A new indoor localization strategy via node cooperation and iterative detection

Indoor wireless localization suffers from severe multi-path fading and non-line-of-sight conditions. Since many applications involve positioning two or more targeting sensors that are co-located, this paper proposes a novel localization strategy that, through a simple but efficient collaboration between two sensors, effectively exploits both the time-difference-of-arrival (TDOA) and the received-signal-strength (RSS) techniques, each in their best operating region. A maximum likelihood (ML) algorithm is formulated and efficient iterative detection is developed, which is shown to achieve near-ML performance with very low complexity and fast convergence. Theoretical analysis on localization distortion and Monte Carlo simulations show that the proposed cooperative strategy achieves significantly higher localization accuracy compared to the existing systems, especially in heavily obstructed scenarios.