改进卡尔曼滤波降噪，防止高噪声下输出饱和
Preventing output saturation in active noise control: An output-constrained Kalman filter approach
- 用约束因子调整干扰信号，间接控制输出功率
- 仿真显示可快速抑制动态噪声并避免非线性失真
- 适合硬件输出受限的实时主动降噪场景
基于卡尔曼滤波（KF）的主动降噪（ANC）系统在动态噪声场景中相比最小均方（LMS）方法具有更好的跟踪性能和更快的收敛速度。然而，在极高噪声环境下，控制信号功率可能超过系统额定输出功率，因硬件限制导致输出饱和，引发非线性失真。为此，提出一种带输出约束的改进型卡尔曼滤波方法：将干扰作为测量值，通过系统额定功率、次级路径增益和干扰功率共同决定的约束因子进行重缩放，从而间接将控制信号输出功率限制在系统最大输出范围内，确保系统稳定性。仿真结果表明，该算法不仅实现了动态噪声的快速抑制，还能有效防止因输出饱和引起的非线性失真，具有重要的实际应用价值。
原文摘要 · Abstract (English)
The Kalman filter (KF)-based active noise control (ANC) system demonstrates superior tracking and faster convergence compared to the least mean square (LMS) method, particularly in dynamic noise cancellation scenarios. However, in environments with extremely high noise levels, the power of the control signal can exceed the system's rated output power due to hardware limitations, leading to output saturation and subsequent non-linearity. To mitigate this issue, a modified KF with an output constraint is proposed. In this approach, the disturbance treated as an measurement is re-scaled by a constraint factor, which is determined by the system's rated power, the secondary path gain, and the disturbance power. As a result, the output power of the system, i.e. the control signal, is indirectly constrained within the maximum output of the system, ensuring stability. Simulation results indicate that the proposed algorithm not only achieves rapid suppression of dynamic noise but also effectively prevents non-linearity due to output saturation, highlighting its practical significance.
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