低軌衛(wèi)星星載通信信號處理關(guān)鍵技術(shù)研究
發(fā)布時間：2018-08-07 18:09
【摘要】：衛(wèi)星技術(shù)的發(fā)展推動了低軌衛(wèi)星星群化和網(wǎng)絡(luò)化程度的不斷加深。通過星間鏈路構(gòu)成的低軌衛(wèi)星網(wǎng)絡(luò)可以為全球數(shù)據(jù)傳輸和多種業(yè)務(wù)應(yīng)用提供支持，長期以來一直受到各國軍事和科研部門的關(guān)注。應(yīng)用需求和承擔(dān)角色的轉(zhuǎn)變對低軌衛(wèi)星通信體制與技術(shù)提出了一系列挑戰(zhàn)。就通信信號處理的角度而言，這些挑戰(zhàn)主要包括提高捕獲精度、降低星上信號處理開銷、提高功率利用率等。本文以具有星間鏈路與星上處理能力的低軌衛(wèi)星系統(tǒng)為背景，以解決低軌衛(wèi)星星載通信信號處理面臨的挑戰(zhàn)為目標，圍繞上述三方面問題開展工作，對直擴信號高精度捕獲技術(shù)、稀疏簡化時頻處理技術(shù)、最緊致高階調(diào)制技術(shù)進行研究。本文的主要工作和創(chuàng)新性成果如下： 提出基于頻域重排實現(xiàn)并行高精度捕獲的高精度頻域重排捕獲技術(shù)，通過引入相頻特性將二維估計轉(zhuǎn)化為一維估計問題從而實現(xiàn)并行捕獲。與傳統(tǒng)捕獲方法基于信號幅頻特性通過能量檢測實現(xiàn)捕獲的思路不同，高精度頻域重排捕獲算法充分利用了信號的相頻特性。在頻域重排捕獲算法中，相頻特性與幅頻特性各自表征一個參量且二者間存在約束關(guān)系，因此二維估計問題被轉(zhuǎn)化為一維估計問題，，可以通過一次運算同時得到時頻估計結(jié)果。引入相頻特性使頻域重排捕獲算法在不降低捕獲時效性的基礎(chǔ)上獲得精度上的改善。文中對影響算法性能的因素和算法的抗噪聲性能進行了分析，推導(dǎo)了信噪比門限的非緊致理論界，并對捕獲精度進行了仿真。結(jié)果表明，該算法的碼相位估計精度和頻率估計精度比傳統(tǒng)算法分別改善了50%和60%以上。 提出基于頻域解耦改善算法抗噪聲性能的頻域重排聯(lián)合解耦捕獲算法，通過固化幅頻特性對相頻特性譜的影響減少時頻估計受到的限制。在高精度頻域重排捕獲算法中，時頻二維估計過程在流程上的耦合效應(yīng)對算法抗噪聲性能產(chǎn)生了影響。通過引入聯(lián)合解耦處理，算法在保持幅頻和相頻特性各自反映的參量特征不變的基礎(chǔ)上，使得二者的處理流程不相關(guān)化，減少了對碼相位偏移和剩余頻率估計過程的限制，從而改善了整體的抗噪聲性能。通過聯(lián)合解耦處理獲得的抗噪聲性能的改善不以降低捕獲算法的時效性為代價。文中分析了算法的抗噪聲性能，推導(dǎo)了信噪比門限的非緊致理論界。結(jié)果表明，頻域重排聯(lián)合解耦捕獲算法的信噪比門限比頻域重排捕獲算法改善了約6dB。 提出定位優(yōu)化的稀疏傅里葉變換算法，充分利用直擴信號的“限帶稀疏”特性來降低稀疏處理流程的運算復(fù)雜度。傳統(tǒng)稀疏傅里葉變換方法的稀疏處理過程本質(zhì)上是解欠定方程的問題，必須采用“壓縮、解算、選擇”的處理流程。與傳統(tǒng)方法不同，文中提出的定位優(yōu)化稀疏傅里葉變換方法充分利用直擴信號優(yōu)異的“限帶稀疏”特性來防止有效譜峰的碰撞。這使得稀疏處理過程轉(zhuǎn)化為解結(jié)果具有一定波動的常規(guī)方程的問題，因而可以采用“壓縮、預(yù)選、解算”的處理流程來降低整體復(fù)雜度，且不以最終估計結(jié)果的精確性為代價。文中對定位優(yōu)化的稀疏傅里葉變換算法性能進行了分析，并將其引入前文所述捕獲算法中。結(jié)果表明，定位優(yōu)化的稀疏傅里葉變換算法的復(fù)雜度比原稀疏傅里葉變換算法降低約50%；基于定位優(yōu)化的稀疏傅里葉變換的頻域重排捕獲算法以及頻域重排聯(lián)合解耦捕獲算法的復(fù)雜度比傳統(tǒng)捕獲算法分別降低了約96%和90%。 建立最緊致高階調(diào)制方式通用數(shù)學(xué)模型，基于分類和遞推的方法求得抗噪聲性能的通用解析表達式并提出低復(fù)雜度的三相投影解調(diào)算法。由于最緊致高階調(diào)制方式數(shù)學(xué)模型的不完善，之前的相關(guān)研究主要著眼于對無窮平面上星座的性能進行理論探討，以及對特定點數(shù)星座的性能進行實驗分析。為解決上述問題，文中提出了一套基于星座點幅值分類的通用模型，基于該模型推導(dǎo)了最緊致高階調(diào)制方式抗噪聲性能的通用表達式，隨后提出了具有恒定運算復(fù)雜度的三相解調(diào)算法。結(jié)果表明，該模型及抗噪聲性能通解與實際情況吻合；最緊致高階調(diào)制方式在大星座下的調(diào)制效率高于QAM調(diào)制方式；該低復(fù)雜度解調(diào)算法運算量僅為18次實乘與9次實加且與星座階數(shù)無關(guān)。
