【摘要】：時(shí)代在進(jìn)步,科技在發(fā)展,電子戰(zhàn)逐漸成為一種重要的現(xiàn)代化作戰(zhàn)手段。全世界都在積極地研究各種電子戰(zhàn)算法,因此未來(lái)的電磁環(huán)境將會(huì)越來(lái)越復(fù)雜。常用的跳頻通信技術(shù)是以犧牲有限頻譜資源來(lái)提高其抗干擾能力,而這種犧牲也是有限的,因此需要新的方法來(lái)改善跳頻系統(tǒng)的抗干擾性能。本文利用盲源分離方法的優(yōu)勢(shì),研究了盲源分離方法與跳頻通信技術(shù)相結(jié)合的抗干擾技術(shù),來(lái)提高系統(tǒng)的抗干擾能力。主要內(nèi)容為:研究了基于空間時(shí)頻分布矩陣的跳頻信號(hào)盲分離技術(shù)。跳頻信號(hào)是典型的非平穩(wěn)信號(hào),且不同跳頻信號(hào)具有不同的跳頻圖案。本文充分利用跳頻信號(hào)的這些特征,采用了根據(jù)觀測(cè)信號(hào)的平滑偽Wigner-Ville分布來(lái)構(gòu)造一個(gè)四維矩陣,檢測(cè)出自源時(shí)頻點(diǎn)后,利用特征矩陣近似聯(lián)合對(duì)角化算法估計(jì)出酉矩陣,進(jìn)而分離出源信號(hào)。本文在已有的時(shí)頻點(diǎn)選擇方法基礎(chǔ)上,對(duì)已有的選擇方法進(jìn)行了改進(jìn)。改進(jìn)后的基于空間時(shí)頻分布矩陣的盲分離方法提高了分離性能,但是其算法的復(fù)雜度有所增加。研究了基于負(fù)熵最大化的FastICA分離算法。在接收端接收的信號(hào)都是來(lái)自不同的電子設(shè)備,所以可以認(rèn)為觀測(cè)信號(hào)中包含的跳頻信號(hào)是統(tǒng)計(jì)獨(dú)立的。本文充分利用跳頻信號(hào)的相互獨(dú)立性、源信號(hào)中最多只有一路高斯信號(hào)和高斯信號(hào)的負(fù)熵最大這些性質(zhì),采用了基于負(fù)熵最大化的FastICA算法來(lái)分離包含多路跳頻信號(hào)的觀測(cè)信號(hào)。本文在原FastICA算法的基礎(chǔ)上,對(duì)迭代算法進(jìn)行了改進(jìn)。改進(jìn)后的FastICA算法提高了分離精度,而且其復(fù)雜度遠(yuǎn)遠(yuǎn)小于基于空間時(shí)頻分布矩陣的盲分離算法,改進(jìn)的FastICA算法分離精度隨信噪比遞增,隨干信比增加而有所下降。為了分析盲源分離算法對(duì)誤比特率性能的影響,本文比較了先盲源分離后解跳與直接解跳的誤比特率,前者的誤比特率性能要優(yōu)于后者。研究了跳頻信號(hào)的欠定混合矩陣估計(jì)。雖然跳頻信號(hào)在時(shí)域不是稀疏信號(hào),但是通過(guò)短時(shí)傅里葉變換后,跳頻信號(hào)在時(shí)頻域內(nèi)具有稀疏性。本文利用跳頻信號(hào)這一特性,通過(guò)K-means聚類算法估計(jì)出了混合矩陣。本文提出了一種新的初始聚類中心選擇方法,通過(guò)比較已有的選擇方法,本文提出的新方法對(duì)混合矩陣的估計(jì)精度要高于其他三種選擇方法。盲源分離技術(shù)與跳頻技術(shù)的結(jié)合提高了其抗干擾能力,這項(xiàng)研究將會(huì)有力推動(dòng)軍事通信抗干擾技術(shù)的不斷發(fā)展,具有重要的軍事意義和重大的現(xiàn)實(shí)意義。
[Abstract]:In the times of progress, science and technology are developing, and electronic warfare is becoming an important means of modern warfare. The world is actively studying various electronic warfare algorithms, so the future electromagnetic environment will become more and more complex. The commonly used frequency-hopping communication technology is to improve the anti-interference ability of the frequency-hopping system by sacrificing the limited spectrum resources, and the sacrifice is also limited, so a new method is needed to improve the anti-interference performance of the frequency-hopping system. This paper makes use of the advantages of blind source separation method, and studies the anti-interference technology of blind source separation method and frequency-hopping communication technology to improve the anti-interference ability of the system. The main content of this paper is to study the blind separation technology of frequency-hopping signal based on spatial time-frequency distribution matrix. The frequency hopping signal is a typical non-stationary signal and the different frequency hopping signals have different frequency hopping patterns. In this paper, the characteristics of frequency-hopping signals are fully utilized, a four-dimensional matrix is constructed based on the smooth pseudo-Wigner-Ville distribution of the observed signals. After the source frequency points are detected, the matrix is estimated by using the characteristic matrix approximation and the joint diagonalization algorithm, and then the source signals are separated. On the basis of the existing time-point selection method, the existing selection method is improved. The improved blind separation method based on the spatial time-frequency distribution matrix improves the separation performance, but the complexity of the algorithm is increased. The FastICA separation algorithm based on the maximum negative entropy is studied. The signals received at the receiving end are all from different electronic devices, so that the frequency hopping signals contained in the observation signals can be considered to be statistically independent. This paper makes full use of the mutual independence of frequency-hopping signals, the most only one-way Gaussian signal in the source signal and the maximum of the negative entropy of the Gaussian signal, and adopts the FastICA algorithm based on the maximization of the negative entropy to separate the observation signals containing the multi-channel frequency-hopping signals. In this paper, the iterative algorithm is improved on the basis of the original FastICA algorithm. The improved FastICA algorithm improves the separation precision, and the complexity is much smaller than the blind separation algorithm based on the spatial time-frequency distribution matrix. In order to analyze the effect of the blind source separation algorithm on the bit error rate performance, the bit error rate of the first blind source separation and the direct solution jump is compared, and the error bit rate performance of the former is better than the latter. In this paper, the under-determined mixed matrix estimation of frequency-hopping signals is studied. Although the frequency-hopping signal is not a sparse signal in the time domain, the frequency-hopping signal has a sparsity in the time domain by a short-time Fourier transform. In this paper, using the characteristic of frequency-hopping signal, the hybrid matrix is estimated by the K-means clustering algorithm. In this paper, a new method of initial cluster center selection is proposed. By comparing the existing selection method, the estimation accuracy of the hybrid matrix is higher than that of the other three selection methods. The combination of blind source separation technology and frequency-hopping technology has improved its anti-interference ability. The research will push forward the development of the anti-interference technology of military communication, which is of great military significance and great practical significance.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN973

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相關(guān)期刊論文 前2條

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本文編號(hào)：2501358