Further results on cooperative localization via semidefinite programming

As a powerful tool to convert nonconvex problems into convex ones, semidefinite programing (SDP) has been introduced to both cooperative and non-cooperative localization systems. In this paper, we derive the Cramér-Rao Lower Bound (CRLB) for several scenarios to show the advantage of cooperative localization. We then consider cooperative localization via SDP using various semidefinite relaxations, including existing Standard SDP (SSDP), Edge-based SDP (ESDP), Node-based SDP (NSDP) and our proposed Component-wise SDP (CSDP). We analyze their performances and complexity and find that CSDP has advantages in both aspects. Simulations will also be carried out to corroborate our analyses.