Reliability control of GNSS carrier-phase integer ambiguity resolution

This research investigates how to obtain accurate and reliable positioning results with global navigation satellite systems (GNSS). The work provides a theoretical framework for reliability control in GNSS carrier phase ambiguity resolution, which is the key technique for precise GNSS positioning in centimetre levels. The proposed approach includes identification and exclusion procedures of unreliable solutions and hypothesis tests, allowing the reliability of solutions to be controlled in the aspects of mathematical models, integer estimation and ambiguity acceptance tests. Extensive experimental results with both simulation and observed data sets effectively demonstrate the reliability performance characteristics based on the proposed theoretical framework and procedures.

Study of robust H(infinity) filtering application in loosely coupled INS/GPS system

Since a celebrate linear minimum mean square (MMS) Kalman filter in integration GPS/INS system cannot guarantee the robustness performance, a H(infinity) filtering with respect to polytopic uncertainty is designed. The purpose of this paper is to give an illustration of this application and a contrast with traditional Kalman filter. A game theory H(infinity) filter is first reviewed; next we utilize linear matrix inequalities (LMI) approach to design the robust H(infinity) filter. For the special INS/GPS model, unstable model case is considered. We give an explanation for Kalman filter divergence under uncertain dynamic system and simultaneously investigate the relationship between H(infinity) filter and Kalman filter. A loosely coupled INS/GPS simulation system is given here to verify this application. Result shows that the robust H(infinity) filter has a better performance when system suffers uncertainty; also it is more robust compared to the conventional Kalman filter.

Uncertainty based online planning for UAV target finding in cluttered and GPS-denied environments

There are some scenarios in which Unmmaned Aerial Vehicle (UAV) navigation becomes a challenge due to the occlusion of GPS systems signal, the presence of obstacles and constraints in the space in which a UAV operates. An additional challenge is presented when a target whose location is unknown must be found within a confined space. In this paper we present a UAV navigation and target finding mission, modelled as a Partially Observable Markov Decision Process (POMDP) using a state-of-the-art online solver in a real scenario using a low cost commercial multi rotor UAV and a modular system architecture running under the Robotic Operative System (ROS). Using POMDP has several advantages to conventional approaches as they take into account uncertainties in sensor information. We present a framework for testing the mission with simulation tests and real flight tests in which we model the system dynamics and motion and perception uncertainties. The system uses a quad-copter aircraft with an board downwards looking camera without the need of GPS systems while avoiding obstacles within a confined area. Results indicate that the system has 100% success rate in simulation and 80% rate during flight test for finding targets located at different locations.

A survey and analysis of the GNSS spoofing threat and countermeasures

Detection and prevention of global network satellite system (GNSS) “spoofing” attacks, or the broadcast of false global navigation satellite system services, has recently attracted much research interest. This survey aims to fill three gaps in the literature: first, to assess in detail the exact nature of threat scenarios posed by spoofing against the most commonly cited targets; second, to investigate the many practical impediments, often underplayed, to carrying out GNSS spoofing attacks in the field; and third, to survey and assess the effectiveness of a wide range of proposed defences against GNSS spoofing. Our conclusion lists promising areas of future research.

RTK-based GNSS drifters for monitoring in estuaries and rivers: Field experimental results

This paper presents a novel RTK-based GNSS Lagrangian drifter system that is capable of monitoring water velocity, turbulence and dispersion coefficients of river and estuarine. The Lagrangian drifters use the dual-frequency real time kinematic (RTK) technique for both position and velocity estimations. The capsule is designed to meet the requirements such as minimizing height, diameter, minimizing the direct wind drag, positive buoyancy for satellite signal reception and stability, and waterproof housing for electronic components, such as GNSS receiver and computing board. The collected GNSS data are processed with post-processing RTK software. Several experiments have been carried out in two rivers in Brisbane and Sunshine Coast in Queensland. Results show that the high accuracy GNSS-drifters can be used to measure dispersion coefficient resulting from sub-tidal velocity fluctuations in shallow tidal water. In addition, the RTK-GNSS drifters respond well to vertical motion and thus could be applicable to flood monitoring.

Geometry-free stochastic analysis of BDS triple frequency signals

The paper presents a geometry-free approach to assess the variation of covariance matrices of undifferenced triple frequency GNSS measurements and its impact on positioning solutions. Four independent geometryfree/ ionosphere-free (GFIF) models formed from original triple-frequency code and phase signals allow for effective computation of variance-covariance matrices using real data. Variance Component Estimation (VCE) algorithms are implemented to obtain the covariance matrices for three pseudorange and three carrier-phase signals epoch-by-epoch. Covariance results from the triple frequency Beidou System (BDS) and GPS data sets demonstrate that the estimated standard deviation varies in consistence with the amplitude of actual GFIF error time series. The single point positioning (SPP) results from BDS ionosphere-free measurements at four MGEX stations demonstrate an improvement of up to about 50% in Up direction relative to the results based on a mean square statistics. Additionally, a more extensive SPP analysis at 95 global MGEX stations based on GPS ionosphere-free measurements shows an average improvement of about 10% relative to the traditional results. This finding provides a preliminary confirmation that adequate consideration of the variation of covariance leads to the improvement of GNSS state solutions.