基于壓縮感知的寬帶信號(hào)采集與處理關(guān)鍵技術(shù)研究
發(fā)布時(shí)間：2018-07-18 09:24
【摘要】：隨著無(wú)線通信技術(shù)的迅猛發(fā)展,電磁頻譜環(huán)境日益復(fù)雜,在電磁頻譜監(jiān)測(cè)、無(wú)線電頻譜感知、通信偵察等非合作接收應(yīng)用中,不僅待處理的頻帶不斷拓展,而且由于信號(hào)類(lèi)型不斷豐富,接收信號(hào)的動(dòng)態(tài)范圍也不斷增大。因此,接收機(jī)必須具備大帶寬、大動(dòng)態(tài)的信號(hào)接收處理能力,而受到現(xiàn)有模擬數(shù)字轉(zhuǎn)換(ADC)器件采樣能力和動(dòng)態(tài)范圍水平的限制,以奈奎斯特理論為基礎(chǔ)的寬帶信號(hào)采集與處理機(jī)制面臨著嚴(yán)峻的技術(shù)挑戰(zhàn)。壓縮感知技術(shù)針對(duì)稀疏信號(hào)或可壓縮信號(hào),以遠(yuǎn)低于奈奎斯特采樣率的頻率對(duì)信號(hào)進(jìn)行壓縮采樣測(cè)量,降低了接收端對(duì)模擬數(shù)字轉(zhuǎn)換器的要求,同時(shí)減輕了大數(shù)據(jù)量給后端存儲(chǔ)和處理帶來(lái)的壓力,為解決寬帶信號(hào)采集與處理中的難題帶來(lái)了新的思路。本文圍繞壓縮感知理論應(yīng)用于寬帶信號(hào)采集與處理中的若干關(guān)鍵問(wèn)題展開(kāi)研究,重點(diǎn)針對(duì)感知矩陣不確定條件下的稀疏信號(hào)重構(gòu)、基于壓縮感知的寬帶信號(hào)采集的動(dòng)態(tài)范圍分析、壓縮域的干擾抑制、低信噪比下的寬帶稀疏信號(hào)檢測(cè)和調(diào)制識(shí)別以及基于壓縮感知的電磁頻譜監(jiān)測(cè)方案實(shí)現(xiàn)等問(wèn)題進(jìn)行了研究,論文的主要工作和創(chuàng)新點(diǎn)如下:1、面向壓縮感知在寬帶信號(hào)采集中的實(shí)現(xiàn),針對(duì)壓縮感知中測(cè)量矩陣攝動(dòng)和稀疏基矩陣失配的現(xiàn)實(shí)問(wèn)題,建立感知矩陣不確定的壓縮測(cè)量理論模型,提出一種基于誤差限的聯(lián)合正則化重構(gòu)算法,理論分析和仿真結(jié)果表明,相對(duì)于標(biāo)準(zhǔn)的壓縮測(cè)量模型和相應(yīng)的重構(gòu)算法,該算法在感知矩陣存在誤差情況下,能夠有效抵抗性能的惡化。標(biāo)準(zhǔn)壓縮感知重構(gòu)算法建立在理想的數(shù)學(xué)模型之上,但在測(cè)量矩陣攝動(dòng)或稀疏基失配時(shí),由于誤差的影響將導(dǎo)致其重構(gòu)性能惡化。本文在研究分析感知矩陣不確定因素的基礎(chǔ)上,假定誤差項(xiàng)有界,將感知矩陣不確定的信號(hào)重構(gòu)問(wèn)題轉(zhuǎn)化為一個(gè)1和2范數(shù)聯(lián)合約束的凸優(yōu)化求解問(wèn)題,通過(guò)1范數(shù)對(duì)重構(gòu)信號(hào)的稀疏性進(jìn)行約束的同時(shí),引入2范數(shù)對(duì)感知矩陣不確定性進(jìn)行約束,以一定的稀疏性為代價(jià),實(shí)現(xiàn)稀疏信號(hào)有效重構(gòu)的同時(shí)保證了求解的穩(wěn)定性。2、針對(duì)寬帶信號(hào)接收的無(wú)雜散動(dòng)態(tài)范圍性能問(wèn)題展開(kāi)研究,推導(dǎo)并給出正弦信號(hào)激勵(lì)下ADC無(wú)雜散動(dòng)態(tài)范圍性能的理論界,分析和仿真該理論界和量化間隔、高斯噪聲和采樣率等因素的關(guān)系,得出相關(guān)結(jié)論;從理論上分析了壓縮采樣的量化噪聲譜分布特征和無(wú)雜散動(dòng)態(tài)范圍性能,結(jié)果表明壓縮采樣的量化噪聲譜分布和輸入信號(hào)形式無(wú)關(guān),相對(duì)于傳統(tǒng)ADC采樣,壓縮采樣的無(wú)雜散動(dòng)態(tài)范圍受噪聲、ADC非線性等因素的影響較小,且可通過(guò)降低采樣率、丟棄小部分飽和測(cè)量值等方式,有效提高寬帶信號(hào)采集的動(dòng)態(tài)范圍。ADC無(wú)雜散動(dòng)態(tài)范圍測(cè)試方法受器件因素影響難以得到精確結(jié)果,本文在對(duì)ADC量化噪聲譜分析基礎(chǔ)上,理論推導(dǎo)了單音正弦信號(hào)激勵(lì)下ADC的無(wú)雜散動(dòng)態(tài)范圍性能界,研究了量化比特、輸入信號(hào)幅度、加性高斯噪聲的方差等因素對(duì)SFDR性能的影響;結(jié)合傅里葉分析法,分析推導(dǎo)了不同采樣率條件下的SFDR性能,得出了采樣率和正弦激勵(lì)信號(hào)頻率呈“質(zhì)數(shù)”關(guān)系時(shí)SFDR性能相對(duì)較好、整數(shù)倍采樣時(shí)SFDR性能隨采樣率呈折線上升的結(jié)論;進(jìn)一步對(duì)壓縮測(cè)量的量化噪聲譜進(jìn)行了分析,得出了由于隨機(jī)測(cè)量矩陣的作用,壓縮感知的量化噪聲譜是和輸入信號(hào)形式無(wú)關(guān)的白噪聲譜的結(jié)論;比較分析了ADC電路非線性對(duì)ADC采樣和壓縮采樣的無(wú)雜散動(dòng)態(tài)范圍性能的影響,從降低采樣率和測(cè)量值的公平性角度,闡明了壓縮感知解決寬帶信號(hào)采集中的大動(dòng)態(tài)問(wèn)題的優(yōu)勢(shì)。3、針對(duì)壓縮感知框架下寬帶信號(hào)的干擾抑制問(wèn)題,提出了一種基于最小輸出能量準(zhǔn)則的壓縮域干擾抑制算法,理論分析和仿真結(jié)果表明,該算法在干擾信號(hào)支撐集信息未知的情況下,能夠有效抑制干擾對(duì)目標(biāo)信號(hào)重構(gòu)性能的影響。在壓縮感知框架下,主要利用子空間正交投影算法和斜投影算法對(duì)干擾進(jìn)行抑制,但都需要以干擾信號(hào)支撐集的先驗(yàn)知識(shí)為前提,這在非合作方的寬帶信號(hào)采集與處理應(yīng)用中通常無(wú)法滿足。為此本文提出了一種無(wú)需支撐集先驗(yàn)知識(shí)的干擾抑制算法,該算法以感知矩陣每一列的期望投影的輸出能量最小化為準(zhǔn)則,設(shè)計(jì)相應(yīng)的投影濾波器對(duì)壓縮測(cè)量值進(jìn)行投影濾波,進(jìn)一步通過(guò)設(shè)定投影值門(mén)限對(duì)干擾信號(hào)進(jìn)行抑制,同時(shí)保留目標(biāo)信號(hào)的全部信息以便于后續(xù)的相關(guān)處理。