基于变步长最小均方误差自适应滤波器的变压器机器鱼避障时延仿真

为了解决变压器机器鱼避障过程中时延信息难以准确估计的问题，提出一种基于变步长LMS（最小均方误差）自适应滤波器的时延估计方法。不同于传统的互相关时延估计算法，变步长LMS自适应滤波器算法不需要信号和噪声的统计先验知识，具有更好的适用性。算法根据最小均方误差准则和最速下降法对滤波器的输出和权系数向量进行自适应调节，迭代过程中采用变步长代替传统的固定步长提高了收敛速度，然后对权系数向量进行Sinc函数插值获得时延估计结果。仿真分析了不同信噪比条件的时延估计性能，并且与传统互相关算法进行了比较，结果表明所提算法相对于传统互相关算法具有更好的抗噪性和更明显的时延估计峰值。

Obstacle Avoidance Time Delay Simulation of Transformer Robot Fish Based on Variable Step Size LMS Adaptive Filtering

It is difficult to accurately estimate the time delay between the transmitted signal and echo signal in the process of obstacle avoidance of the transformer robot fish. A time-delay estimation method based on the improved variable step size LMS (Least Mean Square) algorithm is proposed. Different from the traditional BCC (basic cross-correlation) algorithm, the LMS algorithm does not need prior statistical knowledge of signal and noise and has better applicability. The algorithm adaptively adjusts the output and weight vector of the filter according to the least mean square error criterion and the fastest descent method. In the iteration process, the variable step size is used instead of the traditional fixed step size to improve the convergence speed. Finally, the Sinc function interpolation is performed on the weight vector to obtain the delay estimation result. Simulation results show that the proposed algorithm has better anti-noise performance and more obvious peak time delay estimation than the traditional BCC algorithm.