【摘要】：跳頻通信具有抗干擾能力強(qiáng)、截獲概率低、抗衰落能力強(qiáng)和多址組網(wǎng)能力等優(yōu)勢(shì),其在短波通信中的應(yīng)用日益廣泛,針對(duì)跳頻信號(hào)的研究已成為信號(hào)監(jiān)測(cè)中的重要內(nèi)容。作為非協(xié)作第三方,快速有效地從復(fù)雜電磁環(huán)境中檢測(cè)出跳頻信號(hào),是對(duì)其進(jìn)行后續(xù)處理的前提,也是跳頻信號(hào)監(jiān)測(cè)的難點(diǎn)。對(duì)此本文主要研究了短波跳頻信號(hào)的盲檢測(cè)技術(shù),主要工作和創(chuàng)新如下:1.研究了短波寬帶接收的預(yù)處理問(wèn)題。首先介紹了短波寬帶接收的復(fù)雜電磁環(huán)境,并分析了帶內(nèi)不平坦噪聲基底對(duì)信號(hào)檢測(cè)的影響。然后分析了基于低通濾波和形態(tài)學(xué)濾波的兩種噪聲基底估計(jì)算法,并采用高斯型結(jié)構(gòu)元素對(duì)形態(tài)學(xué)估計(jì)算法進(jìn)行改進(jìn),使得白化處理后的背景噪聲近似為白噪聲,為后續(xù)的短波跳頻信號(hào)盲檢測(cè)奠定了基礎(chǔ)。2.研究了強(qiáng)干擾條件下跳頻信號(hào)的存在性判定問(wèn)題。在信道化檢測(cè)算法的基礎(chǔ)上,根據(jù)不同信號(hào)之間頻譜的概率分布不同,給出了基于時(shí)頻圖單頻分量譜熵和加權(quán)標(biāo)準(zhǔn)差的兩種跳頻信號(hào)存在性檢測(cè)算法。首先給出了單頻分量的譜熵和加權(quán)標(biāo)準(zhǔn)差兩個(gè)概率分布特征的統(tǒng)計(jì)量,并對(duì)不同信號(hào)類型的譜熵和加權(quán)標(biāo)準(zhǔn)差進(jìn)行分析。然后根據(jù)其特性給出了譜熵法和加權(quán)標(biāo)準(zhǔn)差法的跳頻信號(hào)存在性檢測(cè)算法。仿真結(jié)果表明,該算法無(wú)需先驗(yàn)信息,能夠有效判定是否存在跳頻信號(hào),為后續(xù)的進(jìn)一步檢測(cè)提供依據(jù)。3.研究了基于信號(hào)灰度時(shí)頻圖的跳頻信號(hào)盲檢測(cè)問(wèn)題。首先根據(jù)跳頻hop在時(shí)頻圖中的鄰域分布特征及其空間位置信息,引入對(duì)稱共生矩陣閾值分割算法對(duì)灰度時(shí)頻圖處理,給出了一種基于改進(jìn)對(duì)稱共生矩陣的跳頻信號(hào)盲檢測(cè)算法。然后根據(jù)跳頻hop在時(shí)頻圖中的灰度形態(tài)特征,分別對(duì)灰度時(shí)頻圖的頻率分量和時(shí)間分量進(jìn)行灰度形態(tài)學(xué)濾波,給出了一種基于二次形態(tài)學(xué)濾波的跳頻信號(hào)盲檢測(cè)算法。仿真結(jié)果表明所提算法能夠有效降低噪聲和干擾信號(hào)的影響,從灰度時(shí)頻圖中提取完整的跳頻hop,且無(wú)需對(duì)時(shí)頻圖二值化處理,算法簡(jiǎn)單易于工程實(shí)現(xiàn)。4.研究了基于信號(hào)向頻圖的跳頻信號(hào)盲檢測(cè)問(wèn)題。針對(duì)接收帶寬內(nèi)同時(shí)存在多個(gè)跳頻信號(hào)的情況,引入信號(hào)示向信息對(duì)現(xiàn)有檢測(cè)算法進(jìn)行改進(jìn)。首先給出了向頻圖的定義,并分析了其用于跳頻信號(hào)檢測(cè)的可行性。然后根據(jù)信號(hào)示向在向頻圖中的分布,分別進(jìn)行信號(hào)示向檢測(cè)、二值化分割向頻圖和HMT提取特征等處理,給出了一種基于示向分析的檢測(cè)算法。最后根據(jù)前兩章的研究結(jié)論,聯(lián)合時(shí)頻圖對(duì)向頻圖進(jìn)行預(yù)處理,有效地降低了噪聲示向的影響。仿真結(jié)果表明該算法能夠有效地分離信號(hào),省去了復(fù)雜的去干擾步驟,且聯(lián)合時(shí)頻圖預(yù)處理,提高了信號(hào)示向的檢測(cè)概率并降低了虛警。
[Abstract]:Frequency hopping communication has many advantages, such as strong anti-interference ability, low probability of interception, strong anti-fading ability and multi-access networking ability, and its application in shortwave communication is becoming more and more extensive. The research on frequency hopping signal has become an important part of signal monitoring. As a non-cooperative third party, fast and effective detection of frequency hopping signal from complex electromagnetic environment is the premise of subsequent processing, and it is also the difficulty of frequency hopping signal monitoring. In this paper, the blind detection technology of shortwave frequency hopping signal is studied. The main work and innovation are as follows: 1. The preprocessing problem of shortwave broadband reception is studied. Firstly, the complex electromagnetic environment of shortwave broadband reception is introduced, and the influence of in-band uneven noise substrate on signal detection is analyzed. Then two kinds of noise base estimation algorithms based on low-pass filtering and morphological filtering are analyzed, and the Gaussian structural elements are used to improve the morphological estimation algorithm, so that the background noise after whitening is approximately white noise. It lays a foundation for the subsequent blind detection of shortwave frequency hopping signals. 