【摘要】 研究和分析了多种子空间跟踪算法.直接特征值分解和奇异值分解复杂度高,不利于工程实现,针对低复杂度的PASTd算法由于估计的特征向量不正交,从而导致收敛速度极慢的问题,提出一种改进的PASTd子空间跟踪算法,并将其应用于基于子空间的半盲多用户检测.该算法保证了特征向量的正交性,因此提高了算法的收敛速度.仿真结果表明,提出的算法收敛速度快,输出信干噪比和误码率性能优于PASTd半盲检测算法和OPAST半盲检测算法,逼近SVD半盲检测算法,并保持了较低的计算复杂度.更多还原

【Abstract】 Several algorithms of subspace tracking are investigated.The eigenvalue decomposition(EVD) and singular value decomposition(SVD) are not suited for engineering implementation because of their high computation complexities,while the PASTd algorithm brings about very slow convergence rate because of the nonorthogonality of estimated eigenvectors though its computation complexity is low.An improved PASTd subspace tracking algorithm is therefore proposed to be applied to the subspace-based semi-blind multi-user detector for adaptive subspace estimation.The algorithm proposed can insure the orthogonality of the eigenvectors,thus quickening its convergence rate.Simulation results show that the proposed algorithm is superior to both the PASTd semi-blind multi-user detection and OPAST semi-blind multi-user detection in convergence rate,bit error rate(BER) and output signal interference to noise ratio(SINR),and approaches to the SVD semi-blind multi-user detection with relatively low computing speed kept.更多还原