[Abstract]:The development of satellite technology has promoted the deepening of the star cluster and networking of low rail. The low orbit satellite network composed of intersatellite links can provide support for global data transmission and various business applications. It has been concerned by military and scientific research departments of various countries for a long time. The satellite communication system and technology put forward a series of challenges. In terms of communication signal processing, these challenges mainly include improving the acquisition precision, reducing the overhead of signal processing on the satellite, and improving the power utilization. This paper is based on the low orbit satellite system with inter satellite link and on the satellite processing capability to solve the low orbit satellite satellite carrier. The target of signal signal processing is to carry out the work on the above three aspects, the high precision acquisition technology of DSS signal, the sparse simplified time frequency processing technology and the most compact high order modulation technology. The main work and innovative achievements of this paper are as follows:
A high precision frequency domain rearrangement capture technology based on frequency domain rearrangement is proposed. By introducing phase frequency characteristics, the two dimensional estimation is transformed into one dimension estimation problem to achieve parallel acquisition. The idea of capture is different from the traditional acquisition method based on the amplitude frequency characteristics of the signal, and the high-precision frequency domain rearrangement is captured. The algorithm makes full use of the phase frequency characteristics of the signal. In the frequency domain rearrangement acquisition algorithm, the phase frequency characteristics and amplitude frequency characteristics represent one parameter and there is a constraint relationship between the two. Therefore, the two-dimensional estimation problem is transformed into one dimension estimation problem, and the time frequency estimation results can be obtained by one operation. The phase frequency characteristic is introduced to rearrange the frequency domain. The acquisition algorithm improves the precision on the basis of not reducing the timeliness of acquisition. In this paper, the factors affecting the performance of the algorithm and the anti noise performance of the algorithm are analyzed. The non compact theoretical bounds of the SNR threshold are derived, and the acquisition precision is simulated. The results show that the accuracy of the code phase estimation and the precision of the frequency estimation are shown by the algorithm. The degree is improved by 50% and over 60% compared with the traditional algorithm.