4、針對(duì)寬帶稀疏信號(hào)的檢測(cè)和調(diào)制識(shí)別問(wèn)題,將循環(huán)譜的相關(guān)理論引入壓縮感知框架下,基于循環(huán)頻率切面的近似塊稀疏特性,提出一種基于壓縮域循環(huán)譜能量特征的信號(hào)檢測(cè)算法,仿真結(jié)果表明,該算法能夠有效實(shí)現(xiàn)較低信噪比條件下的稀疏信號(hào)檢測(cè)。以此為基礎(chǔ)設(shè)計(jì)了一種基于二分迭代的壓縮域循環(huán)譜特征提取方法,并結(jié)合二叉樹(shù)分類(lèi)器,實(shí)現(xiàn)了調(diào)制信號(hào)的識(shí)別。首先分析了現(xiàn)有壓縮感知框架下,經(jīng)典的壓縮檢測(cè)、子空間檢測(cè)算法在低信噪比條件下檢測(cè)性能的局限性,然后基于大部分調(diào)制信號(hào)具備循環(huán)平穩(wěn)特性以及高斯白噪聲只在零循環(huán)頻率處出現(xiàn)的事實(shí),將循環(huán)譜分析引入到壓縮感知框架下,提出基于壓縮域循環(huán)譜能量特征的稀疏信號(hào)檢測(cè)算法。該算法不同于現(xiàn)有壓縮循環(huán)譜的信號(hào)檢測(cè)算法,不需要對(duì)壓縮循環(huán)譜進(jìn)行完全重構(gòu),且充分利用信號(hào)在循環(huán)頻率切面的近似塊稀疏特性,所需的壓縮測(cè)量數(shù)目和計(jì)算量大大降低,仿真結(jié)果表明該算法在低信噪比條件下能有效實(shí)現(xiàn)信號(hào)的檢測(cè)。最后在塊稀疏壓縮循環(huán)譜模型的基礎(chǔ)上,給出一種基于二分迭代的循環(huán)譜特征提取方法,并結(jié)合二叉樹(shù)分類(lèi)器實(shí)現(xiàn)對(duì){BPSK,FSK,2ASK,16QAM,MSK}五類(lèi)常見(jiàn)信號(hào)的調(diào)制識(shí)別。5、針對(duì)“電磁頻譜監(jiān)測(cè)傳感器網(wǎng)絡(luò)關(guān)鍵技術(shù)研究”課題中電磁頻譜監(jiān)測(cè)的需求,設(shè)計(jì)了基于AIC壓縮測(cè)量的電磁頻譜監(jiān)測(cè)方案和原理驗(yàn)證平臺(tái),并針對(duì)AIC實(shí)現(xiàn)時(shí)濾波器非理想導(dǎo)致重構(gòu)性能惡化的問(wèn)題,提出了一種基于自適應(yīng)濾波校正的方法,提高了重構(gòu)性能。為降低瞬時(shí)突發(fā)信號(hào)的漏檢概率,緩解前端ADC的壓力和要求,本方案基于AIC的壓縮測(cè)量思想對(duì)電磁頻譜信號(hào)進(jìn)行寬帶采集,并利用測(cè)量值直接在壓縮域?qū)崿F(xiàn)信號(hào)檢測(cè)、調(diào)制識(shí)別等信號(hào)處理工作。設(shè)計(jì)實(shí)現(xiàn)了方案的原理驗(yàn)證平臺(tái),模擬端采用RD結(jié)構(gòu),數(shù)字端考慮到系統(tǒng)的可擴(kuò)展性和靈活性,采用GPU+FPGA+ARM的結(jié)構(gòu)。最后針對(duì)基于RD的模擬壓縮測(cè)量實(shí)現(xiàn)過(guò)程中,濾波器沖激響應(yīng)非理想影響信號(hào)重構(gòu)性能的問(wèn)題,設(shè)計(jì)自適應(yīng)濾波校正算法對(duì)非理想濾波器的脈沖沖激響應(yīng)進(jìn)行估計(jì),提高了系統(tǒng)的性能,且無(wú)需改變?cè)袎嚎s測(cè)量結(jié)構(gòu)。
[Abstract]:With the rapid development of wireless communication technology, the electromagnetic spectrum environment is increasingly complex. In the non cooperative receiving applications, such as electromagnetic spectrum monitoring, radio spectrum sensing, communication reconnaissance and other non cooperative applications, the frequency band of the processing is not only expanded, but also the dynamic range of received signals is increasing because of the continuous enrichment of signal types. Therefore, the receiver must be equipped with With large bandwidth, large dynamic signal reception and processing capability, and limited by the existing analog digital conversion (ADC) device sampling and dynamic range level, the wideband signal acquisition and processing mechanism based on Nyquist theory faces severe technical challenges. Compression sensing technology is far lower for sparse signal or compressible signal. The compression sampling measurement of the Nyquist sampling rate reduces the demand for analog digital converter at the receiving end, reduces the pressure caused by the large amount of data to the back end storage and processing, and brings new ideas to solve the difficult problems in the acquisition and processing of wide-band signals. Several key problems in signal acquisition and processing are studied, focusing on sparse signal reconstruction under the uncertainty of perceptual matrix, dynamic range analysis of compressed sensing based wideband signal acquisition, interference suppression in