【摘要】：頻譜感知技術(shù)是研究和發(fā)展認(rèn)知動態(tài)系統(tǒng)的關(guān)鍵基礎(chǔ)要素,然而隨著無線通信、電子偵察以及電子對抗等技術(shù)的迅速發(fā)展,電子設(shè)備的種類層出不窮,各種無線電信號功率電平差異越來越大,通信信號體制、探測方式以及干擾模式也越來越多樣,使得空間電磁環(huán)境越來越復(fù)雜,這給頻譜感知技術(shù)帶來許多亟待解決和必須持續(xù)關(guān)注的新需求:1.穩(wěn)健頻譜感知在實用的認(rèn)知動態(tài)系統(tǒng)中,頻譜感知技術(shù)必須具備在極低信噪比和無線信道嚴(yán)重衰落電磁背景下,進(jìn)行實時盲頻譜感知的能力,這給經(jīng)典的頻譜感知算法感知的穩(wěn)健性帶來巨大的挑戰(zhàn)。2.精細(xì)頻譜感知新一代無線通信、監(jiān)測、偵察、對抗系統(tǒng)都或多或少地朝著超寬帶、短時突發(fā)以及多交互目標(biāo)的趨勢進(jìn)一步推進(jìn),通信電子行業(yè)的這一技術(shù)發(fā)展趨勢需要認(rèn)知動態(tài)系統(tǒng)具備超寬帶多目標(biāo)實時感知的能力,這為頻譜感知技術(shù)引入實時地在超寬帶內(nèi)精細(xì)化分析的技術(shù)難題。3.深度頻譜感知針對某些認(rèn)知動態(tài)系統(tǒng),如認(rèn)知雷達(dá)、認(rèn)知電子對抗、認(rèn)知無線電等,需要具備對無線電信號深度分析的能力,以獲取任意目標(biāo)信號的射頻特性、通信和干擾制式、調(diào)制類型、波形成形、來波方向以及位置等等眾多關(guān)鍵信息,從而實現(xiàn)系統(tǒng)的聯(lián)合優(yōu)化設(shè)計。然而,目前國內(nèi)外頻譜感知的技術(shù)和設(shè)備,極少具有深度感知的能力,這也將是頻譜感知技術(shù)需要長期演化進(jìn)步的一個方向。4.抗差異性協(xié)作頻譜感知背景噪聲是由地面噪聲、大氣噪聲、降雨噪聲、人為噪聲、干擾噪聲以及信號檢測接收機熱噪聲等多種噪聲形式復(fù)合而成,背景噪聲電平往往在時間、地點以及頻率等多維域上呈現(xiàn)出異常復(fù)雜的高動態(tài)起伏變化的特點。為了使協(xié)作頻譜感知的結(jié)果更為準(zhǔn)確和可靠,協(xié)作頻譜感知技術(shù)必須具備適應(yīng)參與協(xié)作節(jié)點背景噪聲電平高動態(tài)變化的能力,來對抗和削弱各節(jié)點自身差異性帶給最終協(xié)作結(jié)果的不良影響。本文針對認(rèn)知動態(tài)系統(tǒng)中頻譜感知技術(shù)面臨的新需求展開研究,論文主要研究成果如下:1.頻譜感知的系統(tǒng)模型常被簡單地塑造成二元假設(shè)的問題,往往忽略了衰落信道系數(shù)、信號碼元速率以及白噪聲帶寬之間的相互影響,本文構(gòu)建的系統(tǒng)模型,充分分析了三者之間的關(guān)聯(lián),并重點考慮了平坦慢衰落信道引入的樣本之間的相關(guān)性,保證了系統(tǒng)模型的精確性。2.針對現(xiàn)存頻譜感知算法面臨的穩(wěn)健性需求,本文從頻域信號處理的角度,利用歸一化純量變換的原理,設(shè)計出基于歸一化譜的信號檢測算法,該算法依據(jù)傅里葉變換的漸進(jìn)正態(tài)性和相互獨立性計算功率譜統(tǒng)計特性,利用監(jiān)測頻帶內(nèi)部分譜線強度和與全部譜線強度和的比值作為檢驗統(tǒng)計量,該算法的判決門限只與頻譜感知算法的參數(shù)配置有關(guān),而與節(jié)點的噪聲方差無關(guān),可有效克服噪聲不確定度對頻譜感知性能的影響,固定信噪比,算法的頻譜感知性能不受噪聲電平改變的影響,應(yīng)用于高斯白噪聲和平坦慢衰落信道中,可在較寬的信噪比范圍內(nèi)獲得較優(yōu)越的頻譜感知性能。3.針對頻譜感知精細(xì)化的需求,現(xiàn)有常規(guī)超外差窄帶頻譜感知技術(shù)無法快速精確完成超寬帶內(nèi)多目標(biāo)的頻譜感知,本文進(jìn)行面向離散頻段的實時多目標(biāo)并行頻譜感知研究,利用多通道-多相化的結(jié)構(gòu)進(jìn)行超寬頻段功率譜的計算,然后并行計算出各離散頻段的歸一化功率譜,通過循環(huán)執(zhí)行正向和反向搜索,檢測出通信帶寬內(nèi)被占用的多個頻隙,在一個搜索循環(huán)中,首先執(zhí)行正向判決,檢測出瞬時功率參差不齊、差異較大的子帶信號,然后執(zhí)行反向判決,檢測出正向判決中漏檢的類似梳狀信號類型的子帶信號,完成對帶內(nèi)的多個目標(biāo)的并行頻譜感知。4.針對參與協(xié)作節(jié)點自身參數(shù)的差異性,論文首先設(shè)計了一種更具有普適性的協(xié)作頻譜感知算法,該算法將各節(jié)點本地歸一化譜上傳融合中心,克服噪聲電平動態(tài)變化在時域、空域和頻域上的效應(yīng)積累,然后在融合中心采用等增益平均或者最優(yōu)化加權(quán)平均的方式計算檢驗統(tǒng)計量,可有效消除背景噪聲高動態(tài)變化對信號頻譜感知性能的影響。5.設(shè)計和硬件實現(xiàn)了頻譜傳感器節(jié)點,然后在完成頻譜傳感器組網(wǎng)的基礎(chǔ)上,設(shè)計了一種高精度同步數(shù)據(jù)采集方法,最后對比了歸一化譜頻譜感知算法和能量頻譜感知算法的單點和協(xié)作感知實測性能。
[Abstract]:Spectrum sensing technology is the key basic element for the research and development of cognitive dynamic systems. However, with the rapid development of wireless communications, electronic reconnaissance and electronic countermeasures, the types of electronic devices emerge in endlessly, the power levels of various radio signals are more and more different, the more the communication signal system, the detection mode and the interference mode are also. The more and more diversity, the more and more complex space electromagnetic environment, which brings a lot of new needs to be solved and must continue to pay attention to the spectrum sensing technology: 1. robust spectrum sensing in the practical cognitive dynamic system, spectrum sensing technology must have the real-time blind frequency under the extremely low signal to noise ratio and the wireless channel severe fading electromagnetic background. The ability of spectral perception brings great challenge to the robustness of classical spectrum sensing algorithms..2. fine spectrum sensing new generation of wireless communication, monitoring, reconnaissance, and antagonism are more or less moving towards ultra wideband, short-time burst and multi interactive target, and this technology development trend of communication electronics industry is more or less. Cognitive dynamic systems have the ability to have ultra wideband multi-target real-time perception. This is a technical problem for spectral sensing to introduce real time fine analysis in ultra wideband..3. deep spectrum sensing, such as cognitive radar, cognitive electronic countermeasures, cognitive radio, and so on, needs to have the depth of radio signals. The ability to analyze the radio frequency characteristics of any target signal, communication and interference mode, modulation type, waveform forming, wave direction and position and so on, so as to achieve the joint optimization design of the system. However, at present, the technology and equipment of spectrum sensing at home and abroad are very few with the ability of