A frequency domain rearrangement combined decoupling acquisition algorithm based on frequency domain decoupling is proposed to reduce the time frequency estimation of the phase frequency characteristic spectrum by curing the amplitude frequency characteristics. In the high-precision frequency domain rearrangement acquisition algorithm, the coupling effect of the time frequency two-dimensional estimation process on the process is produced by the noise resistance performance. By introducing the combined decoupling process, the algorithm makes the processing flow unrelated on the basis of keeping the amplitude frequency and phase frequency characteristic of each parameter invariable, reducing the restriction on the code phase shift and the residual frequency estimation process, thus improving the anti noise performance of the whole body. The combined decoupling processing is obtained by the combined decoupling process. The improvement of anti noise performance is not at the cost of reducing the effectiveness of the acquisition algorithm. The anti noise performance of the algorithm is analyzed and the non compact theoretical bounds of the signal to noise ratio threshold are derived. The results show that the signal to noise ratio threshold of the frequency domain rearrangement combined decoupling acquisition algorithm is improved by about 6dB.
The sparse Fourier transform algorithm for location optimization is proposed, which makes full use of the "limited band sparsity" characteristic of DSSS to reduce the computational complexity of the sparse processing process. The sparse processing process of the traditional sparse Fourier transform is essentially a problem of solving the underdetermined equation. The method is different. The location optimization sparse Fourier transform proposed in this paper makes full use of the "limited band sparsity" characteristic of the direct spread spectrum signal to prevent the effective peak collision. This makes the sparse processing process the problem of the conventional equation with certain fluctuation in the solution, and can be treated with the treatment of "compression, preselection, solution". The process is used to reduce the overall complexity and not at the cost of the accuracy of the final estimation results. The performance of the sparse Fourier transform algorithm is analyzed and introduced into the previous acquisition algorithm. The results show that the complexity of the sparse Fourier transform method of location optimization is lower than that of the original sparse Fourier transform algorithm. Low about 50%, the frequency domain rearrangement acquisition algorithm based on the sparse Fourier transform based on location optimization and the complexity of the frequency domain rearrangement combined decoupling capture algorithm are about 96% and 90%. lower than the traditional acquisition algorithm.
The general mathematical model of the most compact high order modulation mode is set up. The general analytic expression of the anti noise performance is obtained based on the classification and recurrence method, and the low complexity three-phase projection demodulation algorithm is proposed. The previous correlation research mainly focuses on the constellations on the infinite plane because of the imperfect mathematical model of the most high-order modulation mode. In order to solve the above problem, a general model based on the classification of the constellation amplitude is proposed. Based on the model, a general expression of the most compact high order modulation method is derived, and a constant operation complexity of three is proposed. The phase demodulation algorithm shows that the general solution of the model and the anti noise performance is in agreement with the actual situation. The modulation efficiency of the most compact high order modulation method under the large constellation is higher than that of the QAM modulation; the low complexity demodulation algorithm is only 18 times real and 9 times, and is independent of the constellation number.
【學(xué)位授予單位】：北京理工大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TN927.2
本文編號：2170900
[Abstract]:The development of satellite technology has promoted the deepening of the star cluster and networking of low rail. The low orbit satellite network composed of intersatellite links can provide support for global data transmission and various business applications. It has been concerned by military and scientific research departments of various countries for a long time. The satellite communication system and technology put forward a series of challenges. In terms of communication signal processing, these challenges mainly include improving the acquisition precision, reducing the overhead of signal processing on the satellite, and improving the power utilization. This paper is based on the low orbit satellite system with inter satellite link and on the satellite processing capability to solve the low orbit satellite satellite carrier. The target of signal signal processing is to carry out the work on the above three aspects, the high precision acquisition technology of DSS signal, the sparse simplified time frequency processing technology and the most compact high order modulation technology. The main work and innovative achievements of this paper are as follows:
A high precision frequency domain rearrangement capture technology based on frequency domain rearrangement is proposed. By introducing phase frequency characteristics, the two dimensional estimation is transformed into one dimension estimation problem to achieve parallel acquisition. The idea of capture is different from the traditional acquisition method based on the amplitude frequency characteristics of the signal, and the high-precision frequency domain rearrangement is captured. The algorithm makes full use of the phase frequency characteristics of the signal. In the frequency domain rearrangement acquisition algorithm, the phase frequency characteristics and amplitude frequency characteristics represent one parameter and there is a constraint relationship between the two. Therefore, the two-dimensional estimation problem is transformed into one dimension estimation problem, and the time frequency estimation results can be obtained by one operation. The phase frequency characteristic is introduced to rearrange the frequency domain. The acquisition algorithm improves the precision on the basis of not reducing the timeliness of acquisition. In this paper, the factors affecting the performance of the algorithm and the anti noise performance of the algorithm are analyzed. The non compact theoretical bounds of the SNR threshold are derived, and the acquisition precision is simulated. The results show that the accuracy of the code phase estimation and the precision of the frequency estimation are shown by the algorithm. The degree is improved by 50% and over 60% compared with the traditional algorithm.