compressed domain, wide-band sparse signal detection and modulation recognition under low signal to noise ratio, and electricity based on compressed sensing. The main work and innovation of this paper are as follows: 1, for the realization of compressed sensing in the acquisition of wide-band signal, in view of the real problems of the measurement matrix perturbation and the sparse matrix mismatch in the compressed sensing, a theoretical model of the uncertainty of the perception matrix is set up, and a kind of theory based on the uncertainty of the perception matrix is proposed. The theoretical analysis and simulation results show that, compared with the standard compression measurement model and the corresponding reconstruction algorithm, the algorithm can effectively resist the deterioration of the performance when the perceptual matrix exists error. The standard compression perception reconstruction algorithm is based on the ideal mathematical model, but the measurement moment is in the measurement moment. In this paper, on the basis of analyzing the uncertainty factors of the perception matrix, this paper assumes that the error term is bounded, and transforms the signal reconstruction problem of the uncertainty of the perception matrix into a convex optimization problem with a combination of 1 and 2 norm constraints, and the reconstruction of the reconstructed letter through the 1 norm. When the sparsity of the number is constrained, the 2 norm is introduced to restrict the uncertainty of the perceptual matrix. At the expense of a certain sparsity, the effective reconstruction of the sparse signal is realized and the stability of the solution is guaranteed at the same time. The study on the performance of the non stray dynamic range of the wideband signal receiving is studied, and the sinusoidal signal excitation is derived and given under the excitation of the wideband signal receiving. ADC has no theoretical circle of stray dynamic range performance, analyzes and emulates the relationship between the theoretical circle and the quantization interval, the relationship between the Gauss noise and the sampling rate, and draws the relevant conclusions. The quantitative noise spectrum distribution and the non stray dynamic range performance of the compressed sampling are analyzed theoretically. The results show the quantization noise spectrum distribution and input of the compressed sampling. The signal form is independent. Compared with the traditional ADC sampling, the non stray dynamic range of the compressed sampling is less affected by the noise, the ADC nonlinearity and other factors, and it can effectively improve the dynamic range of the wideband signal acquisition by reducing the sampling rate and discarding the small part of the saturation measurement. The method is affected by the device factors in the dynamic range of the wideband signal acquisition.ADC. It is difficult to obtain accurate results. On the basis of ADC quantization noise spectrum analysis, this paper derives the performance boundary of ADC without stray dynamic range