Efficient solutions for target localization in asynchronous MIMO networks

Multiple-input multiple-output (MIMO) technique has been drawing extensive attention for its spatial diversity to enhance the performance in the target localization. Considering a fully asynchronous MIMO network, we propose a differential delay time (DDT) method for target localization. Semidefinite programming (SDP) and closed-form solutions (CFS) always converge to global optimum. To efficiently solve the DDT-based localization problem, we propose a global SDP (GSDP) solution to determine the target position. The complexity of the GSDP solution is high. Hence, we also develop a two-stage CFS (TSCFS) to reduce the complexity. We also prove that the performance of the TSCFS and GSDP solutions is able to attain the Cramér–Rao Lower Bound (CRLB) accuracy. The simulated results show that the GSDP solution provides comparable performance with the TSCFS at the low noise levels. Compared with the TSCFS, the GSDP shows its superiority in the estimation performance at the high noise levels.