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We present a general Bernoulli Gaussian scale mixture based approach for modeling priors that can represent a large class of random signals. For inference, we introduce belief propagation (BP) to multi-snapshot signal recovery based on the minimum mean square error estimation criteria. Our method relies on intra-snapshot messages that update the signal vector for each snapshot and inter-snapshot messages that share probabilistic information related to the common sparsity structure across snapshots. Despite the very general model, our BP method can efficiently compute accurate approximations of marginal posterior PDFs. Preliminary numerical results illustrate the superior convergence rate and improved performance of the proposed method compared to approaches based on sparse Bayesian learning (SBL). 

We present a general Bernoulli Gaussian scale mixture based approach for modeling priors that can represent a large class of random signals. For inference, we introduce belief propagation (BP) to multi-snapshot signal recovery based on the minimum mean square error estimation criteria. Our method relies on intra-snapshot messages that update the signal vector for each snapshot and inter-snapshot messages that share probabilistic information related to the common sparsity structure across snapshots. Despite the very general model, our BP method can efficiently compute accurate approximations of marginal posterior PDFs. Preliminary numerical results illustrate the superior convergence rate and improved performance of the proposed method compared to approaches based on sparse Bayesian learning (SBL). 

Multiobject tracking (MOT) is an important task in robotics, autonomous driving, and maritime surveillance. Traditional work on MOT is model-based and aims to establish algorithms in the framework of sequential Bayesian estimation. More recent methods are fully data-driven and rely on the train- ing of neural networks. The two approaches have demonstrated advantages in certain scenarios. In particular, in problems where plenty of labeled data for the training of neural networks is available, data-driven MOT tends to have advantages compared to traditional methods. A natural thought is whether a general and efficient framework can integrate the two approaches. This paper advances a recently introduced hybrid model-based and data-driven method called neural-enhanced belief propagation (NEBP). Compared to existing work on NEBP for MOT, it introduces a novel neural architecture that can improve data association and new object initialization, two critical aspects of MOT. The proposed tracking method is leading the nuScenes LiDAR-only tracking challenge at the time of submission of this paper.