针对传统的扩展卡尔曼滤波方法和无迹卡尔曼滤波方法不能有效地抑制混沌系统的加性噪声这一问题,给出了辅助模型粒子滤波算法,推导了混沌系统的状态空间描述,提出了一种基于辅助模型粒子滤波的混沌信号降噪方法,并将其用于Lorenz混沌信号的降噪。在叠加高斯噪声情况下对混沌系统进行降噪处理实验。结果表明,所提出的降噪方法对含噪Lorenz混沌信号有着较明显的降噪效果。
Abstract
In order to solve the problem of that the traditional extended Kalman filter and unscented Kalman filter methods cannot effectively suppress the additive noise of the chaotic system, an improved particle filter algorithm is presented. The state space description of the chaotic system is derived in detail, and a noise reduction method of chaotic signal based on auxiliary sigma point particle filter is proposed. As an example,Lorenz chaotic signal is used to evaluate the efficiency of the proposed algorithm. The results show that the proposed algorithm has obvious effect on noise reduction for Lorenz chaotic signal with noise.
关键词
信息处理技术 /
粒子滤波 /
混沌信号 /
降噪
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Key words
information processing technology /
particle filter /
chaotic signal /
noise reduction
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