本文選題：高動態(tài) + 弱信號��； 參考：《西安電子科技大學(xué)》2016年博士論文

【摘要】：隨著全球?qū)Ш叫l(wèi)星系統(tǒng)(Global Navigation Satellite System,GNSS)定位技術(shù)的廣泛應(yīng)用,對惡劣環(huán)境下利用GNSS信號定位提出了更高的要求。如接收機在室內(nèi)、城市峽谷或是茂密叢林的定位,在這種環(huán)境下通常接收的信號非常微弱,再比如飛行器的定位、衛(wèi)星的定位和導(dǎo)彈的定位。它們接收的信號具有一定的動態(tài)性而且信號通常較弱,而且對接收機的功耗有一定的要求,在這種情況下提出了無輔助接收技術(shù)。其中,GNSS信號捕獲是GNSS定位的重要步驟,利用信號捕獲來完成信號參數(shù)的初步估計,本文對這種高動態(tài)、弱信號環(huán)境下GNSS信號的捕獲問題展開深入研究。對GNSS接收信號進行了系統(tǒng)地建模,針對不同環(huán)境下的接收信號展開具體分析并提出相應(yīng)的解決方法,其主要創(chuàng)新性工作及研究成果如下：1.為了克服在高動態(tài)弱信號環(huán)境下信號的積分峰值會受到比特符號翻轉(zhuǎn)和頻率誤差的影響,本文提出了一種基于離散Chirp傅里葉變換(Discrete Chirp Fourier Transform,DCFT)塊補零方法。與傳統(tǒng)閉環(huán)捕獲和跟蹤的接收機結(jié)構(gòu)相比,該方法適用于開環(huán)捕獲。為了避免像塊積累半相干積分(Block Accumulating Semi-coherent Integration of Correlations,BASIC)方法一樣計算塊間共軛積降低原始信號的信噪比,該方法結(jié)合了DCFT和塊補零思想,能夠使后相關(guān)信號在剝離比特符號后進行相干積累并且同時進行高動態(tài)參數(shù)精準預(yù)測。同時,對提出方法的檢測性能進行分析推導(dǎo)了本文方法的捕獲概率和虛警概率表達式,最后仿真實驗表明本文方法較傳統(tǒng)的BASIC方法能在更低的信噪比下捕獲信號并精準地估計信號參數(shù)。2.為了提高衛(wèi)星信號在低信噪比、小頻偏時的捕獲概率,提出了兩種基于變換域濾波的捕獲方法。一種稱為基于快速傅里葉變換(Fast Fourier Transform,FFT)濾波的捕獲方法。由于小波變換較離散余弦變換(Discrete Cosine Transform,DCT)或傅里葉變換(Fourier Transform,FT)可以自由選擇小波基函數(shù),提出了一種基于小波域濾波的捕獲方法,該方法通過對信號在小波域內(nèi)進行頻帶劃分,接著進行選擇性重構(gòu)信號,極大地濾除了噪聲且保留了有用信號能量,使檢測概率提高。此外該方法給出利用濾波前后有用信號能量來計算能量比的小波函數(shù)選取準則。仿真分別在恒虛警條件下給出高斯信道和衰落信道的檢測概率,表明本文方法較一般方法能極大地提高信號的檢測概率。3.為了解決在乘性和加性噪聲條件下捕獲問題,提出了一種聯(lián)合均值函數(shù)和自相關(guān)函數(shù)捕獲方法,證明了接收信號的循環(huán)平穩(wěn)特性,利用信號循環(huán)平穩(wěn)特性來捕獲信號,通過選擇適當循環(huán)頻率點檢測峰值完成信號參數(shù)估計的目的。該方法利用信號能量比閾值的方式?jīng)Q定是利用均值函數(shù)還是自相關(guān)函數(shù)去預(yù)測接收信號的頻率�？朔藗鹘y(tǒng)方法僅僅利用均值函數(shù)或自相關(guān)函數(shù)的缺陷。仿真實驗證明本文方法較傳統(tǒng)方法在不同乘性噪聲下具有更好的魯棒性。4.為了實現(xiàn)在信噪比較高的高動態(tài)環(huán)境下的GNSS信號快速捕獲,提出一種基于近鄰差分積累的壓縮感知捕獲方法,對后相關(guān)信號進行近鄰差分積累,而后利用沃爾什哈達瑪變換分成兩步對積累后信號進行壓縮檢測,推導(dǎo)了該方法的檢測概率。由于近鄰差分積累可以減少比特反轉(zhuǎn)和頻率誤差對積累峰值的影響,兩步壓縮檢測可以極大降低檢測時間,所以本文方法可以實現(xiàn)在高動態(tài)下GNSS信號的快速捕獲。最后仿真實驗表明實際檢測概率和理論推導(dǎo)的一致性和在恒虛警條件下較對比方法有更高的檢測概率和更少的捕獲時間。通過本文的研究工作,為完成在特殊環(huán)境下GNSS信號捕獲提出了基于DCFT塊補零參數(shù)估計方法、基于變換域濾波的碼捕獲方法、聯(lián)合均值函數(shù)和自相關(guān)函數(shù)信號捕獲方法和利用相鄰差分改善的壓縮信號捕獲方法,并通過理論推導(dǎo)和實驗仿真說明了本文方法能有效地提高了無輔助條件下的GNSS信號捕獲性能。本文的研究理論不僅為提升GNSS信號的檢測性能提供了依據(jù),而且可以結(jié)合輔助捕獲手段為提高我國北斗導(dǎo)航精準定位提供理論依據(jù)。
[Abstract]:With the development of global navigation satellite system (Global Navigation Satellite System, GNSS) widely used in positioning technology, put forward higher requirements on the use of GNSS positioning signals under harsh environment. Such as the receiver in the room, city or location of a jungle Canyon, usually receiving signal in this environment is very weak, such as vehicle positioning positioning, satellite positioning and missiles. They received signals with dynamic signal and some are often weak, and power consumption of the receiver has certain requirements, in this case the auxiliary receiving technology. Among them, GNSS signal acquisition is an important step in the localization of GNSS, preliminary estimates using signal acquisition to complete signal the parameters, the high dynamic, in-depth research on acquisition of GNSS signal in weak signal environment. The GNSS signal was studied systematically for modeling. The received signal under different environment to analyze and put forward the corresponding solution, the main innovative work and research results are as follows: 1. in order to overcome the weak points in the high dynamic environment for the signal peak signal will be affected by bit symbol flipping and frequency error, this paper proposes a Fourier transform based on discrete Chirp transform (Discrete Chirp Fourier Transform, DCFT) block zero method. Compared with the traditional closed-loop receiver structure acquisition and tracking, this method is suitable for open-loop capture. In order to avoid accumulation as a semi coherent integration (Block Accumulating Semi-coherent Integration of Correlations, BASIC) method calculation block conjugate product to reduce the original signal to noise ratio, the method of combining DCFT and block zero thought, can make signal after coherent integration and at the same time in high dynamic parameters in peel bit symbol Accurate prediction. At