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A Non-Linear Filtering Algorithm Based on Alpha-Divergence Minimization.
- Source :
-
Sensors (14248220) . Oct2018, Vol. 18 Issue 10, p3217. 1p. - Publication Year :
- 2018
-
Abstract
- A non-linear filtering algorithm based on the alpha-divergence is proposed, which uses the exponential family distribution to approximate the actual state distribution and the alpha-divergence to measure the approximation degree between the two distributions; thus, it provides more choices for similarity measurement by adjusting the value of α during the updating process of the equation of state and the measurement equation in the non-linear dynamic systems. Firstly, an α -mixed probability density function that satisfies the normalization condition is defined, and the properties of the mean and variance are analyzed when the probability density functions p (x) and q (x) are one-dimensional normal distributions. Secondly, the sufficient condition of the alpha-divergence taking the minimum value is proven, that is when α ≥ 1 , the natural statistical vector's expectations of the exponential family distribution are equal to the natural statistical vector's expectations of the α -mixed probability state density function. Finally, the conclusion is applied to non-linear filtering, and the non-linear filtering algorithm based on alpha-divergence minimization is proposed, providing more non-linear processing strategies for non-linear filtering. Furthermore, the algorithm's validity is verified by the experimental results, and a better filtering effect is achieved for non-linear filtering by adjusting the value of α. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 18
- Issue :
- 10
- Database :
- Academic Search Index
- Journal :
- Sensors (14248220)
- Publication Type :
- Academic Journal
- Accession number :
- 132629966
- Full Text :
- https://doi.org/10.3390/s18103217