船艦輻射聲源來自船體三個部位，即中段輔機部分、中後段主機部分和艉段螺旋槳部分。而每艘船艦的設備與結構皆有所差異，使得噪聲頻譜結構皆有所不同，當每艘船艦在固定航速下具有唯一的線譜結構，此線譜結構可用於船艦辨識。本研究主要是因自噪聲源到達接收端的距離比目標敵艦聲源與接收端的距離近時，導致接收訊號的訊雜比降低，造成頻譜結構失真現象，為了解決上述問題，將利用時間反轉法與適應性時間反轉法降低自噪聲，進而提高聲納系統偵測距離。再者，與遞迴歸最小平方演算法(RLS)系統鑑別[1]及卡爾曼濾波器設計[2][3]作擇優分析，淺海環境中短距離的情況下適應性時間反轉法提升之訊雜比較高，長距離則為RLS演算法較佳，而時間反轉法較兩種方法遜色。船艦辨識上僅適應性時間反轉法能做到辨識，其餘兩種方法皆無法做到。

There are three kinds of signal produced from different regions of ship which are the auxiliary in the middle part, the main engine between stern and middle part and the propeller in the stern to construct the ship radiated noise. Every ship has its own noise spectrums because of the difference ship structure design and equipment. Line spectrums can be used to recognize a ship when it is cruising at an equal speed. Because self-noise is far closer to the receiver than our target, it will cause the reduction of signal noise ratio and line spectrums distortion. To solve these problems, we use time reversal method and adaptive time reversal method to reduce self-noise of ship which can also increase the sonar detection distance. Furthermore, these time reversal methods are compared with the approach by using Recursive Least Square(RLS)[1] combined with Kalman filter[2][3] algorithm. In terms of Signal-to-Noise Ratio(SNR), adaptive time reversal method is better than the RLS algorithm when receiver is close to the target in shallow sea. When target is far away from the receiver, RLS has the best results among three methods while time reversal method is the worst. However, adaptive time reversal method is the best method among these three methods that can be used to identify ships.

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