A novel monostatic MIMO array design strategy and signal reconstruction algorithm for sensor failure

In this paper, combining the signal model that includes sum co-array (SCA) and difference co-array (DCA), a novel sparse linear monostatic MIMO array (SLMA) design strategy is proposed. The extension factor is introduced to reasonably sparsify the physical array. This SLMA design strategy not only improves the degrees of freedom (DOFs) in the virtual array but also enhances the flexibility of array design. Guided by this strategy, various types of free sparse monostatic MIMO arrays (FSMAs) can be obtained. A specific FSMA_ANA-CACIS array is presented. Considering the issue of sensor failure in the application of FSMA_ANA-CACIS, we propose a signal reconstruction algorithm (SRA) to address the problem of data loss caused by failures. Based on the location information of the failed sensors, a simplified atomic norm minimization is formulated to reconstruct the received signals with lower computational complexity. Moreover, the alternating direction method of multipliers is used to solve the optimization problem. Theoretical analysis and simulation results demonstrate that the FSMA effectively increases the DOFs and the SRA successfully mitigates the impact of sensor failures with lower computational complexity.