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研究生:許智涵
研究生(外文):Chih-Han Hsu
論文名稱:結合適應性混合式最小均方值演算法在時間延遲估測之三元陣列目標估測
論文名稱(外文):Three-Elements Array Target Ranging with Adaptive Hybrid LMS Algorithm for TDE
指導教授:陳巽璋
口試委員:易志孝馬金溝
口試日期:2017-01-13
學位類別:碩士
校院名稱:淡江大學
系所名稱:電機工程學系碩士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:英文
論文頁數:61
中文關鍵詞:時間延遲估測三元陣列定位混合式最小均方值演算法
外文關鍵詞:TDEThree-Elements Array RangingHLMS
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聲納系統是利用聲學訊號經由確定位置的水下目標物來傳播的水下技術系統; 可以分成;主動式和被動式聲納兩類。被動式聲納 (passive sonar)系統是由大尺度的水聽器 (Hydrophones) 所組成,利用從聲學感測器陣列接收到的雜訊訊號來估測目標的距離和角度。 被動式聲納並不會主動發送聲波,因此不會使自身的位置曝露出來,適合應用於潛艇設備上。本論文旨在研究被動式聲納系統有關測定目標距離和方位等技術。傳統的感測器陣列排列方式是將三個感測器放置在一等間距的直線上,此方法稱為線性三元陣列測距。而在較新的文獻中,已將感測器陣列由等間距發展至不等間距,且更進一步推廣至非直線的排列方式,稱為非線性三元陣列測距。無論線性或是非線性的三元陣列測距技術,各個感測器間接收到的訊號彼此間之時間延遲 (time delay) 是我們在做對目標估測時非常重要的參數(訊息) 。它的估測確度將直接影響到目標估測結果的精確度,因此在三元陣列測距技術中,時間延遲估測技術的選擇也是相當重要的議題。本論文主要採用時間和轉換空間領域之混合式均方值適應性演算法來執行時間延遲估測,並將其結果套用至三元陣列測距技術上,以達到較佳的測距效果。最後以電腦模擬方式來證實我們所採用的方法確實優於其他傳統適應性算法。
Passive Sonar systems make use of the target noise received from the acoustic sensor array to determine the target distance and angular. In passive sonar system, we do not transmit sound wave; it equips with the submarine amount. Linear three-element array ranging technique has been one of the most popular approaches among the sensor array ranging systems; it arranges the three sensors of array on an equidistant line. Recently, to improve the flexibility of target ranging system, other approach has been considered, where the three sensors are not arranging on a line and distance between sensors is not equidistant. It is referred to as the non-linear three elements array ranging. To obtain more accurate localization for the Target sources, the techniques for time-delay estimation are vital for target ranging, for both of linear and non- linear three elements array ranging. It directly affects the result of localizing the target sources. In this thesis, we focus on the adaptive hybrid LMS (HLMS) filtering algorithm for extracting the information of time delay between related sensors and apply it for the Target sources ranging. To obtain the accurate delay estimation results, we adopt the hybrid LMS (HLMS) adaptation algorithm; it could achieve better performance regarding TDE and having faster convergence speed property. From simulation results, we verify the merits of the technique mentioned above compared with other conventional approaches for target sources ranging.
CHAPTER 1 INTRODUCTION 1
CHAPTER 2 Adaptive LMS Filtering Algorithm for TDE 4
2.1 Introduction 4
2.2 Time Delay Estimation with Conventional Time Domain Adaptive Constrained LMS Filtering Algorithms 4
2.3 Time Delay Estimation with Adaptive Constrained DCT Domain LMS Filtering Algorithms 9
CHAPTER 3 Localization with Three Elements Array and Adaptive Hybrid LMS Filtering Algorithms 17
3.1 Introduction 17
3.2 Localization with Passive Three Elements Array 18
3.3 Adaptive Hybrid LMS Filtering Algorithms 22
3.4 Computer Simulation Result 25
CHAPTER 4 Conclusions 51
Appendix A TDE Based on SINC Filtering Algorithm 52
Appendix B The Hybrid LMS (HLMS) Algorithm 55
References 59

LIST OF FIGURES
Fig.2.1 The configuration of time domain adaptive filtering scheme for TDE
....................................................................................................................... 5
Fig.2.2 The block diagram of the transformed domain adaptive filtering
scheme for time delay estimation ................................................................. 11
Fig.3.1 Linear three elements array ranging model ..................................... 19
Fig.3.2 Linear three elements array model in far field environment ............ 20
Fig.3.3 Configuration of the time-domain adaptive hybrid LMS filtering
algorithm for TDE........................................................................................ 22
Fig.3.4: The configuration of the adaptive hybrid DCT-LMS filtering
algorithm. ..................................................................................................... 24
Fig.3.5 (a) The conventional LMS algorithm for TDE between sensor A and
B without noise. ........................................................................................... 26
Fig. 3.5 (b) The conventional LMS algorithm for TDE between sensor B and
C without noise. ........................................................................................... 26
Fig. 3.5 (c) The estimate distance with the conventional LMS algorithm
without noise. ............................................................................................... 27
Fig. 3.5 (d) The estimate angular with the conventional LMS algorithm
without noise. ............................................................................................... 27
Fig. 3.6 (a) The constrained LMS algorithm for TDE between sensor A, B
without noise. ............................................................................................... 28
Fig. 3.6 (b) The constrained LMS algorithm for TDE between sensor B, C
without noise. ............................................................................................... 28
Fig. 3.6 (c) The estimate distance with the constrained LMS algorithm
without noise. ............................................................................................... 29
Fig. 3.6 (d) The estimate angular with the constrained LMS algorithm without
noise. ............................................................................................................ 29
Fig. 3.7 (a) The hybrid LMS algorithm for TDE between sensor A, B without
noise. ............................................................................................................ 30
Fig. 3.7 (b) The hybrid LMS algorithm for TDE between sensor B, C without
noise. ............................................................................................................ 30
Fig. 3.7 (c) The estimate distance with the hybrid LMS algorithm without
noise. ............................................................................................................ 31
Fig. 3.7 (d) The estimate angular with the hybrid LMS algorithm without