under the excitation of monosyllabic sinusoidal signal, and studies the influence of quantized bits, input signal amplitude, variance of additive Gauss noise and other factors on the performance of SFDR. The SFDR performance under the sample rate is obtained. It is concluded that the SFDR performance is relatively good when the sampling rate and the sinusoidal excitation signal frequency is "prime", and the SFDR performance increases with the sampling rate when the integer multiple sampling is sampled. The quantization noise spectrum of the compression measurement is further analyzed, and the compression perception is obtained due to the effect of the random measurement matrix. The quantization noise spectrum is the conclusion of white noise spectrum unrelated to the input signal form. The influence of ADC circuit nonlinearity on the non stray dynamic range performance of ADC sampling and compressed sampling is compared and analyzed. From the angle of reducing the sampling rate and the fairness of the measured values, the advantage.3 of the compressed sensing to solve the big dynamic problems in the broadband signal acquisition is clarified. In view of the interference suppression of wide-band signals under the compressed sensing framework, a compression domain interference suppression algorithm based on the minimum output energy criterion is proposed. The theoretical analysis and simulation results show that the algorithm can effectively suppress the influence of interference on the performance of target signal reconstruction under the condition of unknown interference signal support set. Under the framework of contraction sensing, the interference is suppressed mainly by subspace orthogonal projection algorithm and oblique projection algorithm, but all of them need to be based on the prior knowledge of the interference signal support set, which is usually not satisfied in the application of non cooperative wideband signal acquisition and processing. The algorithm is based on the minimization of the output energy of the expected projection of each column of the perceptual matrix, and the corresponding projection filter is designed for the projection filtering of the measured value, and the interference signal is suppressed by setting the threshold value of the projection value, and all the information of the target signal is retained to facilitate the subsequent related processing of.4. In view of the detection and modulation recognition of wide-band sparse signals, the correlation theory of cyclic spectrum is introduced into the compressed sensing framework. A signal detection algorithm based on the cyclic spectral energy characteristics of the compressed domain is proposed based on the sparse characteristic of the approximate block in the circular frequency section. The simulation results show that the algorithm can effectively implement the low signal to noise ratio conditions. On the basis of this, we design a cyclic spectral feature extraction method based on two sub iteration, and combine the two forked tree classifier to realize the