Multi-Antenna Vision-and-Inertial-Aided CDGNSS for Micro Aerial Vehicle Pose Estimation

A system is presented for multi-antenna carrier phase differential GNSS (CDGNSS)-based pose (position and orientation) estimation aided by monocular visual measurements and a smartphone-grade inertial sensor. The system is designed for micro aerial vehicles, but can be applied generally for low-cost, lightweight, high-accuracy, geo-referenced pose estimation. Visual and inertial measurements enable robust operation despite GNSS degradation by constraining uncertainty in the dynamics propagation, which improves fixed-integer CDGNSS availability and reliability in areas with limited sky visibility. No prior work has demonstrated an increased CDGNSS integer fixing rate when incorporating visual measurements with smartphone-grade inertial sensing. A central pose estimation filter receives measurements from separate CDGNSS position and attitude estimators, visual feature measurements based on the ROVIO measurement model, and inertial measurements. The filter's pose estimates are fed back as a prior for CDGNSS integer fixing. A performance analysis under both simulated and real-world GNSS degradation shows that visual measurements greatly increase the availability and accuracy of low-cost inertial-aided CDGNSS pose estimation.Aerospace Engineering and Engineering Mechanic

New on-board multipurpose architecture integrating modern estimation techniques for generalized GNSS based autonomous orbit navigation

This dissertation investigates a novel Multipurpose Earth Orbit Navigation System (MEONS) architecture aiming at providing a generalized GNSS based spacecraft orbit estimation kernel matching the modern navigation instance of enhanced flexibility with respect to multiple Space Service Volume (SSV) applications (Precise Orbit Determination for Earth Observation satellite, Low Thrust Low to High Autonomous Orbit Rising, formation flying relative navigation, Small Satellite Autonomous Orbit Acquisition). The possibility to address theoretical and operational solutions within a unified framework is a foundamental step for the implementation of a reusable and configurable high performance navigation capability on next generation platforms

Trajectory determination and analysis in sports by satellite and inertial navigation

This research presents methods for performance analysis in sports through the integration of Global Positioning System (GPS) measurements with Inertial Navigation System (INS). The described approach focuses on strapdown inertial navigation using Micro-Electro-Mechanical System (MEMS) Inertial Measurement Units (IMU). A simple inertial error model is proposed and its relevance is proven by comparison to reference data. The concept is then extended to a setup employing several MEMS-IMUs in parallel. The performance of the system is validated with experiments in skiing and motorcycling. The position accuracy achieved with the integrated system varies from decimeter level with dual-frequency differential GPS (DGPS) to 0.7 m for low-cost, single-frequency DGPS. Unlike the position, the velocity accuracy (0.2 m/s) and orientation accuracy (1 – 2 deg) are almost insensitive to the choice of the receiver hardware. The orientation performance, however, is improved by 30 – 50% when integrating four MEMS-IMUs in skew-redundant configuration. Later part of this research introduces a methodology for trajectory comparison. It is shown that trajectories based on dual-frequency GPS positions can be directly modeled and compared using cubic spline smoothing, while those derived from single-frequency DGPS require additional filtering and matching

Precise Orbit Determination of CubeSats

CubeSats are faced with some limitations, mainly due to the limited onboard power and the quality of the onboard sensors. These limitations significantly reduce CubeSats' applicability in space missions requiring high orbital accuracy. This thesis first investigates the limitations in the precise orbit determination of CubeSats and next develops algorithms and remedies to reach high orbital and clock accuracies. The outputs would help in increasing CubeSats' applicability in future space missions

