結合自適性波束構成濾波器之多目標源訊號特徵萃取與追蹤之學術研究;Study on Feature Extractions of Mulitiple Targets Using the Adaptive Beam-Forming Technique

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/49691

Title:	結合自適性波束構成濾波器之多目標源訊號特徵萃取與追蹤之學術研究;Study on Feature Extractions of Mulitiple Targets Using the Adaptive Beam-Forming Technique
Authors:	李柏磊
Contributors:	電機工程學系
Keywords:	研究領域：電子電機工程類
Date:	2011-01-01
Issue Date:	2012-01-17 19:08:05 (UTC+8)
Publisher:	行政院國家科學委員會
Abstract:	現代電子戰著重於如何有效過濾雜訊與去除敵方電子干擾，有效萃取目標雷達訊號的特徵（如頻譜、時間訊號特徵等）。為了有效進行多目標偵測與即時定位，萃取不同目標之雷達信號特徵。本計畫將結合獨立成份分析法(Independent Component Analysis, ICA)與希爾伯黃轉換(Hilbert-Huang Transform, HHT)於多目標源訊號特徵萃取之自適性波束構成(Beam forming)濾波器的設計應用上，藉由獨立成份分析法（ICA）、波束構成(Beam Forming)濾波器、與希爾伯黃轉換(HHT)的基礎，我們將利用獨立成份分析法（ICA）的訊號分離特性，考慮各物體之雷達訊號彼此為統計獨立的特性，將各訊號源予以分離，並經由訊號的時間、頻率、空間特性，選擇適當的獨立成份(Independent Component)進行訊號重建，以達到分離並濾除背景雜訊的目的。藉由獨立成份分析法（ICA）的訊號與雜訊分離，我們可以找到波束構成濾波器設計上，最重要的訊雜比(Signal-to-noise Ratio, SNR)參數，提供波束構成濾波器的訊號相關矩陣(Autocorrelation Matrix)與雜訊變異矩陣(Covariance Matrix)，作為維納濾波器(Wiener Filter)的波束構成濾波器設計基礎。最後，我們將採用希爾伯黃轉換（HHT），對波束構成濾波器所萃取的訊號進行時頻特徵分析(Time-frequency Analysis)，利用希爾伯黃轉換（HHT）的可適性訊號處理特性，進一步將待測物的雷達訊號分解為許多具有不同時頻特徵的經驗模態函數(Intrinsic Mode Function, IMF)，並建立資料庫予以分類整理。 Modern electronic warfare focuses on the effectives of technology developments in noise reduction, radio interference removal, and how to extract meaning features of target signals. To achieve real-time geolocation and simultaneous detection of multiple targets, this project intends to develop a new adaptive beam forming technique, in which the signal detection is based on the combination of the independent component analysis (ICA) and the Hilbert Huang transform (HHT). We utilize the advantage of ICA in blind source separation, to separate target-related signals and target-unrelated noises. The ICA separates the multi-channel signals into a series of independent components (IC), and the target-related signals can then be reconstructed according to the temporal, spectral and spatial features of interested targets, in order to achieve the purpose of noise removal. Since the signal-to-noise ratio (SNR) has been recognized as an important parameter in designing an adaptive beam forming filter, well separation of the target signals and noises will be helpful to provide the signal auto-correlation matrix and noise covariance matrix for designing an effective beam-forming filter. The extracted target-related signals will be subsequently analyzed using HHT to decompose the reconstructed signal into a series of intrinsic mode functions (IMF). A database will be further established to facilitate the recognition of detected targets based on their temporal-frequency features. 研究期間：10001 ~ 10012
Relation:	財團法人國家實驗研究院科技政策研究與資訊中心
Appears in Collections:	[Department of Electrical Engineering] Research Project

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