Improved receiver tracking models for scintillation monitoring

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 scintillation, can impair the tracking performance of a GNSS receiver, leading to increased phase and Doppler errors, cycle slips and sometimes to complete losses of signal lock. In order to mitigate scintillation effects at receiver level, the robustness of the carrier tracking loop, the receiver’s weakest link under scintillation, must be enhanced. Thanks to their adaptive nature, 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 scenarios. This research work 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, two novel self-tuning KF based carrier tracking algorithms are proposed. They self-tune their dynamic models by exploiting the knowledge about scintillation, which is achieved by estimating a number of scintillation indices. These types of tracking schemes are particularly suitable for ionospheric scintillation monitor receivers, which are designed for the computation of scintillation indices and other related parameters. Moreover, this thesis analyses and implements algorithms for a reliable computation of scintillation indices even when low cost receivers are exploited. Furthermore, a technique is proposed to compute scintillation indices even if temporary losses of signal lock or cycle slips occur. All algorithms have been assessed by exploiting both simulated and real data affected by high latitude and equatorial scintillation. Results show that the proposed algorithms are able to maintain the signal lock and provide reliable scintillation indices when classical architectures and commercial Ionospheric Scintillation Monitoring Receivers (ISMRs) fail

Tuning a Kalman filter carrier tracking algorithm in the presence of ionospheric scintillation

© 2017, The Author(s). 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

Improved receiver tracking models for scintillation monitoring

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 scintillation, can impair the tracking performance of a GNSS receiver, leading to increased phase and Doppler errors, cycle slips and sometimes to complete losses of signal lock. In order to mitigate scintillation effects at receiver level, the robustness of the carrier tracking loop, the receiver’s weakest link under scintillation, must be enhanced. Thanks to their adaptive nature, 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 scenarios. This research work 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, two novel self-tuning KF based carrier tracking algorithms are proposed. They self-tune their dynamic models by exploiting the knowledge about scintillation, which is achieved by estimating a number of scintillation indices. These types of tracking schemes are particularly suitable for ionospheric scintillation monitor receivers, which are designed for the computation of scintillation indices and other related parameters. Moreover, this thesis analyses and implements algorithms for a reliable computation of scintillation indices even when low cost receivers are exploited. Furthermore, a technique is proposed to compute scintillation indices even if temporary losses of signal lock or cycle slips occur. All algorithms have been assessed by exploiting both simulated and real data affected by high latitude and equatorial scintillation. Results show that the proposed algorithms are able to maintain the signal lock and provide reliable scintillation indices when classical architectures and commercial Ionospheric Scintillation Monitoring Receivers (ISMRs) fail

Gait Analysis for Pedestrian Navigation Using MEMS Handheld Devices

Advances in Micro-Electro-Mechanical Systems (MEMS) technology play a central role in the design of new generation of smartphones. Indeed MEMS sensors, such as accelerometers and gyroscopes, are currently embedded in most smart devices in order to augment their capabilities. In the near future, it is expected that these sensors will be further exploited for pedestrian navigation purposes. However, the processing of signals from MEMS sensors cannot provide accurate navigation solutions without external aiding, e.g. from GNSS (Global Navigation Satellite Systems) signals, since their signals deteriorate due to significant errors, principally biases and drift which requires frequent sensor resets. When GNSS is not available and the sensors are mounted on the user’s foot, periodic zero velocity updates can be performed during the identified stance phases of the foot, namely the periods when the foot is flat on the ground. In the case of handheld devices, this approach cannot be adopted, since zero velocity periods are not present. Furthermore, when the sensors are held in a hand, the sensed motion can be decoupled from the global user’s motion rendering the situation much more complex to deal with. For this reason previous studies on pedestrian navigation are mainly focused on the body fixed sensor case. In this thesis, algorithms for characterizing the gait cycle of a pedestrian holding an IMU (Inertial Measurement Unit) in hand are proposed but without constraining the user in its behaviour and thus taking into account several sensor carrying modes. In view of the variety and complexity of human motions, the recognition of the user’s global motion from handheld devices is first thoroughly examined. A classifier able to recognize several different motion modes, including standing, walking, running, climbing and descending the stairs, is designed and implemented. Then an algorithm for evaluating the linear displacement of a pedestrian walking on a flat plane using only a handheld IMU is proposed. The complete algorithm comprises the following three modules: (1) Characterization of the user's activity and recognition of the sensor carrying mode, (2) Step detection and (3) Step length evaluation. The analysis leads to a novel step length model combining the user’s height, the step frequency and a set of three constants. First a universal model is proposed where the three constants have been trained with 12 different test subjects. Then, the same model is used for 10 different subjects to calibrate individually the set of constants. The validity of both universal and calibrated models is assessed in position domain using the above 10 test subjects. The fitted solution achieves an error between 2.5 and 5 % of the travelled distance, which is comparable with the performance of models proposed in the literature for body fixed sensors.

Motion mode recognition and step detection algorithms for mobile phone user

sensor

Step Length Estimation Using Handheld Inertial Sensors

sensor

Design of a robust receiver architecture for scintillation monitoring

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