Simultaneous Localization and Calibration for Cooperative Radio Navigation

Abstract

Cooperative radio localization and navigation systems can be used in scenarios where the reception of global navigation satellite system (GNSS) signals is not possible or impaired. While the benefit of cooperation has been highlighted by many papers, calibration is not widely considered, but equally important in practice. Utilizing the signal propagation time requires group delay or ranging bias calibration and estimating the directions-of-arrival (DoAs) requires antenna response calibration. Often, calibration parameters are determined only once before operation. However, the calibration parameters are influenced by e.g. changing temperatures of radio frequency (RF) components or changing surroundings of antennas. To cope with that, we derive a cooperative simultaneous localization and calibration (SLAC) algorithm based on Bayesian filtering, which estimates antenna responses and ranging biases simultaneously with positions and orientations. By simulations, we show that the calibration parameters can be estimated during operation without additional sensors. We further proof practical applicability of SLAC by evaluating measurement data from robotic rovers. With SLAC, both ranging and DoAs estimation performance is improved, resulting in better position and orientation estimation accuracy. SLAC is thus able to provide reliable calibration and to mitigate model mismatch. Finally, we discuss open research questions and possible extensions of SLAC.