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State space maximum correntropy filter

机译:状态空间最大熵过滤器

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摘要

The state space recursive least squares (SSRLS) filter is a new addition to the well-known recursive least squares (RLS) family filters, which can achieve an excellent tracking performance by overcoming some limitations of the standard RLS algorithm. However, when the underlying system is disturbed by some heavy-tailed non-Gaussian impulsive noises, the performance of SSRLS will deteriorate significantly. The main reason for this is that the SSRLS is derived under the minimum mean square error (MMSE) criterion, which is not well-suited to estimation problems under non-Gaussian noises. To overcome this issue, we propose in this paper a novel linear filter, called the state space maximum correntropy (SSMC) filter, which is derived under the maximum correntropy criterion (MCC) instead of the MMSE. Since MCC is very suited to non-Gaussian signal processing, the SSMC performs very well in non-Gaussian noises especially when the signals are corrupted by impulsive noises. A simple illustrative example is presented to demonstrate the desirable performance of the new algorithm.
机译:状态空间递归最小二乘(SSRLS)过滤器是对众所周知的递归最小二乘(RLS)系列过滤器的新添加,它可以克服标准RLS算法的某些局限性,从而实现出色的跟踪性能。但是,当基础系统受到一些重尾非高斯脉冲噪声的干扰时,SSRLS的性能将显着下降。这样做的主要原因是SSRLS是根据最小均方误差(MMSE)准则得出的,这不适用于非高斯噪声下的估计问题。为了克服这个问题,我们在本文中提出了一种新颖的线性滤波器,称为状态空间最大熵(SSMC)滤波器,它是根据最大熵准则(MCC)而不是MMSE派生的。由于MCC非常适合非高斯信号处理,因此SSMC在非高斯噪声中的性能非常好,尤其是当信号被脉冲噪声破坏时。给出了一个简单的说明性示例,以演示新算法的理想性能。

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