modulation signal recognition. First, it analyzes the local detection performance under the existing compression sensing framework, the classic compression detection and subspace detection under the low signal to noise ratio conditions. Based on the fact that most modulation signals have cyclostationary characteristics and the fact that Gauss white noise appears only at zero cycle frequency, the cyclic spectrum analysis is introduced into the compressed sensing framework, and the sparse signal detection algorithm based on the cyclic spectral energy characteristics of the compressed domain is proposed. This algorithm is different from the existing compression cycle spectrum signal detection. The algorithm does not need to reconstruct the compression cycle spectrum completely, and make full use of the approximate block sparsity of the signal in the circular frequency section. The number of compressed measurements and the amount of computation are greatly reduced. The simulation results show that the algorithm can detect the signal effectively under the condition of low signal to noise ratio. Finally, the base of the block sparse compression cyclic spectrum model is based on the simulation results. On the base of this, a cyclic spectral feature extraction method based on two minute iteration is given, and the modulation recognition.5 for the common signals of {BPSK, FSK, 2ASK, 16QAM and MSK} is realized with the two fork tree classifier. According to the requirement of the electromagnetic spectrum monitoring in the key technology research of the electromagnetic spectrum monitoring sensor network, the AIC compression measurement is designed. The electromagnetic spectrum monitoring scheme and the principle verification platform, and aiming at the problem that the non ideal filter results in the deterioration of the reconstruction performance when the AIC is realized, a method based on adaptive filtering correction is proposed to improve the reconstruction performance. In order to reduce the leakage probability of the instantaneous burst signal and alleviate the pressure and requirement of the ADC in the front end, the scheme is based on the compression of AIC. The idea of measuring the wide-band signal of the electromagnetic spectrum signal, and using the measured value directly in the compressed domain to realize signal detection, modulation recognition and other signal processing work. The design and Realization of the scheme's principle verification platform, the analog end uses the RD structure, the digital end takes into account the extensibility and flexibility of the system, and uses the structure of the GPU+FPGA+ARM. Finally the needle is used. In the implementation of RD based analog compression measurement, the impulse response of the filter is not ideal for the performance of the signal reconstruction. The adaptive filter correction algorithm is designed to estimate the impulse impulse response of the non ideal filter, which improves the performance of the system and does not need to change the original compression measurement structure.
