No abstract

Low cost MEMS sensors typically result in large position errors after very short periods of time unless they are frequently corrected by measurements from other systems. One form of measurements comes from the computer vision community where successive frames from a camera approximately looking at the ground can be used to compute the translation between frames. These measurements can be used to control the drift of an Inertial Measurement Unit (IMU) when measurements from other systems such as GPS are not available. This configuration of sensors is preferable since they are already available on some smartphones. This paper demonstrates that computer vision measurements can significantly reduce the drift of IMU-only positioning with a view for pedestrian navigation indoors. Issues such as computational requirements and operation in low light areas are also discussed.

No abstract

Strong ionospheric electron content gradients may lead to fast and unpredictable fluctuations in the phase and amplitude of the signals from Global Navigation Satellite Systems (GNSS). This phenomenon, known as ionospheric scintillation, is capable of deteriorating the tracking performance of a GNSS receiver, leading to increased phase and Doppler errors, cycle slips and also to complete losses of signal lock. In order to mitigate scintillation effects at receiver level, the robustness of the carrier tracking loop, the receiver weakest link under scintillation, must be enhanced. Kalman filter (KF)-based tracking algorithms are particularly suitable to cope with the variable working conditions imposed by scintillation. However, the effectiveness of this tracking approach strongly depends on the accuracy of the assumed dynamic model, which can quickly become inaccurate under randomly variable situations. This study first shows how inaccurate dynamic models can lead to a KF suboptimum solution or divergence, when both strong phase and amplitude scintillation are present. Then, to overcome this issue, it proposes two self-tuning KF-based carrier tracking algorithms, which self-tune their dynamic models by exploiting the knowledge about scintillation that can be achieved through scintillation monitoring. The algorithms have been assessed with live equatorial data affected by strong scintillation. Results show that the algorithms are able to maintain the signal lock and provide reliable scintillation indices when classical architectures and commercial ionospheric scintillation monitoring receivers fail.

Global Navigation Satellite Systems (GNSS) signals traversing small scale irregularities present in the ionosphere may experience fast and unpredictable fluctuations of their amplitude and phase. This phenomenon can seriously affect the performance of a GNSS receiver, decreasing the position accuracy and, in the worst scenario, also inducing a total loss of lock on the satellite signals. This paper proposes an adaptive Kalman Filter (KF) based Phase Locked Loop (PLL) to cope with high dynamics and strong fading induced by ionospheric scintillation events. The KF based PLL self-tunes the covariance matrix according to the detected scintillation level. Furthermore, the paper shows that radio frequency interference can affect the reliable computation of scintillation parameters. In order to mitigate the effect of the interference signal and filter it out, a wavelet based interference mitigation algorithm has been also implemented. The latter is able to retrieve genuine scintillation indices that otherwise would be corrupted by radio frequency interference.