Abstract
Distance estimation, which arises in many applications and especially in range-based localization, is addressed for joint received signal strength (RSS) and time of arrival (TOA) data. A statistical characterization of the joint maximum likelihood estimator, which is unavailable in closed-form, is provided together with a full performance assessment in terms of the actual mean squared error (MSE), in order to establish when hybrid estimation is superior compared to RSS-only or TOA-only estimation. Furthermore, a novel closed-form estimator is proposed based on an ad-hoc relaxation of the likelihood function, which removes the need to adopt iterative methods for hybrid TOA/RSS ranging and strikes a better bias-variance tradeoff for improved performance. A thorough theoretical analysis, corroborated by numerical simulations, shows the effectiveness of the proposed approach, which outperforms state-of-the-art solutions.
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