù)作為優(yōu)化參數(shù)。經(jīng)過仿真,我們對比分析經(jīng)過相位參數(shù)優(yōu)化的信號表現(xiàn)出的檢測性能與優(yōu)化前信號檢測性能區(qū)別。
[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.

【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TN958

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