Lattice recursive least-squares (LRLS) adaptive filters are well known for their fast convergence to achieve the minimum least-squares error. This accelerated decaying error property, however, comes with a high cost filter structure. Using a variable tap-length technique known as the fractional tap-length (FT) algorithm, this article provides an adaptive method to search for the optimum length of LRLS filters. To validate the performance of the proposed algorithm, simulations are conducted under low noise and high noise conditions. All the simulation results show the advantages of the proposed algorithm compared with fixed length LRLS filters as well as with the fractional tap-length lattice based least-mean-squares (FT-LLMS) algorithm. With a fast convergence rate and a small steady-state error of the tap-length, the proposed algorithm can efficiently track any change in the impulse response and estimate the filter's optimum structure.

格递归最小二乘 (LRLS) 自适应滤波器以其快速收敛以实现最小二乘误差而闻名。然而，这种加速衰减的误差特性伴随着高成本的滤波器结构。使用称为分数抽头长度 (FT) 算法的可变抽头长度技术，本文提供了一种自适应方法来搜索 LRLS 滤波器的最佳长度。为了验证所提出算法的性能，在低噪声和高噪声条件下进行了仿真。所有的仿真结果都表明了所提出的算法与固定长度 LRLS 滤波器以及基于分数抽头长度格的最小均方 (FT-LLMS) 算法相比的优势。收敛速度快，抽头长度稳态误差小，