In this paper, we propose a robust multilateration algorithm for localizing sensor nodes in cluttered environments where the estimated distances between an unlocalized node and reference nodes with known coordinates may contain large errors due to non line of sight signal propagation. We show that the traditional least squares multilateration is severely affected even if one of the measured distances is erroneous whereas our approach functions properly even if half of the measured distances contain large errors due to non line of sight signals. Our algorithm is independent of the physical layer used to perform ranging and does not require the identification of direct and reflected signals or any prior information about the statistical properties of measurement errors or characterization of the environment where the sensor nodes are deployed.
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