Reduced-Bias ML-Based Estimators with Low Complexity for Self-Calibrating RSS Ranging

Abstract

The paper deals with the problem of distance estimation (ranging) between nodes of a wireless system, relevant e.g. to range-based localization. The case of Received Signal Strength (RSS) measurements is addressed, where the Path Loss model (PLM) is adopted to infer the unknown distance via Maximum Likelihood (ML) estimation. In the paper it is shown that, although the ML-based approach can provide unbiased estimates when the PLM parameters are known, it may be severely biased in the real case of self-calibration via estimated parameters. The bias is characterized in detail, and nonlinear effects depending on system aspects are highlighted through the analysis. Novel reduced-bias estimators with low complexity are then derived, and their effectiveness is demonstrated via Monte Carlo simulations and illustrative experimental results by GNU Radio IEEE 802.15.4 receiver and COTS ZigBee nodes.