【摘要】：隨著科技的迅速發(fā)展,電子對(duì)抗技術(shù)已逐步成為現(xiàn)代軍事戰(zhàn)爭(zhēng)的核心,而雷達(dá)工作模式識(shí)別又是其重中之重,它識(shí)別接收機(jī)接收到的敵方雷達(dá)信號(hào),并估計(jì)其威脅水平,能夠?yàn)榉烙瓦M(jìn)攻提供準(zhǔn)確的情報(bào),是實(shí)行電子對(duì)抗的前提。雷達(dá)信號(hào)工作模式識(shí)別主要包括復(fù)雜雷達(dá)信號(hào)的分選預(yù)處理、單輻射源信號(hào)工作模式識(shí)別、工作模式威脅等級(jí)評(píng)估幾個(gè)部分。隨著電磁環(huán)境的不斷惡化,雷達(dá)工作模式識(shí)別出現(xiàn)了新的挑戰(zhàn),傳統(tǒng)的信號(hào)處理技術(shù)對(duì)新型多功能雷達(dá)的識(shí)別精度已經(jīng)逐漸不能滿足要求,機(jī)器學(xué)習(xí)能夠合理地提取雷達(dá)信號(hào)特征,準(zhǔn)確高效地訓(xùn)練與更新模型,因此,本工作運(yùn)用機(jī)器學(xué)習(xí)的方法對(duì)仿真雷達(dá)信號(hào)進(jìn)行工作模式動(dòng)態(tài)識(shí)別。本文主要完成以下工作:(1)介紹復(fù)雜環(huán)境中雷達(dá)信號(hào)分選的概念和一些傳統(tǒng)的分選方法,實(shí)現(xiàn)用聚類的方式處理偵查到的復(fù)雜雷達(dá)信號(hào)的分選問題。(2)采用自適應(yīng)滑動(dòng)窗口劃分方法,對(duì)分選得到的單輻射源信號(hào)進(jìn)行劃分,相比于傳統(tǒng)的固定窗口劃分,該方法能夠根據(jù)模式已知的雷達(dá)信號(hào)中每種工作模式的長(zhǎng)度,自適應(yīng)地選擇出最優(yōu)的窗口長(zhǎng)度與其滑動(dòng)步長(zhǎng)。(3)運(yùn)用兩種分類思路進(jìn)行窗口序列分類。窗口特征為二維特征,分別是時(shí)間維和特征維,第一種思路是在時(shí)間維度上進(jìn)行特征選擇,對(duì)選擇得到的特征向量進(jìn)行訓(xùn)練。第二種是運(yùn)用多任務(wù)的思想,將雷達(dá)模式識(shí)別分為多個(gè)相互關(guān)聯(lián)的學(xué)習(xí)任務(wù),多個(gè)任務(wù)同時(shí)并行的學(xué)習(xí),最終得到多任務(wù)分類器(4)采用一種對(duì)雷達(dá)序列工作模式的概率進(jìn)行融合決策的方法,根據(jù)相鄰窗口序列的分類概率以及概率變化趨勢(shì),將前一個(gè)窗口的類別因素疊加到當(dāng)前窗口上,此方法可以實(shí)現(xiàn)連續(xù)到達(dá)雷達(dá)信號(hào)的動(dòng)態(tài)識(shí)別,還可以修正部分誤分類窗口序列的類別。(5)將增量學(xué)習(xí)方法運(yùn)用到雷達(dá)動(dòng)態(tài)識(shí)別中,提出雷達(dá)的增量學(xué)習(xí)方法。在雷達(dá)種類和工作模式不斷增加的情況下,節(jié)省了存儲(chǔ)空間,加快了模型更新速度。(6)運(yùn)用C/S模式,多線程的方式實(shí)現(xiàn)雷達(dá)系統(tǒng)進(jìn)行前端人機(jī)交互、動(dòng)態(tài)顯示和后端實(shí)時(shí)處理。雷達(dá)信號(hào)越來越復(fù)雜,只有準(zhǔn)確快速地識(shí)別出雷達(dá)信號(hào)的工作模式,才能快速有效地制定對(duì)抗措施。因此研究雷達(dá)信號(hào)工作模式動(dòng)態(tài)識(shí)別技術(shù)具有重要的意義。
[Abstract]:With the rapid development of science and technology, electronic warfare technology has gradually become the core of modern military warfare, and radar working pattern recognition is the most important. It recognizes the enemy radar signal received by the receiver and estimates its threat level. The ability to provide accurate information for defense and attack is a prerequisite for electronic countermeasures. The working mode recognition of radar signal mainly includes several parts: sorting and preprocessing of complex radar signal, working mode recognition of single emitter signal and evaluation of threat level of working mode. With the continuous deterioration of electromagnetic environment, new challenges have emerged in radar working pattern recognition. The recognition accuracy of traditional signal processing technology for new multifunctional radar has been gradually unable to meet the requirements. Machine learning can extract radar signal features reasonably, train and update the model accurately and efficiently. Therefore, this work uses machine learning method to dynamically identify the operating mode of simulated radar signal. The main work of this paper is as follows: (1) the concept of radar signal sorting in complex environment and some traditional sorting methods are introduced. The method of clustering is used to deal with the problem of complex radar signal sorting. (2) the method of adaptive sliding window is used to divide the single emitter signal, which is compared with the traditional fixed window partition. This method can adaptively select the optimal window length and its sliding step size according to the length of each operating mode in radar signals with known modes. (3) two kinds of classification methods are used to classify window sequences. The window features are two dimensional features, which are the time dimension and the feature dimension. The first way is to select the features in the time dimension and train the selected feature vectors. The second is to divide radar pattern recognition into several interrelated learning tasks by using the idea of multi-task. Finally, the multitask classifier (4) adopts a fusion decision method for the working mode probability of radar sequence, according to the classification probability and the probability variation trend of the adjacent window sequence. When the category factors of the former window are superimposed on the current window, this method can realize the dynamic recognition of radar signals continuously, and can also modify the category of partial misclassification of window sequences. (5) the incremental learning method is applied to radar dynamic recognition. An incremental learning method for radar is proposed. With the increasing of radar types and working modes, the storage space is saved and the updating speed of the model is accelerated. (6) the radar system realizes the front-end man-machine interaction, dynamic display and back-end real-time processing by using C / S mode and multi-thread mode. Radar signal is becoming more and more complex. Only by identifying the working mode of radar signal accurately and quickly can countermeasures be formulated quickly and effectively. Therefore, it is of great significance to study the dynamic recognition technology of radar signal working mode.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN957.51

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