盲源信號分離算法研究及應(yīng)用
發(fā)布時間：2018-09-11 09:25
【摘要】：盲源信號分離(Blind Source/Signal Separation,BSS)是指在未知源信號和傳輸通道參數(shù)的情況下,根據(jù)輸入源信號的統(tǒng)計特性,從觀測信號恢復(fù)出源信號各個分量的過程,是目前信號處理中最熱門的研究方向之一。由于盲源信號分離技術(shù)對混合源和傳輸通道的先驗信息要求不高,能夠僅從接收端就能完成信號的估計和恢復(fù),具有其它信號處理技術(shù)不能比擬的優(yōu)勢,目前,盲源信號分離技術(shù)以其突出的技術(shù)優(yōu)勢已經(jīng)在眾多領(lǐng)域得到應(yīng)用和發(fā)展,尤其在生物醫(yī)學(xué)、電子對抗、語音增強、遙感、地震探測、通信系統(tǒng)、地球物理學(xué)、計量經(jīng)濟學(xué)、機械力學(xué)等領(lǐng)域發(fā)揮了重要作用。但是,盲源信號分離技術(shù)在與實際問題結(jié)合中也暴露了分離性能較差、計算復(fù)雜度較高、應(yīng)用條件受限等缺點。研究有效的盲源信號分離理論,提高分離性能、降低計算復(fù)雜度、減少先驗信息的約束條件是現(xiàn)代通信系統(tǒng)應(yīng)用的迫切需求,因此,本文開展了盲源信號分離算法及應(yīng)用方面的研究。本文結(jié)合被動雷達系統(tǒng)、跳頻信號和鄰星干擾等實際應(yīng)用背景,從理論和實踐角度研究盲源信號分離處理算法,力求達到提高通信系統(tǒng)頻譜效率、增強通信系統(tǒng)抗干擾和信號檢測性能的目的。本文主要研究工作包括被動雷達系統(tǒng)中強干擾弱信號的分離、正交跳頻體制下欠定盲源信號的分離、非正交跳頻體制下欠定盲源信號的分離和鄰星干擾中盲源信號分離四個方面的內(nèi)容,具體研究內(nèi)容如下:針對強干擾條件下被動雷達系統(tǒng)中弱信號盲源分離問題,提出了干擾抵消算法(Interference Cancellation Algorithm,IC-Algorithm),達到消除強干擾、提高弱信號檢測能力的目的。具體地,本文把強干擾信號分為合作信號和非合作信號兩種情況分別開展研究。第一種情況是強干擾信號為合作信號,在強干擾條件下,需要對強干擾信號進行估計和重建,估計和重建的精度直接影響盲源信號分離的效果。根據(jù)重建后的強干擾信號提出了干擾抵消算法(IC-Algorithm)消除強干擾信號。由于獲取的是弱目標(biāo)混合信號,本文接著提出了KM-FastICA算法進行弱目標(biāo)混合信號的分離。干擾抵消后殘余信號的強弱對源信號的分離效果也產(chǎn)生很大的影響,本文從信息論的角度分析了殘余信號對源混合信號分離的影響。第二種情況是強干擾信號為非合作信號,信號參數(shù)未知,不能直接應(yīng)用本文所提出的干擾抵消算法消除強干擾信號。針對該特殊場景,本文提出了先分離再抵消的方法,降低了對強干擾先驗信息的要求,具有更廣闊的應(yīng)用前景。針對正交(指源混合信號兩兩內(nèi)積為零)跳頻體制下的欠定盲源信號分離問題,提出了基于密度聚類的盲源分離算法(DCBS-algorithm),達到提高盲源信號分離性能、優(yōu)化使用頻譜資源的目的。本文提出的密度聚類盲分離算法(DCBS-algorithm)分為兩步:第一步,借助跳頻信號的稀疏性,采用短時傅里葉變換(STFT)獲得采樣信號的時頻域信息,根據(jù)采樣信號的時頻域信息構(gòu)建了代價函數(shù)對(?,?)和決策坐標(biāo)系統(tǒng),然后利用代價函數(shù)對(?,?)對采樣信號的時頻域值進行密度聚類,找到聚類中心;第二步,根據(jù)聚類中心完成采樣信號的分類,利用短時傅里葉變換的逆變換完成信號的恢復(fù),實現(xiàn)盲源信號分離。所提的密度聚類盲分離算法(DCBS-algorithm)解決了正交跳頻體制下欠定條件盲源信號分離問題,在較低計算復(fù)雜度的前提下提升了分離性能。針對非正交(指源混合信號兩兩之間內(nèi)積不為零)跳頻體制下的盲源信號分離問題,提出了匹配優(yōu)化盲分離(Matching Optimization Blind Separation,MOBS)算法,達到優(yōu)化盲源信號分離算法、提高頻譜利用效率的目的。將信號分為兩類:一類是未發(fā)生碰撞的采樣信號,對于該類采樣信號提出了密度聚類盲分離算法(DCBS-algorithm);另一類為發(fā)生碰撞的采樣信號,由于該類信號的采樣點為多個信號之和,不再滿足稀疏性,無法使用聚類的方法完成盲源信號分離。因此,提出了匹配優(yōu)化盲分離(MOBS)算法,根據(jù)信號采樣特征提出了代價函數(shù),基于最陡下降法構(gòu)建了代價函數(shù),實現(xiàn)了盲源信號分離。該算法解決了非正交跳頻體制下的盲源信號分離問題,進一步擴展了盲源信號分離算法的應(yīng)用范圍。針對現(xiàn)代衛(wèi)星通信鄰星干擾中存在的盲源信號分離問題,提出了基于粒子群優(yōu)化的盲源信號分離算法,提高星上處理能力和抗干擾能力。該算法包含三個步驟:首先,通過計算每一個采樣點的短時傅里葉變換,獲得樣本信號的時頻域信息;其次,應(yīng)用K-means聚類算法對樣本信號進行預(yù)處理,得到更好的分離性能和較低的計算復(fù)雜度;最后,根據(jù)鄰星干擾的特征定義了迭代參數(shù),提出了基于粒子群優(yōu)化的盲源信號分離方法。該算法具有較好的收斂性和魯棒性,增強了星上處理能力和抗干擾能力。
[Abstract]:Blind Source/Signal Separation (BSS) refers to the process of recovering each component of the source signal from the observed signal according to the statistical characteristics of the input signal under the condition of unknown source signal and transmission channel parameters. Blind Source/Signal Separation (BSS) is one of the most popular research directions in signal processing. The prior information of source and transmission channel is not high, and it can estimate and restore the signal only from the receiver. It has the advantage that other signal processing technology can not compare. At present, blind source signal separation technology with its outstanding technical advantages has been applied and developed in many fields, especially in biomedicine, electronic countermeasures, and voice. Enhancement, remote sensing, seismic detection, communication systems, geophysics, econometrics, mechanical mechanics and other fields have played an important role. However, blind source signal separation technology in combination with practical problems has also exposed the disadvantages of poor separation performance, high computational complexity and limited application conditions. High separation performance, reduced computational complexity and reduced constraints of prior information are the urgent needs of modern communication systems. Therefore, this paper studies the blind source separation algorithm and its application. Source signal separation and processing algorithms strive to improve the spectrum efficiency of communication systems, enhance the anti-jamming and signal detection performance of communication systems. This paper mainly studies the separation of strong and weak jamming signals in passive radar systems, the separation of underdetermined blind source signals in orthogonal frequency hopping systems, and the underdetermined blind source signals in non-orthogonal frequency hopping systems. Four aspects of separation and blind source separation in adjacent satellite jamming are studied as follows: Aiming at the problem of weak signal blind source separation in passive radar system with strong jamming, an interference Cancellation Algorithm (IC-Algorithm) is proposed to eliminate strong jamming and improve the ability of weak signal detection. Specifically, this paper divides the strong