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.

中文翻译：

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协作无线电导航的同步定位和校准


协作无线电定位和导航系统可用于无法接收或受损的全球导航卫星系统 (GNSS) 信号的情况。虽然许多论文都强调了合作的好处，但校准并未得到广泛考虑，但在实践中同样重要。利用信号传播时间需要群延迟或测距偏差校准，而估计到达方向 (DoA) 需要天线响应校准。通常，校准参数仅在操作前确定一次。然而，校准参数会受到例如射频(RF)部件的温度变化或天线周围环境变化的影响。为了解决这个问题，我们推导了一种基于贝叶斯滤波的协作同步定位和校准（SLAC）算法，该算法可以同时估计天线响应和测距偏差以及位置和方向。通过模拟，我们表明可以在运行过程中估计校准参数，而无需额外的传感器。我们通过评估机器人流动站的测量数据进一步证明 SLAC 的实际适用性。借助 SLAC，测距和 DoA 估计性能均得到改善，从而获得更好的位置和方向估计精度。因此，SLAC 能够提供可靠的校准并减少模型失配。最后，我们讨论开放的研究问题和 SLAC 的可能扩展。