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研究生:吳承勳
研究生(外文):Cheng-Shiun Wu
論文名稱:改良式多重目標物之追蹤演算法
論文名稱(外文):Improved Tracking Algorithm for Multiple Targets
指導教授:洪賢昇
指導教授(外文):Hsien-Sen Hung
學位類別:碩士
校院名稱:國立臺灣海洋大學
系所名稱:電機工程學系
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:2007
畢業學年度:95
語文別:中文
論文頁數:64
中文關鍵詞:訊號子空間方位估測投影近似子空間追蹤近場追蹤多項式解根法卡爾曼濾波器角度預估
外文關鍵詞:signal subspacedirection-of-arrival estimationPASTnear-fieldtrackingpolynomial rootingKalman Filterpredict angle
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結合子空間追蹤與MUSIC方位估測所形成的適應性MUSIC演算法(稱為AMUSIC),可用來估測多訊號源的移動軌跡。然而當訊號源互相接近甚至交錯時,此方法所估測的方位角會有嚴重的誤差,導致無法正確的追蹤訊號源。
為了有效解決上述的問題,本論文提出一個改良式的適應性近場訊號源追蹤演算法(稱之為KF-AMUSIC)。此方法利用AMUSIC所得到的方位估測角作為卡爾曼濾波器(KF)的輸入資料,並由所提的訊號源判定機制來決定訊號源的歸屬,以作為KF的狀態估測值更新與否之依據。藉由KF的狀態估測與預估特性,KF-AMUSIC可進一步改善方位估測的精確度及訊號源的追蹤能力。
An adaptive MUSIC method (known as AMUSIC), based on subspace tracking and MUSIC, can be used for tracking multiple signal sources. However, when the signal sources are very close or even crossed, AMUSIC produces abnormally large errors in the angle estimation, thus leading to erroneous tracking results.
In order to overcome the aforementioned problem, we propose a improved adaptive tracking algorithm, known as KF-AMUSIC, for tracking near-field sources. This method uses angle estimates obtained from AMUSIC as input data for Kalman Filter, and determines a correspondence between angle estimates and signal sources in order to decide whether state estimates should be updated or not. Through the state estimation and prediction in KF, the proposed algorithm can improve the accuracy of angle estimate and tracking capability of signal sources.
目錄
第一章 緒論 1
1.1 簡介…………………………………………………… 1
1.2 研究動機與方法……………………………………… 3
1.3 論文架構介紹………………………………………… 5
第二章 以訊號子空間為主的二維近場訊號源定位與追蹤演算法 6
2.1 簡介…………………………………………………… 6
2.2 近場源的訊號模型…………………………………… 7
2.3 近場2-D MUSIC估測法 ……………………………… 9
2.4 多項式解根法…..…………………………………… 13
2.5 SW-OPAST演算法………..…………………………… 17
2.6 Adaptive 2-D MUSIC定位法………………………… 20
第三章 基於角度預估之改良式多重目標物追蹤演算法 26
3.1 簡介…………………………………………………… 26
3.2 卡爾曼濾波器之介紹………………………………… 27
3.3 卡爾曼濾波器之運算流程…………………………… 28
3.4 KF-AMUSIC …………………………………………… 33
3.5 電腦模擬結果與分析………………………………… 39
3.51 KF-AMUSIC與AMUSIC演算法之性能比較…… 40
3.52 KF-AMUSIC針對訊號源互相交錯之追蹤性能 43
3.53 比較KF-AMUSIC和PARK所提之方法………… 46
第四章 結論與未來展望 49
參考文獻 ………………………………………………………… 51
圖表目錄 ………………………………………………………… II
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