School of Energy and Power Engineering, Beihang University, Beijing 100191, China.
School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China.
Sensors (Basel). 2022 Jun 17;22(12):4566. doi: 10.3390/s22124566.
The filtered-x recursive least square (FxRLS) algorithm is widely used in the active noise control system and has achieved great success in some complex de-noising environments, such as the cabin in vehicles and aircraft. However, its performance is sensitive to some user-defined parameters such as the forgetting factor and initial gain. Once these parameters are not selected properly, the de-noising effect of FxRLS will deteriorate. Moreover, the tracking performance of FxRLS for mutation is still restricted to a certain extent. To solve the above problems, this paper proposes a new proportional FxRLS (PFxRLS) algorithm. The forgetting factor and initial gain sensitivity are successfully reduced without introducing new turning parameters. The de-noising level and tracking performance have also been improved. Moreover, the momentum technique is introduced in PFxRLS to further improve its robustness and de-noising level. To ensure stability, its convergence condition is also discussed in this paper. The effectiveness of the proposed algorithms is illustrated by simulations and experiments with different user-defined parameters and time-varying noise environments.
滤波-x 递归最小二乘（FxRLS）算法在主动噪声控制系统中得到了广泛的应用，并在一些复杂的去噪环境中取得了巨大的成功，例如车辆和飞机的机舱。然而，其性能对一些用户定义的参数（如遗忘因子和初始增益）敏感。一旦这些参数选择不当，FxRLS 的去噪效果就会恶化。此外，FxRLS 对突变的跟踪性能仍然受到一定程度的限制。为了解决上述问题，本文提出了一种新的比例 FxRLS（PFxRLS）算法。成功降低了遗忘因子和初始增益的敏感性，而无需引入新的转向参数。去噪水平和跟踪性能也得到了提高。此外，在 PFxRLS 中引入了动量技术，以进一步提高其鲁棒性和去噪水平。为了确保稳定性，本文还讨论了其收敛条件。通过不同用户定义参数和时变噪声环境的仿真和实验验证了所提出算法的有效性。
