復雜環(huán)境下雷達信號的分選方法
發(fā)布時間：2019-06-02 14:11
【摘要】：雷達信號分選指從多部雷達發(fā)射的混疊信號中將各個雷達發(fā)出的信號歸類的過程。在當前電子對抗戰(zhàn)爭中,及時準確地偵察敵方信號,捕獲敵方信息是取得勝利的關(guān)鍵,而信號分選是電子偵察系統(tǒng)的關(guān)鍵技術(shù)。因此,在當前電子環(huán)境復雜,信號交疊嚴重,雷達輻射源未知的條件下,如何有效地將雷達信號分選出來是亟待解決的問題。為此,本文首先對k均值聚類算法和模糊聚類算法進行深入研究:k均值聚類算法是對雷達樣本進行的硬劃分,聚類準確率不高,模糊聚類則需要事先設(shè)定先驗信息,對未知雷達輻射源信號不能有效聚類,針對傳統(tǒng)聚類算法存在的不足,首先從雷達信號的脈間特征著手,利用傳統(tǒng)五參數(shù),提出一種基于入侵性雜草改進的FCM算法,然后研究雷達信號的脈內(nèi)特征的提取方法,提出一種基于時頻原子提取脈內(nèi)特征的方法。主要研究內(nèi)容和取得的成果如下:針對雷達信號脈間特征的聚類,入侵性雜草算法具有結(jié)構(gòu)簡單,參數(shù)少,全局搜索能力強的特點,能夠在較少的迭代次數(shù)下搜尋最優(yōu)解,為此,本文提出一種基于雜草改進的FCM算法,該算法主要是對模糊聚類算法對初始聚類中心的依賴性進行改進,首先根據(jù)樣本數(shù)目確定雷達類別數(shù)目的解空間,然后根據(jù)距離準則,采用雜草算法在整個解空間內(nèi)搜索最佳的類別數(shù)目,作為模糊聚類的初始參數(shù)進行聚類,并跟傳統(tǒng)的k均值聚類和AP聚類算法進行比較,驗證了該算法擺脫了對初始聚類中心的依賴性,具有較高的分選正確率。針對雷達信號脈內(nèi)特征的聚類,使用雷達信號的脈內(nèi)特征來研究信號的分選問題是近幾年討論的熱點,許多學者驗證了基于時頻原子提取脈內(nèi)特征是有效的,但是時頻原子的數(shù)量巨大,計算復雜度高,針對這一問題本文提出一種改進的時頻原子提取脈內(nèi)特征的方法,首先介紹5種經(jīng)典的雷達信號的數(shù)學模型,然后提出將雜草算法與時頻原子相結(jié)合的方法,根據(jù)距離準則,利用雜草智能算法搜尋能夠區(qū)分不同調(diào)制方式的雷達信號的一組原子,并與待分選的雷達信號做內(nèi)積運算,作為改進FCM算法的輸入矢量進行分類,分別在-3dB到5dB的信噪比下進行仿真實驗,驗證該算法的有效性。
[Abstract]:Radar signal sorting refers to the process of classifying the signals emitted by each radar from mixed signals transmitted by multiple radars. In the current electronic confrontation war, timely and accurate detection of enemy signals and acquisition of enemy information is the key to victory, and signal sorting is the key technology of electronic reconnaissance system. Therefore, under the condition that the current electronic environment is complex, the signal overlap is serious, and the radar radiation source is unknown, how to effectively sort out the radar signal is an urgent problem to be solved. Therefore, in this paper, the k-means clustering algorithm and fuzzy clustering algorithm are deeply studied: K-means clustering algorithm is a hard division of radar samples, the clustering accuracy is not high, fuzzy clustering needs to set prior information in advance. The unknown radar emitter signal can not be effectively clustering. Aiming at the shortcomings of the traditional clustering algorithm, an improved FCM algorithm based on invasive weeds is proposed by using the traditional five parameters, starting from the inter-pulse characteristics of radar signals. Then the method of extracting intra-pulse features of radar signals is studied, and a method of extracting intra-pulse features based on time-frequency atoms is proposed. The main research contents and achievements are as follows: according to the clustering of radar signal inter-pulse characteristics, the invasive weed algorithm has the characteristics of simple structure, less parameters and strong global search ability, and can search for the optimal solution under less iterations. In this paper, an improved FCM algorithm based on weeds is proposed, which mainly improves the dependence of fuzzy clustering algorithm on the initial clustering center. Firstly, the solution space of radar category number is determined according to the number of samples. Then, according to the distance criterion, the weed algorithm is used to search for the best number of categories in the whole solution space, which is used as the initial parameter of fuzzy clustering, and compared with the traditional k-means clustering and AP clustering algorithm. It is verified that the algorithm gets rid of the dependence on the initial clustering center and has a high sorting accuracy. Aiming at the clustering of intra-pulse features of radar signals, it is a hot topic to study the sorting of radar signals by using the in-pulse features of radar signals in recent years. Many scholars have verified that it is effective to extract intra-pulse features based on time-frequency atoms. However, the number of time-frequency atoms is huge and the computational complexity is high. In order to solve this problem, an improved method of extracting intra-pulse features by time-frequency atoms is proposed in this paper. Firstly, five classical mathematical models of radar signals are introduced. Then, a method of combining weed algorithm with time-frequency atom is proposed. According to the range criterion, the weed intelligent algorithm is used to search for a group of atoms that can distinguish different modulation radar signals, and the internal product operation is performed with the radar signal to be sorted. As the input vector of the improved FCM algorithm, the simulation experiments are carried out under the signal-to-noise ratio (SNR) of-3dB to 5dB to verify the effectiveness of the algorithm.