interference signal into cooperative signal and non-cooperative signal. The first case is that the strong interference signal is cooperative signal. Under the strong interference condition, it is necessary to estimate and reconstruct the strong interference signal. The accuracy of estimation and reconstruction directly affects the effect of blind source signal separation. After the strong interference signal, the IC-Algorithm is proposed to eliminate the strong interference signal. Because the weak target mixed signal is obtained, the KM-FastICA algorithm is proposed to separate the weak target mixed signal. The residual signal after interference cancellation also has a great influence on the separation effect of the source signal. This paper presents a new method to separate the weak target mixed signal. From the angle of information theory, the influence of residual signal on the separation of mixed source signals is analyzed. The second case is that the strong interference signal is a non-cooperative signal and the signal parameters are unknown. A density clustering based blind source separation algorithm (DCBS-algorithm) is proposed to improve the performance of blind source separation and optimize the use of spectrum resources in orthogonal frequency hopping system. The density clustering blind separation algorithm (DCBS-algorithm) proposed in this paper is divided into two steps. The first step is to obtain the time-frequency domain information of the sampled signal by means of the sparsity of the frequency hopping signal. The cost function pair (???) and the decision coordinate system are constructed according to the time-frequency domain information of the sampled signal. The second step is to classify the sampled signal according to the clustering center and restore the signal using the inverse transform of short-time Fourier transform to realize blind source separation. Conditional Blind Source Separation (CBS) improves the separation performance with lower computational complexity. Matching Optimization Blind Separation (MOBS) algorithm is proposed to optimize the Blind Source Signal Separation (BSS) in non-orthogonal frequency hopping (FH) systems. The signal is divided into two types: one is the sample signal without collision, and the other is the sample signal with collision, which can no longer satisfy the sparse requirement. Therefore, a matching optimization blind source separation (MOBS) algorithm is proposed. According to the characteristics of signal sampling, a cost function is proposed. Based on the steepest descent method, a cost function is constructed to realize blind source separation. In order to solve the problem of Blind Source Separation in the neighboring satellite jamming of modern satellite communication, a Blind Source Separation algorithm based on particle swarm optimization is proposed to improve the on-board processing ability and anti-jamming ability. The algorithm consists of three steps: firstly, the short of each sampling point is calculated. Secondly, K-means clustering algorithm is used to preprocess the sample signal to obtain better separation performance and lower computational complexity. Finally, iteration parameters are defined according to the characteristics of neighboring star interference, and a blind source separation method based on particle swarm optimization is proposed. It has good convergence and robustness, and enhances the processing capability and anti-jamming capability on the satellite.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：TN911.7
本文編號：2236289
[Abstract]:Blind Source/Signal Separation (BSS) refers to the process of recovering each component of the source signal from the observed signal according to the statistical characteristics of the input signal under the condition of unknown source signal and transmission channel parameters. Blind Source/Signal Separation (BSS) is one of the most popular research directions in signal processing. The prior information of source and transmission channel is not high, and it can estimate and restore the signal only from the receiver. It has the advantage that other signal processing technology can not compare. At present, blind source signal separation technology with its outstanding technical advantages has been applied and developed in many fields, especially in biomedicine, electronic countermeasures, and voice. Enhancement, remote sensing, seismic detection, communication systems, geophysics, econometrics, mechanical mechanics and other fields have played an important role. However, blind source signal separation technology in combination with practical problems has also exposed the disadvantages of poor separation performance, high computational complexity and limited application