Integration of the global positioning system (GPS) with other sensors to improve positioning and navigation is a viable solution for intelligent vehicles. In particular, integrating GPS with an inertial measurement unit (IMU) and vision sensor is a common consideration for enhancing the performance of vehicle navigation systems. However, the accuracy and availability of GPS are seriously degraded by Non-line-of-sight (NLOS) reception and multipath interference in urban environments. Combining the unreliable GPS positioning results with other sensors will degrade the performance of the integrated navigation system. Therefore, we propose a novel measurement quality control algorithm for a loosely coupled GPS, IMU, and stereo visual odometer (VO) integration for urban vehicle navigation. In the proposed algorithm, an adaptive fusion strategy based on geometric dilution of precision (GDOP) and a measurement fault detection and exclusion (FDE) based on the K-means clustering considering predicted pseudorange errors are employed to mitigate NLOS/multipath effects. The validation of the proposed integrated solution incorporating the measurement quality control is carried out. The results show that the algorithm achieves positioning accuracy in terms of 3D root mean square error (RMSE) of 2.85 m and 7.17 m, in mid and urban canyons, respectively. This level of performance is superior to that of the traditional GPS/IMU/VO integration. The corresponding improvements are 49.0% in the mid urban canyon, and 79.6% in the deep urban canyon respectively.

The use of Unmanned Aerial Vehicles (UAVs) has increased significantly in recent years. On-board integrated navigation sensors are a key component of UAVs’ flight control systems and are essential for flight safety. In order to ensure flight safety, timely and effective navigation sensor fault detection capability is required. In this paper, a novel data-driven Adaptive Neuron Fuzzy Inference System (ANFIS)-based approach is presented for the detection of on-board navigation sensor faults in UAVs. Contrary to the classic UAV sensor fault detection algorithms, based on predefined or modelled faults, the proposed algorithm combines an online data training mechanism with the ANFIS-based decision system. The main advantages of this algorithm are that it allows real-time model-free residual analysis from Kalman Filter (KF) estimates and the ANFIS to build a reliable fault detection system. In addition, it allows fast and accurate detection of faults, which makes it suitable for real-time applications. Experimental results have demonstrated the effectiveness of the proposed fault detection method in terms of accuracy and misdetection rate.

GPS is the most widely used global navigation satellite system. By design, there is no provision for real time integrity information within the Standard Positioning Service (SPS). However, in safety critical sectors like aviation, stringent integrity performance requirements must be met. This can be achieved externally or at the receiver level through receiver autonomous integrity monitoring (RAIM). The latter is a cost effective method that relies on data consistency, and therefore requires redundant measurements. An external aid to provide this redundancy can be in the form of an Inertial Navigation System (INS). This should enable continued performance even during RAIM holes (when no redundant satellite measurements are available). However, due to the inclusion of an additional system and the coupling mechanism, integrity issues become more challenging. To develop an effective integrity monitoring capability, a good understanding of the potential failure modes of the integrated system is vital. In this paper potential failure modes of integrated GPS/INS systems are identified. This is followed by the specification of corresponding models that would be required to investigate the capability of existing integrity algorithms and to develop enhancements or new algorithms.

Global Navigation Satellite Systems (GNSS) such as GPS and digital road maps can be used for land vehicle navigation systems. However, GPS requires a level of augmentation with other navigation sensors and systems such as Dead Reckoning (DR) devices, in order to achieve the required navigation performance (RNP) in some areas such as urban canyons, streets with dense tree cover, and tunnels. One of the common solutions is to integrate GPS with DR by employing a Kalman Filter (Zhao et al., 2003). The integrated navigation systems usually rely on various types of sensors. Even with very good sensor calibration and sensor fusion technologies, inaccuracies in the positioning sensors are often inevitable. There are also errors associated with spatial road network data. This paper develops an improved probabilistic Map Matching (MM) algorithm to reconcile inaccurate locational data with inaccurate digital road network data. The basic characteristics of the algorithm take into account the error sources associated with the positioning sensors, the historical trajectory of the vehicle, topological information on the road network (e.g., connectivity and orientation of links), and the heading and speed information of the vehicle. This then enables a precise identification of the correct link on which the vehicle is travelling. An optimal estimation technique to determine the vehicle position on the link has also been developed and is described. Positioning data was obtained from a comprehensive field test carried out in Central London. The algorithm was tested on a complex urban road network with a high resolution digital road map. The performance of the algorithm was found to be very good for different traffic maneuvers and a significant improvement over using just an integrated GPS/DR solution.