单滤波器延时估计-局部迭代算法

利用目标系统的稀疏性，提出基于延时估计的局部迭代(Delay Estimate-Selective Partial Update, DE-SPU)算法以降低滤波器有效长度。新算法先利用移动窗积分获取目标系统的延时估计以确定活跃系数位置，然后每次迭代均以自适应算法更新全部活跃系数与循环更新一段非活跃系数。活跃系数获得较高的更新频率以提高系统收敛速度，非活跃系数获得较低的更新频率以保证系统的跟踪能力。新算法用一个滤波器完成对稀疏系统的延时估计与活跃系数辨识，可有效避免双滤波器结构的信息冗余。最后以回声消除为应用背景对新算法进行实验仿真，仿真结果验证了新算法的有效性。 

DELAY ESTIMATE-SELECTIVE PARTIAL UPDATE ALGORITHM BASED ON SINGLE FILTER STRUCTURE

Exploiting the sparseness of the objective system, a Selective Partial Update algorithm based on Delay Estimate (DE-SPU) is proposed to decrease the length of the adaptive filter. The new algorithm firstly estimates the bulk delay to locate the active coefficients using moving window integration. Then it updates a segment of the non-active coefficients periodically and all the active coefficients at each adaptation. Active coefficients are assigned with large update probability to increase the convergence speed. Non-active coefficients achieve low update probability to ensure the tracking capability. The new algorithm effectively eliminates the information redundancy of the parallel structure by estimating the delay and identifying the active coefficients using only one filter. Finally, the simulation results in the context of echo cancellation indicate the advantages of the proposed algorithm.