【學位授予單位】：哈爾濱工程大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN957.51
本文編號：2491162
[Abstract]:Radar signal sorting refers to the process of classifying the signals emitted by each radar from mixed signals transmitted by multiple radars. In the current electronic confrontation war, timely and accurate detection of enemy signals and acquisition of enemy information is the key to victory, and signal sorting is the key technology of electronic reconnaissance system. Therefore, under the condition that the current electronic environment is complex, the signal overlap is serious, and the radar radiation source is unknown, how to effectively sort out the radar signal is an urgent problem to be solved. Therefore, in this paper, the k-means clustering algorithm and fuzzy clustering algorithm are deeply studied: K-means clustering algorithm is a hard division of radar samples, the clustering accuracy is not high, fuzzy clustering needs to set prior information in advance. The unknown radar emitter signal can not be effectively clustering. Aiming at the shortcomings of the traditional clustering algorithm, an improved FCM algorithm based on invasive weeds is proposed by using the traditional five parameters, starting from the inter-pulse characteristics of radar signals. Then the method of extracting intra-pulse features of radar signals is studied, and a method of extracting intra-pulse features based on time-frequency atoms is proposed. The main research contents and achievements are as follows: according to the clustering of radar signal inter-pulse characteristics, the invasive weed algorithm has the characteristics of simple structure, less parameters and strong global search ability, and can search for the optimal solution under less iterations. In this paper, an improved FCM algorithm based on weeds is proposed, which mainly improves the dependence of fuzzy clustering algorithm on the initial clustering center. Firstly, the solution space of radar category number is determined according to the number of samples. Then, according to the distance criterion, the weed algorithm is used to search for the best number of categories in the whole solution space, which is used as the initial parameter of fuzzy clustering, and compared with the traditional k-means clustering and AP clustering algorithm. It is verified that the algorithm gets rid of the dependence on the initial clustering center and has a high sorting accuracy. Aiming at the clustering of intra-pulse features of radar signals, it is a hot topic to study the sorting of radar signals by using the in-pulse features of radar signals in recent years. Many scholars have verified that it is effective to extract intra-pulse features based on time-frequency atoms. However, the number of time-frequency atoms is huge and the computational complexity is high. In order to solve this problem, an improved method of extracting intra-pulse features by time-frequency atoms is proposed in this paper. Firstly, five classical mathematical models of radar signals are introduced. Then, a method of combining weed algorithm with time-frequency atom is proposed. According to the range criterion, the weed intelligent algorithm is used to search for a group of atoms that can distinguish different modulation radar signals, and the internal product operation is performed with the radar signal to be sorted. As the input vector of the improved FCM algorithm, the simulation experiments are carried out under the signal-to-noise ratio (SNR) of-3dB to 5dB to verify the effectiveness of the algorithm.
【學位授予單位】：哈爾濱工程大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN957.51
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