tailieunhanh - Báo cáo hóa học: " Bounds for 2-D angle-of-arrival estimation with separate and joint processing"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Bounds for 2-D angle-of-arrival estimation with separate and joint processing | Mailaender EURASIP Journal on Advances in Signal Processing 2011 2011 5 http content 2011 1 5 o EURASIP Journal on Advances in Signal Processing a SpringerOpen Journal RESEARCH Open Access Bounds for 2-D angle-of-arrival estimation with separate and joint processing Laurence Mailaender Abstract Cramer-Rao bounds for one- and two-dimensional angle-of-arrival estimation are reviewed for generalized 3-D array geometries. Assuming an elevated sensor array is used to locate sources on a ground plane we give a simple procedure for drawing x-y location confidence ellipses from the Cramer-Rao covariance matrix. We modify the ordinary bounds for the case of separate 1-D estimates and numerically compare this with the full joint bound. We prove that separate processing is optimal for a Uniform Cross Array with a single source and that it is not optimal for the L-shaped array. A trade-off emerges between location accuracy and array height for distant sources increased height generally reduces error. When more than one source is present significant gains are obtained from joint processing. We also show useful gains for distant sources by adding out-of-plane sensors in an L z configuration with joint processing. These comparisons can aid system designers in deciding between separate and joint processing approaches. 1. Introduction Transmitting sources may be located by estimating the angles-of-arrival at a receiving array if the direct path is present . the straight line path between source and destination. Angle-of-arrival AOA estimation may be efficiently performed by well-known approaches such as MUSIC and ESPRIT 1 and the performance of these algorithms has been shown to approach the Cramer-Rao lower bound CRLB at moderate SNR 2 . The CRLB is well-studied for one-dimensional 1-D angle estimation and 1-D bounds have been derived under various assumptions about the source signals . the Conditional Model Assumption CMA 2 3 and the Unconditional .