Probability density function filter design based on symmetric K-L distance

Recently the filtering design based on probability density function become an important method to solve Non-gaussian filtering. However, the existing methods are difficult to use in practice because the performance of non-negative cannot be guaranteed or owning to the highly conservative. So this paper make symmetric K-L distance as a new performance index function and give constraint range of the weighting function. Then present the optimal iteration filtering method based on gradient search technique and numeric integration. Finally, a simulation example is used to illustrate the use of proposed algorithm and desired results have been obtained.