depth perception, which will also be the spectrum. The perceptual technology needs a direction of long-term evolution and progress..4. anti heterosexual cooperative spectrum sensing background noise is composed of ground noise, atmospheric noise, rain noise, artificial noise, interference noise, and the thermal noise of signal detection receiver. The background noise level is often multidimensional in time, location and frequency. In order to make the results of cooperative spectrum sensing more accurate and reliable, the cooperative spectrum sensing technology must have the ability to adapt to the high dynamic changes of the background noise level of the participating cooperation nodes, to counter and weaken the bad results of the nodes themselves to the final cooperation results. The main research results of this paper are as follows: 1. the system model of spectrum sensing is often simply molded into the two element hypothesis, often neglecting the fading channel coefficient, the signal code rate and the interaction between the white noise bandwidth, and the paper. The built system model fully analyzes the correlation between the three, and focuses on the correlation between the samples introduced by the flat slow fading channel, which ensures the accuracy of the system model to meet the robustness requirements of the existing spectrum sensing algorithm. This paper uses the principle of normalized pure transformation from the angle of frequency domain signal processing. A signal detection algorithm based on normalized spectrum is taken into account. According to the asymptotic normality and mutual independence of Fu Liye transform, the algorithm is used to calculate the statistical characteristics of power spectrum, using the intensity of spectral lines in the monitoring band and the ratio of all spectral lines to the intensity of the spectrum. The threshold of the algorithm is only matched with the parameters of the spectrum sensing algorithm. It has nothing to do with the noise variance of the node. It can effectively overcome the influence of the noise uncertainty on the spectrum sensing performance. The fixed signal to noise ratio, the spectrum sensing performance of the algorithm is not affected by the change of noise level. It can be applied to Gauss white noise and flat slow fading channels, and a better spectrum sense can be obtained in a wider range of signal to noise ratio. The existing conventional ultra heterodyne narrowband spectrum sensing technology can not quickly and accurately complete the spectrum sensing of the UWB multiple targets. In this paper, the real-time multi-target parallel spectrum sensing research oriented to discrete frequency band is studied and the multi channel multiphase structure is used to calculate the power spectrum of the ultra wide band.3.. Then the normalized power spectrum of each discrete frequency band is calculated in parallel, and the multiple frequency gaps are detected in the communication bandwidth by cyclic forward and reverse search. In a search cycle, the forward decision is performed first, and the instantaneous power is uneven and the different subband signals are detected. Then the reverse decision is performed to detect positive positive results. The subband signal similar to the comb type signal is missed in the decision, and the parallel spectrum sensing.4. for multiple targets in the band is completed for the differences in the parameters of the participating nodes. In this paper, a more universal cooperative spectrum sensing algorithm is designed. The algorithm overcomes the local normalized spectrum uploading center and overcoming each node. The dynamic changes of noise level in time domain, space and frequency domain are accumulated, and then the test statistics are calculated with equal gain average or optimal weighted average in the fusion center, which can effectively eliminate the influence of high dynamic background noise on the spectrum sensing performance of the signal.5. design and hardware implementation of the spectrum sensor nodes. After completing the networking of the spectrum sensor, a high precision synchronous data acquisition method is designed. Finally, the single point and the cooperative perception measurement performance of the normalized spectral spectrum sensing algorithm and the energy spectrum sensing algorithm are compared.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TN925

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