Benefits from a multi-receiver architecture for GNSS precise positioning

Precise positioning with a stand-alone GPS receiver or using differential corrections is known to be strongly degraded in an urban or sub-urban environment due to frequent signal masking, strong multipath effect, frequent cycle slips on carrier phase, etc. The objective of this Ph.D. thesis is to explore the possibility of achieving precise positioning with a low-cost architecture using multiple installed low-cost single-frequency receivers with known geometry whose one of them is RTK positioned w.r.t an external reference receiver. This setup is thought to enable vehicle attitude determination and RTK performance amelioration. In this thesis, we firstly proposed a method that includes an array of receivers with known geometry to enhance the performance of the RTK in different environments. Taking advantage of the attitude information and the known geometry of the installed array of receivers, the improvement of some internal steps of RTK w.r.t an external reference receiver can be achieved. The navigation module to be implemented in this work is an Extended Kalman Filter (EKF). The performance of a proposed two-receiver navigation architecture is then studied to quantify the improvements brought by the measurement redundancy. This concept is firstly tested on a simulator in order to validate the proposed algorithm and to give a reference result of our multi-receiver system’s performance. The pseudorange measurements and carrier phase measurements mathematical models are implemented in a realistic simulator. Different scenarios are conducted, including varying the distance between the 2 antennas of the receiver array, the satellite constellation geometry, and the amplitude of the noise measurement, in order to determine the influence of the use of an array of receivers. The simulation results show that our multi-receiver RTK system w.r.t an external reference receiver is more robust to noise and degraded satellite geometry, in terms of ambiguity fixing rate, and gets a better position accuracy under the same conditions when compared with the single receiver system. Additionally, our method achieves a relatively accurate estimation of the attitude of the vehicle which provides additional information beyond the positioning. In order to optimize our processing, the correlation of the measurement errors affecting observations taken by our array of receivers has been determined. Then, the performance of our real-time single frequency cycle-slip detection and repair algorithm has been assessed. These two investigations yielded important information so as to tune our Kalman Filter. The results obtained from the simulation made us eager to use actual data to verify and improve our multi-receiver RTK and attitude system. Tests based on real data collected around Toulouse, France, are used to test the performance of the whole methodology, where different scenarios are conducted, including varying the distance between the 2 antennas of the receiver array as well as the environmental conditions (open sky, suburban, and constrained urban environments). The thesis also tried to take advantage of a dual GNSS constellation, GPS and Galileo, to further strengthen the position solution and the reliable use of carrier phase measurements. The results show that our multi-receiver RTK system is more robust to degraded GNSS environments. Our experiments correlate favorably with our previous simulation results and further support the idea of using an array of receivers with known geometry to improve the RTK performance

On Improving the Accuracy and Reliability of GPS/INS-Based Direct Sensor Georeferencing

Due to the complementary error characteristics of the Global Positioning System (GPS) and Inertial Navigation System (INS), their integration has become a core positioning component, providing high-accuracy direct sensor georeferencing for multi-sensor mobile mapping systems. Despite significant progress over the last decade, there is still a room for improvements of the georeferencing performance using specialized algorithmic approaches. The techniques considered in this dissertation include: (1) improved single-epoch GPS positioning method supporting network mode, as compared to the traditional real-time kinematic techniques using on-the-fly ambiguity resolution in a single-baseline mode; (2) customized random error modeling of inertial sensors; (3) wavelet-based signal denoising, specially for low-accuracy high-noise Micro-Electro-Mechanical Systems (MEMS) inertial sensors; (4) nonlinear filters, namely the Unscented Kalman Filter (UKF) and the Particle Filter (PF), proposed as alternatives to the commonly used traditional Extended Kalman Filter (EKF). The network-based single-epoch positioning technique offers a better way to calibrate the inertial sensor, and then to achieve a fast, reliable and accurate navigation solution. Such an implementation provides a centimeter-level positioning accuracy independently on the baseline length. The advanced sensor error identification using the Allan Variance and Power Spectral Density (PSD) methods, combined with a wavelet-based signal de-noising technique, assures reliable and better description of the error characteristics, customized for each inertial sensor. These, in turn, lead to a more reliable and consistent position and orientation accuracy, even for the low-cost inertial sensors. With the aid of the wavelet de-noising technique and the customized error model, around 30 percent positioning accuracy improvement can be found, as compared to the solution using raw inertial measurements with the default manufacturer’s error models. The alternative filters, UKF and PF, provide more advanced data fusion techniques and allow the tolerance of larger initial alignment errors. They handle the unknown nonlinear dynamics better, in comparison to EKF, resulting in a more reliable and accurate integrated system. For the high-end inertial sensors, they provide only a slightly better performance in terms of the tolerance to the losses of GPS lock and orientation convergence speed, whereas the performance improvements are more pronounced for the low-cost inertial sensors

