The shallow water noise shows obvious impulsive property, which greatly degrades the direction of arrival (DOA) performance due to the conventional design concept based on the Gaussian assumption. In this paper, DOA e...
详细信息
The shallow water noise shows obvious impulsive property, which greatly degrades the direction of arrival (DOA) performance due to the conventional design concept based on the Gaussian assumption. In this paper, DOA estimator in presence of impulsive noise utilising variationalbayesianinference is proposed. The Middleton's Class A noise model is considered as a typical underwater noise model to analyse the performance of DOA estimation. The DOA estimation problem is modelled as the sparse signal recovery problem, and the hierarchical bayesian learning framework is formulated by considering the common sparsity of signal and the element-wise sparsity of the impulsive noise. The variationalbayesianinference realises the posterior estimation of signal and impulsive noise components. To mitigate the basis mismatches, the root sparse bayesian learning method is applied to refine the steering vectors. Simulations verify the advantages of the proposed DOA method in terms of spatial resolution, root mean square error, accuracy, and robustness compared with the state-of-the-art benchmarks in the presence of Middleton's Class A noise.
A robust adaptive filter is proposed by using the variationalbayesian (VB) inference to extended target tracking with heavy-tailed noise in clutter. An explicit distribution is used to describe the non-Gaussian heavy...
详细信息
A robust adaptive filter is proposed by using the variationalbayesian (VB) inference to extended target tracking with heavy-tailed noise in clutter. An explicit distribution is used to describe the non-Gaussian heavy-tailed noise based on Student's t-distribution. The need for arbitrary decisions is then eliminated, and the robust operation is provided which is less sensitive to extreme observation. Moreover, an approximate measurement update using the analytical techniques of VB methods is derived to approximate the posterior states at each time step. To obtain a more accurate result, clutter estimation is also integrated considering the uncertainty of target tracking in a cluttered environment. The performance of the proposed algorithm is demonstrated with simulated data.
This article studies a variationalbayesianmethod to fix the linear regression (LR) model of which regressors are Gaussian distributed with non-zero prior means, and then apply the method to the linear state space (L...
详细信息
This article studies a variationalbayesianmethod to fix the linear regression (LR) model of which regressors are Gaussian distributed with non-zero prior means, and then apply the method to the linear state space (LSS) model. Here, we innovatively transform the LSS model into a special LR model: In each state, the value obtained from the predict step can be seen as the prior mean of the regressors, and the update step can be viewed as the iterative solving in LR model with non-zero prior means. We simulate the proposed algorithm with high-dimensional discrete LSS models where most states are prior zeros;simulation results show that the proposed algorithm and its applications in LSS are both effective and reliable.
暂无评论