Sparse Bayesian learning (SBL) has given renewed interest to the problem of direction-of-arrival (DOA) estimation. It is generally assumed that the measurement matrix in SBL is precisely known. Unfortunately, this assumption may be invalid in practice due to the imperfect manifold caused by unknown or misspecified mutual coupling. This paper describes a modified SBL method for joint estimation of DOAs and mutual coupling coefficients with uniform linear arrays (ULAs). Unlike the existing method that only uses stationary priors, our new approach utilizes a hierarchical form of the Student t prior to enforce the sparsity of the unknown signal more heavily. We also provide a distinct Bayesian inference for the expectation-maximization (EM) algorithm, which can update the mutual coupling coefficients more efficiently. Another difference is that our method uses an additional singular value decomposition (SVD) to reduce the computational complexity of the signal reconstruction process and the sensitivity to the measurement noise.

Whittaker-Shannon interpolation formula instead of narrowband filter (NF) is used to interpolate the signal more precisely in wideband direction-of-arrival (DOA) estimation problem when the number of samples is highly limited. A novel algorithm named block FOCal Underdetermined System Solver (BFOCUSS) is proposed to solve this problem. The simulation results validate the superior performance compared with the other algorithms.

Computing tasks offload of the edge computing has a significant impact on edge devices energy consumption and load balancing. To reduce the total cost, an offloading strategy that combines energy consumption and load balancing degree is proposed. A task partitioning model is given to perform fine-grained division of computing tasks. Furthermore, the energy consumption model of computing task offloading is obtained through the time delay model, and then the cost function of computing task offloading is constructed in combination with the load balancing degree and energy consumption. With the task offloading strategy, the minimum cost of task offloading is obtained under the multiple constraints, and the path of computing task offloading is determined. The simulation results demonstrate that the strategy can significantly improve the load balancing of the edge server and the overall performance of the edge server.