A frequency domain rearrangement combined decoupling acquisition algorithm based on frequency domain decoupling is proposed to reduce the time frequency estimation of the phase frequency characteristic spectrum by curing the amplitude frequency characteristics. In the high-precision frequency domain rearrangement acquisition algorithm, the coupling effect of the time frequency two-dimensional estimation process on the process is produced by the noise resistance performance. By introducing the combined decoupling process, the algorithm makes the processing flow unrelated on the basis of keeping the amplitude frequency and phase frequency characteristic of each parameter invariable, reducing the restriction on the code phase shift and the residual frequency estimation process, thus improving the anti noise performance of the whole body. The combined decoupling processing is obtained by the combined decoupling process. The improvement of anti noise performance is not at the cost of reducing the effectiveness of the acquisition algorithm. The anti noise performance of the algorithm is analyzed and the non compact theoretical bounds of the signal to noise ratio threshold are derived. The results show that the signal to noise ratio threshold of the frequency domain rearrangement combined decoupling acquisition algorithm is improved by about 6dB.
The sparse Fourier transform algorithm for location optimization is proposed, which makes full use of the "limited band sparsity" characteristic of DSSS to reduce the computational complexity of the sparse processing process. The sparse processing process of the traditional sparse Fourier transform is essentially a problem of solving the underdetermined equation. The method is different. The location optimization sparse Fourier transform proposed in this paper makes full use of the "limited band sparsity" characteristic of the direct spread spectrum signal to prevent the effective peak collision. This makes the sparse processing process the problem of the conventional equation with certain fluctuation in the solution, and can be treated with the treatment of "compression, preselection, solution". The process is used to reduce the overall complexity and not at the cost of the accuracy of the final estimation results. The performance of the sparse Fourier transform algorithm is analyzed and introduced into the previous acquisition algorithm. The results show that the complexity of the sparse Fourier transform method of location optimization is lower than that of the original sparse Fourier transform algorithm. Low about 50%, the frequency domain rearrangement acquisition algorithm based on the sparse Fourier transform based on location optimization and the complexity of the frequency domain rearrangement combined decoupling capture algorithm are about 96% and 90%. lower than the traditional acquisition algorithm.
The general mathematical model of the most compact high order modulation mode is set up. The general analytic expression of the anti noise performance is obtained based on the classification and recurrence method, and the low complexity three-phase projection demodulation algorithm is proposed. The previous correlation research mainly focuses on the constellations on the infinite plane because of the imperfect mathematical model of the most high-order modulation mode. In order to solve the above problem, a general model based on the classification of the constellation amplitude is proposed. Based on the model, a general expression of the most compact high order modulation method is derived, and a constant operation complexity of three is proposed. The phase demodulation algorithm shows that the general solution of the model and the anti noise performance is in agreement with the actual situation. The modulation efficiency of the most compact high order modulation method under the large constellation is higher than that of the QAM modulation; the low complexity demodulation algorithm is only 18 times real and 9 times, and is independent of the constellation number.
【學(xué)位授予單位】：北京理工大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TN927.2
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