Optimized Filter Design for Non-Differential GPS/IMU Integrated Navigation

The endeavours in improving the performance of a conventional non-differential GPS/MEMS IMU tightly-coupled navigation system through filter design, involving nonlinear filtering methods, inertial sensors' stochastic error modelling and the carrier phase implementation, are described and introduced in this thesis. The main work is summarised as follows. Firstly, the performance evaluation of a recently developed nonlinear filtering method, the Cubature Kalman filter (CKF), is analysed based on the Taylor expansion. The theoretical analysis indicates that the nonlinear filtering method CKF shows its benefits only when implemented in a nonlinear system. Accordingly, a nonlinear attitude expression with direction cosine matrix (DCM) is introduced to tightly-coupled navigation system in order to describe the misalignment between the true and the estimated navigation frames. The simulation and experiment results show that the CKF performs better than the extended Kalman filter (EKF) in the unobservable, large misalignment and GPS outage cases when attitude errors accumulate quickly, rendering the psi-angle expression invalid and subsequently showing certain nonlinearity. Secondly, the use of shaping filter theory to model the inertial sensors' stochastic errors in a navigation Kalman filter is also introduced. The coefficients of the inertial sensors' noises are determined from the Allan variance plot. The shaping filter transfer function is deduced from the power spectral density (PSD) of the noises for both stationary and non-stationary processes. All the coloured noises are modelled together in the navigation Kalman filter according to equivalence theory. The coasting performance shows that the shaping filter based modelling method has a similar and even smaller maximum position drift than the conventional 1st-order Markovian process modelling method during GPS outages, thus indicating its effectiveness. Thirdly, according to the methods of dealing with carrier phase ambiguities, tightly-coupled navigation systems with time differenced carrier phase (TDCP) and total carrier phase (TCP) as Kalman filter measurements are deduced. The simulation and experiment results show that the TDCP can improve the velocity estimation accuracy and smooth trajectories, but position accuracy can only achieve the single point positioning (SPP) level if the TDCP is augmented with the pseudo-range, while the TCP based method's position accuracy can reach the sub-meter level. In order to further improve the position accuracy of the TDCP based method, a particle filter (PF) with modified TDCP observation is implemented in the TDCP/IMU tightly-coupled navigation system. The modified TDCP is defined as the carrier phase difference between the reference and observation epochs. The absolute position accuracy is determined by the reference position accuracy. If the reference position is taken from DGPS, the absolute position accuracy can reach the sub-meter level. For TCP/IMU tightly-coupled navigation systems, because the implementation of TCP in the navigation Kalman filter introduces additional states to the state vector, a hybrid CKF+EKF filtering method with the CKF estimating nonlinear states and the EKF estimating linear states, is proposed to maintain the CKF's benefits while reducing the computational load. The navigation results indicate the effectiveness of the method. After applying the improvements, the performance of a non-differential GPS/MEMS IMU tightly-coupled navigation system can be greatly improved

Automated Processing of GPS/MEMS-IMU Data for Position, Velocity and Attitude Determination

This master thesis deals with the problematic of the integration of Global Positioning System (GPS) measurements with inertial data acquired by a Micro-Electro-Mechanical System (MEMS) Inertial Measurement Units (IMU). This technology is employed for the purposes of sport performances assessment, as it enables reconstructing accurately athletes trajectories. Based on a recent development at the TOPO laboratory at the Swiss Federal Institute of Technology of Lausanne (EPFL), a new software's architecture is proposed to ensure an automated treatment of the input data. In its first phase, a Continuous Wavelet Transform (CWT) is performed to split the signal as a function of its dynamic. Then, quasi static periods are automatically identified to initialize the processing. Thereafter, several ranges can be integrated in order to compute an optimal trajectory. The performances of this new architecture are validated and evaluated using several sport experiments in skiing and biking. The implemented method is reliable and works correctly. The process offers the capability of bridging lacks of GPS data lasting up to 10 seconds, without any substantial degradation of the trajectory's accuracy. In the frame of this project, a new MEMS-IMU was also engaged, in order to evaluate its navigation performances. It appears that its stochastic model needs to be refined and a specific initialization strategy developed before this sensor finds its place in trajectory reconstruction for downhill skiing

GNSS precise point positioning :the enhancement with GLONASS

PhD ThesisPrecise Point Positioning (PPP) provides GNSS navigation using a stand-alone receiver with no base station. As a technique PPP suffers from long convergence times and quality degradation during periods of poo satellite visibility or geometry. Many applications require reliable realtime centimetre level positioning with worldwide coverage, and a short initialisation time. To achieve these goals, this thesis considers the use of GLONASS in conjunction with GPS in kinematic PPP. This increases the number of satellites visible to the receiver, improving the geometry of the visible satellite constellation. To assess the impact of using GLONASS with PPP, it was necessary to build a real time mode PPP program. pppncl was constructed using a combination of Fortran and Python to be capable of processing GNSS observations with precise satellite ephemeris data in the standardised RINEX and SP3 formats respectively. pppncl was validated in GPS mode using both staticsites and kinematic datasets.In GPS only mode,one sigma accuracy of 6.4mm and 13mm in the horizontal and vertical respectively for 24h static positioning was seen. Kinematic horizontal and vertical accuracies of 21mm and 33mm were demonstrated. pppncl was extended to assess the impact of using GLONASS observations in addi- tion to GPS instatic and kinematic PPP. Using ESA and Veripos Apex G2 satel- lite orbit and clock products,the average time until 10cm 1D static accuracy was achieved, over arange of globally distributed sites, was seen to reduce by up to 47%. Kinematic positioning was tested for different modes of transport using real world datasets. GPS/GLONAS SPPP reduced the convergence time to decimetre accuracy by up to a factor of three. Positioning was seen to be more robust in comparison to GPS only PPP, primarily due to cycle slips not being present on both satellite systems on the occasions when they occurred,and the reduced impact of undetected outliersEngineering and Physical Sciences Research Council, Verip os/Subsea