conditions. High separation performance, reduced computational complexity and reduced constraints of prior information are the urgent needs of modern communication systems. Therefore, this paper studies the blind source separation algorithm and its application. Source signal separation and processing algorithms strive to improve the spectrum efficiency of communication systems, enhance the anti-jamming and signal detection performance of communication systems. This paper mainly studies the separation of strong and weak jamming signals in passive radar systems, the separation of underdetermined blind source signals in orthogonal frequency hopping systems, and the underdetermined blind source signals in non-orthogonal frequency hopping systems. Four aspects of separation and blind source separation in adjacent satellite jamming are studied as follows: Aiming at the problem of weak signal blind source separation in passive radar system with strong jamming, an interference Cancellation Algorithm (IC-Algorithm) is proposed to eliminate strong jamming and improve the ability of weak signal detection. Specifically, this paper divides the strong interference signal into cooperative signal and non-cooperative signal. The first case is that the strong interference signal is cooperative signal. Under the strong interference condition, it is necessary to estimate and reconstruct the strong interference signal. The accuracy of estimation and reconstruction directly affects the effect of blind source signal separation. After the strong interference signal, the IC-Algorithm is proposed to eliminate the strong interference signal. Because the weak target mixed signal is obtained, the KM-FastICA algorithm is proposed to separate the weak target mixed signal. The residual signal after interference cancellation also has a great influence on the separation effect of the source signal. This paper presents a new method to separate the weak target mixed signal. From the angle of information theory, the influence of residual signal on the separation of mixed source signals is analyzed. The second case is that the strong interference signal is a non-cooperative signal and the signal parameters are unknown. A density clustering based blind source separation algorithm (DCBS-algorithm) is proposed to improve the performance of blind source separation and optimize the use of spectrum resources in orthogonal frequency hopping system. The density clustering blind separation algorithm (DCBS-algorithm) proposed in this paper is divided into two steps. The first step is to obtain the time-frequency domain information of the sampled signal by means of the sparsity of the frequency hopping signal. The cost function pair (???) and the decision coordinate system are constructed according to the time-frequency domain information of the sampled signal. The second step is to classify the sampled signal according to the clustering center and restore the signal using the inverse transform of short-time Fourier transform to realize blind source separation. Conditional Blind Source Separation (CBS) improves the separation performance with lower computational complexity. Matching Optimization Blind Separation (MOBS) algorithm is proposed to optimize the Blind Source Signal Separation (BSS) in non-orthogonal frequency hopping (FH) systems. The signal is divided into two types: one is the sample signal without collision, and the other is the sample signal with collision, which can no longer satisfy the sparse requirement. Therefore, a matching optimization blind source separation (MOBS) algorithm is proposed. According to the characteristics of signal sampling, a cost function is proposed. Based on the steepest descent method, a cost function is constructed to realize blind source separation. In order to solve the problem of Blind Source Separation in the neighboring satellite jamming of modern satellite communication, a Blind Source Separation algorithm based on particle swarm optimization is proposed to improve the on-board processing ability and anti-jamming ability. The algorithm consists of three steps: firstly, the short of each sampling point is calculated. Secondly, K-means clustering algorithm is used to preprocess the sample signal to obtain better separation performance and lower computational complexity. Finally, iteration parameters are defined according to the characteristics of neighboring star interference, and a blind source separation method based on particle swarm optimization is proposed. It has good convergence and robustness, and enhances the processing capability and anti-jamming capability on the satellite.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：TN911.7
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