Positioning systems are crucial in multiple fields such as Internet of Things (IoT) due to their wide-ranging applications across smart environments and industries. Existing methods for global and local positioning are not effective in certain scenarios. This paper presents an advanced mutual positioning solution that combines a randomized stochastic optimization algorithm tailored for dynamic systems under unknown-but-bounded disturbances with a consenus method. The proposed method addresses measurement noise while maintaining computational efficiency. The approach is validated through numerical simulations, demonstrating its effectiveness in real-time positioning tasks within complex networks. © 2025 IEEE.