在展頻系統內的窄頻和寬頻干擾的消除

Abstract

在一個直接序列的展頻系統□，如果將接收到的記號在由相關處理器處理之前，預先通過一個預測誤差瀘波器，則此展頻系統的窄頻干擾的消除能力能明顯增強，這是大家所熟知的。近來，非線性濾波器已被建議用來解決此問題。非線性濾波器的性能比線性濾波器來得好，這點已被證明。然而，通道的效應，它總是存在通訊頻道□，沒有被列入考慮。如果我們考慮通道效應，它總是存在通訊頻道□，沒有被列入考慮。如果我們考慮通道效應，則接收到的PN序列將不再是白色與二元性。取而代之的是，PN序列將趨近於一個有色的高斯程序。在這情形下，線性與非線性濾波器的性能都會衰減。在這篇論文的第一部分，我們建議一個新的方法來增加窄頻干號分成數個子頻帶訊號。然後，我們將通道和干擾在第一個子頻帶中模擬成自迴歸過程，而後用卡門濾波器來估計干擾訊號。最後，在每一個子頻帶中，干擾被消除的訊號被加在一起形成全頻帶訊號。在論文的第二部分，我們考慮寬頻干擾。我們建議一個新的接收器的結構。這結構的明顯特徵是使用中位數濾波器並且能消除脈波和脈衝干擾。模擬的結果顯示，建議的方法能表現得比現有的方法來得好。

It is well known that the narrow - band (NB) interference rejection capability of a direct - sequence (DS) spread spectrum system can be enhanced by passing received signal through a linear prediction error filter prior to correlating it with PN correlator. Recently, nonlinear filters have also been proposed to solve the problem. It has been shown that the nonlinear filters can have better performance compared to the linear ones. However, the channel effect, which always exists in the communication channel, was not taken into account. If we consider the channel effect, the received PN sequence will no longer be white and binary. Instead, the PN sequence will approach a colored Gaussian process. In this case, the performances of the linear / nonlinear approaches will all be degraded. In the first part of this thesis, we propose a new method improving the NB interference suppression capability. Using a filter bank, we first split the received DS signal into subbands. Then, we model the channel and interference in each band as autoregressive (AR) processes and a Kalman filter is used to estimate the interference signal. Finally, the interference suppressed subband signals are combined to form the fullband signal. In the second part of this thesis, we consider the wide - band interference, we suggest a new receiver structure. This distinct feature of the structure is that it employs a median fileter sand can suppress pulse - tone as well as impulse interferences. Simulations show that the proposed new algorithms can perform significantly better than existing ones.