【學(xué)位授予單位】：解放軍信息工程大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TN911.7
本文編號(hào)：2131473
[Abstract]:With the rapid development of wireless communication technology, the electromagnetic spectrum environment is increasingly complex. In the non cooperative receiving applications, such as electromagnetic spectrum monitoring, radio spectrum sensing, communication reconnaissance and other non cooperative applications, the frequency band of the processing is not only expanded, but also the dynamic range of received signals is increasing because of the continuous enrichment of signal types. Therefore, the receiver must be equipped with With large bandwidth, large dynamic signal reception and processing capability, and limited by the existing analog digital conversion (ADC) device sampling and dynamic range level, the wideband signal acquisition and processing mechanism based on Nyquist theory faces severe technical challenges. Compression sensing technology is far lower for sparse signal or compressible signal. The compression sampling measurement of the Nyquist sampling rate reduces the demand for analog digital converter at the receiving end, reduces the pressure caused by the large amount of data to the back end storage and processing, and brings new ideas to solve the difficult problems in the acquisition and processing of wide-band signals. Several key problems in signal acquisition and processing are studied, focusing on sparse signal reconstruction under the uncertainty of perceptual matrix, dynamic range analysis of compressed sensing based wideband signal acquisition, interference suppression in compressed domain, wide-band sparse signal detection and modulation recognition under low signal to noise ratio, and electricity based on compressed sensing. The main work and innovation of this paper are as follows: 1, for the realization of compressed sensing in the acquisition of wide-band signal, in view of the real problems of the measurement matrix perturbation and the sparse matrix mismatch in the compressed sensing, a theoretical model of the uncertainty of the perception matrix is set up, and a kind of theory based on the uncertainty of the perception matrix is proposed. The theoretical analysis and simulation results show that, compared with the standard compression measurement model and the corresponding reconstruction algorithm, the algorithm can effectively resist the deterioration of the performance when the perceptual matrix exists error. The standard compression perception reconstruction algorithm is based on the ideal mathematical model, but the measurement moment is in the measurement moment. In this paper, on the basis of analyzing the uncertainty factors of the perception matrix, this paper assumes that the error term is bounded, and transforms the signal reconstruction problem of the uncertainty of the perception matrix into a convex optimization problem with a combination of 1 and 2 norm constraints, and the reconstruction of the reconstructed letter through the 1 norm. When the sparsity of the number is constrained, the 2 norm is introduced to restrict the uncertainty of the perceptual matrix. At the expense of a certain sparsity, the effective reconstruction of the sparse signal is realized and the stability of the solution is guaranteed at the same time. The study on the performance of the non stray dynamic range of the wideband signal receiving is studied, and the sinusoidal signal excitation is derived and given under the excitation of the wideband signal receiving. ADC has no theoretical circle of stray dynamic range performance, analyzes and emulates the relationship between the theoretical circle and the quantization interval, the relationship between the Gauss noise and the sampling rate, and draws the relevant conclusions. The quantitative noise spectrum distribution and the non stray dynamic range performance of the compressed sampling are analyzed theoretically. The results show the quantization noise spectrum distribution and input of the compressed sampling. The signal form is independent. Compared with the traditional ADC sampling, the non stray dynamic range of the compressed sampling is less affected by the noise, the ADC nonlinearity and other factors, and it can effectively improve the dynamic range of the wideband signal acquisition by reducing the sampling rate and discarding the small part of the saturation measurement. The method is affected by the device factors in the dynamic range of the wideband signal acquisition.ADC. It is difficult to obtain accurate results. On the basis of ADC quantization noise spectrum analysis, this paper derives the performance boundary of ADC without stray dynamic range under the excitation of monosyllabic sinusoidal signal, and studies the influence of quantized bits, input signal amplitude, variance of additive Gauss noise and other factors on the performance of SFDR. The SFDR performance under the sample rate is obtained. It is concluded that the SFDR performance is relatively good when the sampling rate and the sinusoidal excitation signal frequency is "prime", and the SFDR performance increases with the sampling rate when the integer multiple sampling is sampled. The quantization noise spectrum of the compression measurement is further analyzed, and the compression perception is obtained due to the effect of the random measurement matrix. The quantization noise spectrum is the conclusion of white noise spectrum unrelated to the input signal form. The