A Time-Space-Time Based Approach for DOA-Delay Joint Estimationin a Wireless Channel

• Main Authors: Yung-Yi Wang, 王永宜
• Other Authors: Wen-Hsien Fang
• Format: Others
• Language: en_US
• Published: 2000
• Online Access: http://ndltd.ncl.edu.tw/handle/14851808166409282830

博士 === 國立臺灣科技大學 === 電子工程系 === 88 === In this dissertation, a unified tree structure algorithm, Time-Space-Time (TST) method, is proposed to jointly estimate the directions of arrival (DOAs) and the propagation delays of a wireless multipath channel. The first contribution of this dissertation is to develop a Temporal-MUltiple SIgnal Classification (T-MUSIC) and a Temporal-Estimation of Signal Parameters via Rotational Invariant Technique (T-ESPRIT) algorithm for the delay time estimation of a multipath channel. To be distinguished, the traditional MUSIC and ESPRIT algorithms for DOA estimation here is referred to as the Spatial-MUSIC (S-MUSIC) algorithm and the Spatial-ESPRIT (S-ESPRIT) algorithm, respectively. The second contribution of this dissertation is that by making use of the space-time characteristics of the multipath channel, the TST method combines the temporal filtering and the spatial beamforming techniques in conjunction with three one-dimensional (1-D) MUSIC (ESPRIT) algorithms, i.e. one S-MUSIC (S-ESPRIT) and two T-MUSIC (T-ESPRIT) algorithms. As such, the incoming rays are grouped, isolated, then estimated, and the paring of the estimated DOAs and delays are automatically determined. Furthermore, the proposed approach can resolve the incoming rays with very close DOAs or delays. In addition, the number of antennas required by the TST-MUSIC (TST-ESPRIT) algorithm can be less than that of the incoming rays. Compared to the algorithm of using the shift invariance property of the channel, the proposed one performs significantly better. Statistical analysis on the subspace distance caused by the residual rays, the error propagation between the three 1-D MUSICs and the variances of the estimates are provided as well to gain further insights of the proposed algorithm.