In most of the papers I have read, calculation of weights is done by the following formula:
Where L is the dimensionality of my state and lambda is calculated by:
And the usual values for alpha, beta and kappa are given as 0.001, 2 (for Gaussian distributions) and 0 respectively.
My problem is that this would give a value of Lambda that is very close to - L, which drives the weights to huge negative values. This is affecting my covariance estimation, leading to wildly inaccurate predictions. This seems like a fundamental error in the way the weights are calculated, but I'm not sure if I'm missing some critical intuition here.