本文關(guān)鍵詞： 船舶自動(dòng)識(shí)別系統(tǒng) 單通道分離 改進(jìn)EMD算法 冗余小波變換 信道跟蹤　出處：《天津理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：船載自動(dòng)識(shí)別系統(tǒng)(Automatic Identification System,AIS)作為一種新型的數(shù)字導(dǎo)航系統(tǒng),不僅在防避碰和自動(dòng)識(shí)別方面取得了成功,而且在加強(qiáng)海上生命安全、提高航行的效率、以及保護(hù)海洋環(huán)境等方面有著十分重要的作用。針對(duì)星載AIS系統(tǒng)中存在的信噪比較低、多普勒頻移較大、信道時(shí)變較快和多用戶干擾嚴(yán)重等問(wèn)題,本文主要工作如下:(1)提出了一種基于改進(jìn)經(jīng)驗(yàn)?zāi)B(tài)分解(Empirical Mode Decomposition,EMD)的單通道信號(hào)分離算法。該算法首先利用極值點(diǎn)對(duì)稱延拓算法對(duì)接收信號(hào)進(jìn)行延拓,該過(guò)程改善了傳統(tǒng)EMD算法中的端點(diǎn)效應(yīng)。然后采用傳統(tǒng)EMD算法將觀測(cè)信號(hào)分解成一系列頻率不同的本征模態(tài)函數(shù)(Intrinsic mode component,IMF);然后根據(jù)KL散度(Kullback-Leible divergence)的值,提取IMF分量中有用部分;最后利用獨(dú)立分量分析(Independent component analysis,ICA)算法實(shí)現(xiàn)部分AIS信號(hào)分離。仿真結(jié)果表明,該算法可以實(shí)現(xiàn)低信噪比下多路信號(hào)混合時(shí)的單通道分離,并且混合的源信號(hào)之間的相對(duì)頻偏越大,分離效果越好。(2)提出了一種基于冗余小波變換(Redundant discrete wavelet transform,RDWT)的單通道星載AIS信號(hào)分離算法。該方法首先利用RDWT算法將觀測(cè)信號(hào)分解成頻率不同的近似信號(hào)和細(xì)節(jié)信號(hào),該過(guò)程完成由單通道分離向多通道分離的轉(zhuǎn)化;然后利用ICA算法實(shí)現(xiàn)AIS源信號(hào)分離。仿真結(jié)果表明,該算法實(shí)現(xiàn)了多路混合信號(hào)的分離,并且分離效果幾乎不受時(shí)延影響。(3)提出了一種信道和相位聯(lián)合跟蹤下的信號(hào)檢測(cè)算法。該算法將信道和相位實(shí)時(shí)跟蹤結(jié)合起來(lái),實(shí)現(xiàn)了參數(shù)與序列的聯(lián)合估計(jì)。仿真結(jié)果表明,該算法性能優(yōu)于差分檢測(cè)、維特比譯碼和基于逐幸存路徑處理(per-survivor processing,PSP)的信號(hào)檢測(cè)算法,并且抗頻偏能力較強(qiáng)。
[Abstract]:As a new type of digital navigation system, automatic Identification system (AIS) has not only been successful in preventing collision and automatic recognition, but also has enhanced the safety of life and the efficiency of navigation. In view of the problems of low SNR, large Doppler frequency shift, fast time-varying channel and serious multi-user interference in spaceborne AIS system, it is very important to protect the marine environment. The main work of this paper is as follows: (1) A single channel signal separation algorithm based on improved empirical Mode decomposition (EMD) is proposed. This process improves the endpoint effect in the traditional EMD algorithm, and then decomposes the observed signal into a series of intrinsic mode component functions with different frequencies by using the traditional EMD algorithm, and then extracts the useful parts of the IMF component according to the KL divergence and Kullback-Leible divergence. Finally, the independent component analysis (ICA) algorithm is used to realize partial AIS signal separation. The simulation results show that the algorithm can realize the single-channel separation of multi-channel signals mixed with low SNR, and the greater the relative frequency deviation between the mixed source signals is. The better the separation effect is, a single channel space-borne AIS signal separation algorithm based on redundant wavelet transform (redundant discrete wavelet transform RDWT) is proposed. Firstly, the observed signal is decomposed into approximate signal and detail signal with different frequency by using RDWT algorithm. The process completes the transformation from single-channel separation to multi-channel separation, and then realizes the AIS source signal separation using ICA algorithm. The simulation results show that the algorithm realizes the separation of multi-channel mixed signals. A signal detection algorithm based on channel and phase joint tracking is proposed, which combines channel and phase tracking in real time, and realizes the joint estimation of parameters and sequences. The performance of this algorithm is superior to that of differential detection, Viterbi decoding and signal detection algorithm based on per-survivor processing PSPs.
【學(xué)位授予單位】：天津理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U675.7;TN911.7

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