Ionospheric disturbances affect Global Positioning System (GPS) performance in terms of accuracy and integrity, especially over the equatorial region. During the period of the disturbances, GPS receivers suffer from a high noise level. Not taken into account by the current stochastic model, the ionospheric disturbances degrade GPS positioning accuracy. In addition, non-Gaussian tails are observed in the distribution of the noise during the period of the disturbances; therefore the integrity of GPS can also be affected. This paper develops a statistical solution that is able to mitigate effects of ionospheric disturbances on GPS accuracy and integrity using a commercial dual frequency receiver. The Rate of Total Electron Content (TEC) change Index (ROTI), a parameter derived from the dual frequency receiver, is used to group the levels of ionospheric disturbances. The standard deviations of the pseudorange noise under different groups are evaluated. By incorporating both the ROTI and the satellite elevation, a modified stochastic model is proposed to reduce the effect of the disturbed observation on the positioning accuracy. The performance of the model is evaluated by a test and an inflated sigma for each group is recommended for over-bounding anomalies of observations to protect the user against threats from ionospheric disturbances. This technique, together with results in this paper, can be applied to mitigate the effects of ionospheric disturbances on GPS.

The benefits of Global Navigation Satellite Systems (GNSS) and in particular GPS have been demonstrated worldwide, and continue to accrue in the form of new applications. Today, some mission critical applications (largely driven by safety) mostly in aviation and to a lesser extent maritime, rely on a number of techniques for the provision of integrity monitoring services. However, for many applications such as those associated with the emergency services, road user charging and personal navigation devices, no such monitoring exists and service validation is limited due to technical and cost constraints. The problem is exacerbated by a recent increase in more challenging problems, particularly in the security industry, in which jamming devices have been used to disrupt the use of GPS. This paper presents a new approach for a cost effective local solution to the problems above, at the individual user level, by providing the user with information on Quality of Service (QoS). The approach is based on the deployment of a single or network of probes supported by an existing regional network. The probes communicate with a central server and are designed to detect localised events whilst the regional network isolates space segment anomalies. The latter is addressed in detail in this paper in terms of design methodology and performance, using the Ordnance Survey's UK GNSS network.

International standards require the use of a weighted least-squares approach to onboard Receiver Autonomous Integrity Monitoring (RAIM). However, the protection levels developed to determine if the conditions exist to perform a measurement check (i.e. failure detection) are not specified. Various methods for the computation of protection levels exist. However, they are essentially approximations to the complex problem of computing the worst-case missed detection probability under a weighted system. In this paper, a novel approach to determine this probability at the worst-case measurement bias is presented. The missed detection probabilities are then iteratively solved against the integrity risk requirement in order to derive an optimal protection level for the operation. It is shown that the new method improves availability by more than 30% compared to the baseline weighted RAIM algorithm.

A version of this paper was first presented at the US Institute of Navigation (ION) GNSS 2009 Conference in Savannah, Georgia.

Carrier phase measurements are used to provide high-accuracy estimates of position. For safety-of-life navigation applications such as precision approach and landing, integrity plays a critical role. Carrier phase-based Receiver Autonomous Integrity Monitoring (CRAIM) has been investigated for many years (Pervan et al, 1998; Feng et al, 2007). Assuming that the carrier phase error has a Gaussian distribution, conventional CRAIM algorithms were directly derived from the Pseudorange-based RAIM (PRAIM). However, the actual carrier phase error does not exactly follow the Gaussian distribution, hence the performance of the conventional CRAIM algorithm is not optimal.

To approach this problem, this paper proposes a new CRAIM algorithm that uses Gaussian sum filters. A Gaussian sum filter can deal with any non-Gaussian error distribution and accurately present the posterior distributions of states. In this paper, a new method of making a Gaussian mixture model, which follows the true error distribution, is introduced. Additionally an integrity monitoring algorithm, using a Gaussian sum filter, is described in detail. The simulation results show that the proposed algorithm can have about 18% smaller Minimum Detectable Bias (MDB) and generates about 20% lower protection levels than those of the conventional CRAIM algorithm. In other words, by considering a non-Gaussian carrier phase error distribution, the new algorithm can improve the accuracy and the availability.

With conventional snapshot RAIM algorithms, it is difficult to detect small errors and simultaneous multiple faults. Assuming that we know the system dynamics, filtering algorithms, such as the Kalman filter, can provide better integrity-monitoring performance than the snapshot algorithms because the filter reduces the noise level of measurements. However, because the Kalman filter presumes that measurement noise and disturbance follow the Gaussian distribution, its performance might degrade if the assumption is wrong. To address this problem, we propose a fault detection and exclusion algorithm using Gaussian sum filters. Because GNSS measurement noise does not follow the Gaussian distribution perfectly, the Gaussian sum filter can estimate the posterior distribution more accurately; therefore it has better integrity-monitoring performance. This paper describes the detailed algorithms and shows simulation results to evaluate the integrity-monitoring performance of the algorithms. The proposed algorithms detect about 30% smaller faults and generate 35% lower protection levels than the conventional methods. The results show that the proposed algorithms can provide better accuracy and availability performance.

Because GPS is a radio navigation system which has a very low power level, it is vulnerable to RFI. Excessive RFI could cause receiver performance degradation, such as degradation of position accuracy, loss of lock and increased acquisition time. After GPS modernization plans introduce dual-frequency civil signals to mitigate ionospheric errors, RFI will remain as one of the dominant threats for differential GPS navigation systems. Examples of safety-critical civil aviation and military missions threatened by RFI include the Local Area Augmentation System (LAAS) and the Joint Precision Approach and Landing System (JPALS). This paper focuses on RFI mitigation through integrity monitoring for a DGPS system like LAAS or JPALS. The mitigation strategy consists of two parts. First, the paper develops a new RFI detection method, using a raw divergence statistic. Second, the paper investigates strategies for maintaining integrity in the case that RFI is detected.

To validate the utility of the divergence-based RFI monitor, this paper takes an experimental approach. The experiments assess the performance of the divergence metric and compare it to existing alternatives for RFI detection, such as metrics for Automatic Gain Control (AGC) and carrier-to-noise ratio (C/N0). Generating a monitoring threshold for these statistics proves challenging, because the threshold depends both on the type of RFI threat (e.g. continuous wave, narrow band, wideband, pulsed) and on environmental conditions, such as temperature. As experiments illustrate, the divergence statistic resolves these limitations, as divergence directly estimates ranging source error, independent of the type of RFI threat or the environmental conditions.

Receiver Autonomous Integrity Monitoring (RAIM) is a method implemented within the receiver to protect users against satellite navigation system failures. For a receiver to execute a RAIM calculation, two conditions must be met: a minimum number of satellites and adequate satellite geometry. The non-existence of the minimum number of satellites (five) is referred to as a RAIM hole. Current regional and global RAIM availability studies use spatial (grid-based) and temporal sampling intervals driven by a trade-off between accuracy and computation workload. The implication of minimising computational load is that accuracy is compromised and potential RAIM holes remain un-sampled, with potential risk to safety. This paper proposes a direct and computationally efficient method (as opposed to the grid-based search approach) to predict RAIM holes. The method is based on the precise computation of satellite coverage (footprint) boundaries, the intersection points and analysis of the topology of the regions of intersection. Test results show that the proposed method is highly accurate and requires minimal computational load compared to the current approach.