2. In this paper, the existence of frequency hopping signals under strong interference is studied. On the basis of channelized detection algorithm, according to the different probability distribution of spectrum among different signals, two kinds of frequency hopping signal existence detection algorithms based on spectral entropy and weighted standard deviation of single frequency component of time-frequency graph are presented. Firstly, the statistics of spectral entropy and weighted standard deviation of single frequency component are given, and the spectral entropy and weighted standard deviation of different signal types are analyzed. Then, according to its characteristics, the existence detection algorithm of frequency hopping signal based on spectral entropy method and weighted standard deviation method is given. The simulation results show that the algorithm does not need prior information and can effectively determine whether there is a frequency hopping signal or not, which provides a basis for further detection. The blind detection of frequency hopping signals based on gray time frequency graph is studied. Firstly, according to the neighborhood distribution characteristics and spatial position information of frequency hopping hop in time frequency graph, the threshold segmentation algorithm of symmetric symbiosis matrix is introduced to process gray time frequency graph, and a blind detection algorithm of frequency hopping signal based on improved symmetric symbiosis matrix is proposed. Then, according to the gray morphological characteristics of frequency hopping hop in time frequency map, the frequency component and time component of gray time frequency graph are filtered by gray morphology, and a blind detection algorithm of frequency hopping signal based on quadratic morphological filtering is proposed. The simulation results show that the proposed algorithm can effectively reduce the influence of noise and interference signals, extract the complete frequency hopping hop, from the gray time frequency diagram without binarization of the time frequency graph, and the algorithm is simple and easy to be implemented in engineering. 4. The blind detection of frequency hopping signals based on signal directed frequency graph is studied. In view of the fact that there are multiple frequency hopping signals in the receiving bandwidth at the same time, the signal direction information is introduced to improve the existing detection algorithms. Firstly, the definition of directed frequency diagram is given, and its feasibility for frequency hopping signal detection is analyzed. Then, according to the distribution of signal direction in directed frequency diagram, signal direction detection, binarization segmentation direction frequency graph and HMT feature extraction are carried out respectively, and a detection algorithm based on direction analysis is proposed. Finally, according to the conclusions of the first two chapters, the directional frequency diagram is preprocessed by combining time-frequency diagram, which effectively reduces the influence of noise direction. The simulation results show that the algorithm can effectively separate the signal, save the complex de-interference steps, and combine time-frequency graph preprocessing, which improves the detection probability of signal direction and reduces the false alarm.
【學(xué)位授予單位】：解放軍信息工程大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN914.41

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