臺灣博碩士論文加值系統

於本論文中，在均勻矩形陣列下，我們提出一個快速而準確的適應性二維方位角追蹤演算法。基於資料中的單一列或單一行，這個新的演算法使用三個一維子空間追蹤演算法，以疊代的方式求出二維方位角。同樣地，為了要提升估測的準確度，這些一維子空間追蹤演算法結合了兩個互補正交的束波形成的程序，可以適當地將被接收訊號做分群，以致於，即使訊號源的方位角非常靠近，我們也可以解析出來。由於這個新演算法只需要一維子空間演算法來求出方位角，所以全部需要的運算複雜度大大地少於現存中二維延伸的子空間演算法，而主要原因是二維子空間演算法需要更新較高維度的特徵質分解或是二維角度的掃瞄。此外，由於樹狀的估測結構，估測出的二維方位角會自動配對，而不再需要額外的運算量。

在無線通訊系統中，相同的技術可以被使用於方位角及延遲時間的結合估測。而利用一個時域濾除及空間束波形成的程序，我們所提出的樹狀結構的方法可以解析非常靠近之入射信號。此外，我們同樣地可以自動配對被接受訊號的方位角及時間延遲。

由已完成的模擬結果可以驗證我們提出的新演算法，不管是在二維方位角或是結合方位角及時間延遲的估測，都可以提供良好的追蹤效能。

In this thesis, we present a fast, yet accurate adaptive algorithm
for tracking two-dimensional (2-D) directions of arrival (DOAs)
using a uniform rectangular array. The new algorithm employs three
one-dimensional (1-D) subspace tracking algorithms to determine
the two DOA components iteratively in a coarse-fine manner based
on a single row or column of data. Also, to enhance the estimation
accuracy, two complementary orthogonal beamforming processes are
invoked between the 1-D subspace tracking algorithms to partition
the incoming signals into appropriate groups so that the DOAs can
be well resolved even if they are very close. Since the new
algorithm only involves the 1-D subspace-based algorithm in
determining the DOAs, the overall computational complexity called
for is substantially less than the direct 2-D extension of the
existing subspace tracking algorithms, which requires an update
higher-dimensional eigendecomposition or 2-D search.
%computationally demanding 2-D search.
Furthermore, with the tree-structured estimation scheme, the
estimated 2-D DOA components are automatically paired without
extra computational overhead.

The similar technique is also employed for joint DOA-delay
estimation in wireless communication systems. Using a temporal
filtering process and a spatial beamforming process, the proposed
tree-structured approach can resolve incoming rays even with very
close DOAs or delays. In addition, the estimated DOAs and delays
are also automatically paired.

Furnished simulations show that the new algorithm can provide
satisfactory tracking performance of either the 2-D DOA or
DOA-delay estimates in various scenarios.

1 INTRODUCTION 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Organization of the Thesis . . . . .. . . . . . . . . . . . . . 4
2 REVIEW OF DOA ESTIMATION ALGORITHM 6
2.1 Data Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Subspace-Based DOA Estimation . . . . . . . . . . . . . . . . . 8
2.2.1 MUSIC Algorithm . . . . . . . . . . . . . . . . . . . . . . . 8
2.2.2 Root-MUSIC Algorithm . . . . . . . . . . . . . . . . . . . . 11
2.2.3 ESPRIT Algorithm . . . . . . . . . . . . . . . . . . . . . . 13
2.3 Adaptive Subspace-Based DOA Tracking . . . . . . . . . . . . . 15
2.3.1 Inflation Method . . . . . . . . . . . . . . . . . . . . . . 16
2.3.2 PAST Algorithm . . . . . . . . . . . . . . . . . . . . . . . 24
3 FAST ADAPTIVE ALGORITHM FOR EIGENSUBSPACE-BASED
2-D DOA TRACKING 29
3.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.2 2-D DOA Tracking Algorithm . . . . . . . . . . . . . . . . . . 31
3.3 Proposed Tree-Structured DOA Tracking Algorithm . . . . . . . 33
3.4 Simulations and Discussions . . . . . . . . . . . . . . . . . 40
3.4.1 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 43
4 FAST ADAPTIVE JOINT DOA AND DELAY TRACKING IN
DS-CDMA SYSTEM 48
4.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.2 Adaptive Joint DOA and Delay tracking . . . . . . . . . . . 50
4.3 Simulation and Discussion . . . . . . . . . . . . . . . . . . 53
4.3.1 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 55
5 CONCLUSIONS 58
REFERENCE 60

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