Robust Hybrid Cooperative Positioning Via a Modified Distributed Projection-Based Method

Cooperative positioning is attracting an increasing amount of attention due to its ability to enhance the accuracy and availability of positioning performance. Current algorithms for cooperative positioning are sensitive to the initial guess as a result of their nonconvex objective functions, which is especially true in hybrid wireless networks. Perfect a priori information about the locations is needed, which is rather problematic in many scenarios. With strong convergence, the iterative parallel projection method (IPPM) is extended to hybrid wireless networks (H-IPPM) in this paper. Motivated by the fact that normal weighted methods cannot achieve the optimal solution, the position uncertainty is modeled, and two distributed weighted parallel projection algorithms, namely, an inexact weighted algorithm called the HBFW-IPPM and an exact weighted algorithm called the HCPW-IPPM, are developed when considering both the range measurement errors and position uncertainty. Experiments in a realistic outdoor scenario are conducted. The results indicate that the exact weighted algorithm HCPW-IPPM shows superior and robust performance in both warm-start and cold-start conditions, and this is true even when non-line of sight (NLOS) measurements and weight estimation errors are taken into account.