noise ............................................................................................................. 31
Fig. 3.8 (a) The DCT-LMS algorithm for TDE between sensors A and B
without noise. ............................................................................................... 33
Fig. 3.8 (b) The DCT-LMS algorithm for TDE between sensors B and C
without noise. ............................................................................................... 33
Fig.3.8(c) The estimate distance with the DCT-LMS algorithm without noise
..................................................................................................................... 34
Fig.3.8(d) The estimate angular with the DCT-LMS algorithm without noise
..................................................................................................................... 34
Fig.3.9(a) The DCT-CLMS algorithm for TDE between sensor A, B without
noise. ............................................................................................................ 35
Fig.3.9(b) The DCT-CLMS algorithm for TDE between sensor B and C
without noise. ............................................................................................... 35
Fig.3.9 (c) The estimate distance with the DCT-CLMS algorithm without
noise ............................................................................................................. 36
Fig.3.9 (d) The estimate angular with the DCT-CLMS algorithm without
noise ............................................................................................................. 36
Fig.3.10 (a) The DCT-HLMS algorithm for TDE between sensor A and B
without noise. ............................................................................................... 37
Fig. 3.10 (b) The DCT-HLMS algorithm for TDE between sensor B, C
without noise. ............................................................................................... 37
Fig. 3.10 (c) The estimate distance with the DCT-HLMS algorithm without
noise. ............................................................................................................ 38
Fig. 3.10 (d) The estimate angular with the DCT-HLMS algorithm without
noise. ............................................................................................................ 38
Fig. 3.11 (a) The comparison with different algorithms for TDE between
sensor A and B without noise. ...................................................................... 39
Fig. 3.11 (b) The comparison with different algorithms for TDE between
sensor A and B without noise. ...................................................................... 39
Fig. 3.12 (a) The comparison with different algorithms for TDE between
sensor B and C without noise. ...................................................................... 40
Fig. 3.12 (b) The comparison with different algorithms for TDE between
sensor B and C without noise. ...................................................................... 40
Fig. 3.13 (a) The conventional LMS algorithm for TDE between sensors A
and B for SNR=10dB. .................................................................................. 42
Fig. 3.13 (b) The conventional LMS algorithm for TDE between sensors B
and C for SNR=10dB. .................................................................................. 42
Fig. 3.14 (a) The constrained LMS algorithm for TDE between sensors A and
B for SNR=10dB. ........................................................................................ 43
Fig. 3.14 (b) The constrained LMS algorithm for TDE between sensor B and
C for SNR=10dB. ........................................................................................ 43
Fig. 3.15 (a) The hybrid LMS algorithm for TDE between sensors A and B for
SNR=10dB. .................................................................................................. 44
Fig. 3.15 (b) The hybrid LMS algorithm for TDE between sensors B, C with
SNR=10dB. .................................................................................................. 44
Fig. 3.16 (a) The DCT-LMS algorithm for TDE between sensors A and B
with SNR=10dB. .......................................................................................... 45
Fig. 3.16 (b) The DCT-LMS algorithm for TDE between sensors B and C for
SNR=10dB. .................................................................................................. 45
Fig. 3.17 (a) The DCT-CLMS algorithm for TDE between sensors A and B
for SNR=10dB. ............................................................................................ 46
Fig. 3.17 (b) The DCT-CLMS algorithm for TDE between sensors B and C
for SNR=10dB. ............................................................................................ 46
Fig. 3.18 (a) The DCT-HLMS algorithm for TDE between sensors A and B
for SNR=10dB. ............................................................................................ 47
Fig. 3.18 (b) The DCT-HLMS algorithm for TDE between sensors B and C
for SNR=10dB. ............................................................................................ 47
Fig. 3.19 (a) The comparison with different algorithms for TDE between
sensors A and B for SNR=10dB. .................................................................. 48
Fig. 3.19 (b) The comparison with different algorithms for TDE between
sensors A and B for SNR=10dB. .................................................................. 48
Fig.3.20(a) The comparison with different algorithms for TDE between
sensor B, C without SNR=10dB. ................................................................. 49
Fig.3.20(b) The comparison with different algorithms for TDE between
sensor B, C without SNR=10dB. ................................................................. 49
Figure A.1 The illustration of SINC function. .......................................... 54
Figure B.1 The transformed domain HLMS adaptive filter. ..................... 58


LIST OF TABLES
Table 3.1 The comparison of TDE performance for different algorithms. .. 50
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