the same time, analyzed the detection probability and false alarm probability of this method to detect the performance of the proposed method. Finally, the simulation results show that the BASIC method in this paper compared with the traditional method in lower SNR signal acquisition and accurate estimation of signal parameters in order to improve the.2. satellite signal in low SNR. Low frequency offset acquisition probability of the proposed two acquisition methods based on transform domain filtering. A method called based on fast Fourier transform (Fast Fourier Transform, FFT) to capture the filtering method. Because wavelet transform with the discrete cosine transform (Discrete Cosine Transform, DCT) or Fourier transform (Fourier Transform FT) can the freedom to choose the wavelet basis function, proposes an acquisition method of wavelet filtering based on the signal frequency division in the wavelet domain, and then choose the reconstruction of the letter In addition, filter noise greatly and retain the useful signal energy, to improve the detection probability. In addition the method given by the before and after filtering useful signal energy to calculate the energy ratio of wavelet function selection criterion. The simulation in CFAR under the condition of given Gauss channel and fading channel detection probability, shows that this method can greatly improve the.3. signal detection probability in order to solve the problem in the condition of capture by additive noise and compared with the general method, proposes a combined mean function and autocorrelation function of capture method, prove the cyclostationarity of the received signal, using the cyclostationarity of signals to capture the signal, by selecting the appropriate cyclic frequency peak signal detection parameter estimation. By using the method of signal energy than the decision threshold way or autocorrelation function to predict the received signal by the mean value function Frequency. To overcome the traditional method using only the mean function or defect of the autocorrelation function. The simulation experiments prove that this method is superior to the traditional methods in robustness.4. multiplicative noise is better in order to realize the fast acquisition of GNSS signal in high dynamic environment under high SNR, proposes a compressed sensing method of capture neighbor difference the accumulation based on the difference of neighbor accumulation after correlation signal, and then divided into two steps of accumulation after signal compression detection using Walsh Hadamard transform, deduces the probability of detection of the method. The nearest neighbor difference accumulation can reduce the bit reversal frequency error and the influence on the accumulation of the peak, the two step detection can be greatly compressed reduce the detection time, so this method can be achieved in high dynamic and fast acquisition of GNSS signals. Finally, simulation results show that the detection probability of the actual and theoretical derivation Consistency and in constant false alarm conditions compared with the control method has higher detection probability of capture time and less. Through this research, to complete in the special environment of GNSS signal acquisition is proposed DCFT block zero parameter estimation method based on transform domain filter code acquisition method based on joint function and mean the autocorrelation function of signal acquisition method and differential compression using adjacent signal acquisition method improved, and through theoretical analysis and experimental simulation shows that this method can effectively improve the performance of GNSS signal acquisition without auxiliary conditions. This paper studies the theory not only provides a basis for the detection performance of GNSS signals, and can be combined with auxiliary capture means to provide a theoretical basis for improving China's Beidou navigation and precise positioning.

【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】：2016
【分類號】：TN967.1

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本文編號：1744360