本文選題：水聲通信　切入點：自適應跳頻　出處：《南京理工大學》2017年碩士論文

【摘要】：隨著通信領域的迅猛發(fā)展,以常規(guī)的跳頻技術為基本方法的自適應跳頻技術漸漸成為通信研究的熱點之一。在水聲自適應跳頻通信系統(tǒng)中,干擾與噪聲的影響以及通信頻率的隨機跳變,使得信道質量估計變得尤為重要。另外,對于通信系統(tǒng),調制解調技術是關鍵部分,影響著通信系統(tǒng)的整體性能。因此,合適的調制解調技術與信道質量估計算法是實現跳頻通信的關鍵所在。本文在總結國內外學者的研究基礎上,研究基于水聲跳頻通信的信道估計算法與CPM信號解調譯碼算法,以算法的準確度與復雜度為衡量標準,對經典算法進行了相應的優(yōu)化,并且提出了一些新的改進算法。首先在深海信道中,對經典最大似然估計算法進行推導,并且在不同的數據長度與調制方式下進行理論仿真實驗分析;根據CPM信號頻譜特性提出一種新的信道估計算法,利用功率譜中信號與噪聲的相對關系進行信噪比估計,相比于最大似然估計算法,新算法在中高信噪比的均方根誤差低0.1dB左右,且算法復雜度大大減少;對經典卡爾曼濾波估計信噪比算法進行改進,使用支持向量回歸模型對卡爾曼濾波結構進行修正,相比于原經典算法,改進算法解決了高信噪比時性能變差的問題,并且估計的均方根誤差至少降低了 0.08dB。然后在淺海信道中,對CPM頻譜信噪比估計算法優(yōu)化推導,擴大算法的應用范圍,理論仿真實驗分析得到該算法在淺海信道下的均方根誤差只比深海信道差了0.08dB,達到了 0.18dB左右,并且復雜度很低,適合實時估計;對經典卡爾曼濾波估計信噪比算法進行改進,改進后的算法適用于淺海信道,并且均方根誤差在0.15dB左右,但是在高信噪比時性能變差。最后進行基于信道估計的CPM信號低復雜度解調譯碼算法的改進。在加性高斯白噪聲信道下,詳細介紹了差分減狀態(tài)序列檢測算法,并且在不同的條件下進行理論仿真實驗;提出一種改進的CPM解調譯碼算法,仿真證明改進算法的誤碼率明顯低于差分減狀態(tài)算法。
[Abstract]:With the rapid development of communication field, adaptive frequency-hopping technology, which is based on conventional frequency-hopping technology, has gradually become one of the hotspots of communication research.In underwater acoustic adaptive frequency-hopping communication system, the influence of interference and noise and the random jump of communication frequency make the channel quality estimation become more and more important.In addition, the modulation and demodulation technology is a key part of the communication system, which affects the overall performance of the communication system.Therefore, appropriate modulation and demodulation technology and channel quality estimation algorithm are the key to achieve FH communication.Based on the research of domestic and foreign scholars, the channel estimation algorithm based on underwater acoustic frequency hopping communication and the demodulation decoding algorithm of CPM signal are studied in this paper. The classical algorithm is optimized according to the accuracy and complexity of the algorithm.Some new improved algorithms are also proposed.At first, the classical maximum likelihood estimation algorithm is deduced in deep-sea channel, and the theoretical simulation experiment is carried out under different data length and modulation mode. According to the spectrum characteristics of CPM signal, a new channel estimation algorithm is proposed.Compared with the maximum likelihood estimation algorithm, the new algorithm has a lower root mean square error (0.1dB) than the maximum likelihood estimation algorithm, and the complexity of the algorithm is greatly reduced.The classical Kalman filter estimation SNR algorithm is improved and the support vector regression model is used to modify the Kalman filter structure. Compared with the original classical algorithm, the improved algorithm solves the problem of poor performance when the SNR is high.The estimated root mean square error is reduced by 0.08dB at least.And the complexity is very low, suitable for real-time estimation; the classical Kalman filter estimation SNR algorithm is improved, the improved algorithm is suitable for shallow water channel, and the root mean square error is about 0.15dB, but the performance becomes worse when the SNR is high.Finally, the low complexity demodulation and decoding algorithm of CPM signal based on channel estimation is improved.In the additive Gao Si white noise channel, the differential subtraction state sequence detection algorithm is introduced in detail, and the theoretical simulation experiment is carried out under different conditions, and an improved CPM demodulation decoding algorithm is proposed.The simulation results show that the BER of the improved algorithm is obviously lower than that of the differential subtraction algorithm.
【學位授予單位】：南京理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN929.3

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本文編號：1705877