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Explicit Time Delay Estimation Algorithm Based on Maximum Correntropy Criterion and Approximate Prolate Series

机译:基于最大熵准则和近似Prolate级数的显式时延估计算法

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Time delay estimation (TDE) algorithms have been widely used in many applications, such as digital communications systems, source location, wireless sensor networks. Explicit TDE (ETDE)-based algorithms are very popular because of low computation complexity and sufficient accuracy in real-time application. However, conventional ETDE-based algorithms use the sinc function as filter coefficients, leading to big truncation error with few filter taps. Furthermore, these algorithms have poor performance in Non-Gaussian environment. In this paper, we use approximate prolate function rather than sinc function as filter coefficients to improve the TDE accuracy with small filter taps. And we use correntropy as cost function to restrict Non-Gaussian noise. A novel ETDE algorithm using maximum correntropy criterion (MCC) and approximate prolate series (APS) is proposed in this paper. Simulation results show that the proposed algorithm has better convergence performance and smaller steady-state error in non-Gaussian noise environment.
机译:时间延迟估计(TDE)算法已广泛用于许多应用中,例如数字通信系统,源位置,无线传感器网络。基于显式TDE(ETDE)的算法非常流行,因为它的计算复杂度低且在实时应用中具有足够的准确性。但是,传统的基于ETDE的算法使用sinc函数作为滤波器系数,从而导致截断误差大,而滤波器抽头却很少。此外,这些算法在非高斯环境中的性能较差。在本文中,我们使用近似分布函数而不是Sinc函数作为滤波器系数,以在较小滤波器抽头的情况下提高TDE精度。并且我们使用熵作为代价函数来限制非高斯噪声。提出了一种基于最大熵准则(MCC)和近似分布序列(APS)的ETDE算法。仿真结果表明,该算法在非高斯噪声环境下具有较好的收敛性能和较小的稳态误差。

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