influence of ADC circuit nonlinearity on the non stray dynamic range performance of ADC sampling and compressed sampling is compared and analyzed. From the angle of reducing the sampling rate and the fairness of the measured values, the advantage.3 of the compressed sensing to solve the big dynamic problems in the broadband signal acquisition is clarified. In view of the interference suppression of wide-band signals under the compressed sensing framework, a compression domain interference suppression algorithm based on the minimum output energy criterion is proposed. The theoretical analysis and simulation results show that the algorithm can effectively suppress the influence of interference on the performance of target signal reconstruction under the condition of unknown interference signal support set. Under the framework of contraction sensing, the interference is suppressed mainly by subspace orthogonal projection algorithm and oblique projection algorithm, but all of them need to be based on the prior knowledge of the interference signal support set, which is usually not satisfied in the application of non cooperative wideband signal acquisition and processing. The algorithm is based on the minimization of the output energy of the expected projection of each column of the perceptual matrix, and the corresponding projection filter is designed for the projection filtering of the measured value, and the interference signal is suppressed by setting the threshold value of the projection value, and all the information of the target signal is retained to facilitate the subsequent related processing of.4. In view of the detection and modulation recognition of wide-band sparse signals, the correlation theory of cyclic spectrum is introduced into the compressed sensing framework. A signal detection algorithm based on the cyclic spectral energy characteristics of the compressed domain is proposed based on the sparse characteristic of the approximate block in the circular frequency section. The simulation results show that the algorithm can effectively implement the low signal to noise ratio conditions. On the basis of this, we design a cyclic spectral feature extraction method based on two sub iteration, and combine the two forked tree classifier to realize the modulation signal recognition. First, it analyzes the local detection performance under the existing compression sensing framework, the classic compression detection and subspace detection under the low signal to noise ratio conditions. Based on the fact that most modulation signals have cyclostationary characteristics and the fact that Gauss white noise appears only at zero cycle frequency, the cyclic spectrum analysis is introduced into the compressed sensing framework, and the sparse signal detection algorithm based on the cyclic spectral energy characteristics of the compressed domain is proposed. This algorithm is different from the existing compression cycle spectrum signal detection. The algorithm does not need to reconstruct the compression cycle spectrum completely, and make full use of the approximate block sparsity of the signal in the circular frequency section. The number of compressed measurements and the amount of computation are greatly reduced. The simulation results show that the algorithm can detect the signal effectively under the condition of low signal to noise ratio. Finally, the base of the block sparse compression cyclic spectrum model is based on the simulation results. On the base of this, a cyclic spectral feature extraction method based on two minute iteration is given, and the modulation recognition.5 for the common signals of {BPSK, FSK, 2ASK, 16QAM and MSK} is realized with the two fork tree classifier. According to the requirement of the electromagnetic spectrum monitoring in the key technology research of the electromagnetic spectrum monitoring sensor network, the AIC compression measurement is designed. The electromagnetic spectrum monitoring scheme and the principle verification platform, and aiming at the problem that the non ideal filter results in the deterioration of the reconstruction performance when the AIC is realized, a method based on adaptive filtering correction is proposed to improve the reconstruction performance. In order to reduce the leakage probability of the instantaneous burst signal and alleviate the pressure and requirement of the ADC in the front end, the scheme is based on the compression of AIC. The idea of measuring the wide-band signal of the electromagnetic spectrum signal, and using the measured value directly in the compressed domain to realize signal detection, modulation recognition and other signal processing work. The design and Realization of the scheme's principle verification platform, the analog end uses the RD structure, the digital end takes into account the extensibility and flexibility of the system, and uses the structure of the GPU+FPGA+ARM. Finally the needle is used. In the implementation of RD based analog compression measurement, the impulse response of the filter is not ideal for the performance of the signal reconstruction. The adaptive filter correction algorithm is designed to estimate the impulse impulse response of the non ideal filter, which improves the performance of the system and does not need to change the original compression measurement structure.
【學(xué)位授予單位】：解放軍信息工程大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TN911.7
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