Integrity is a popular and effective index as a measure of trust for navigation system to place in the correct position. The classical snapshot-based integrity monitoring methods have a widely and mature application in global navigation satellite system (GNSS) assessment. However, they cannot meet the integrity evaluation requirements for multi-sensor integration such as all-source navigation due to its recursive estimation and measurement diversity of sensors, which directly limits it’s use in safety-critical applications. We propose a new Kalman filter based solution separation (KFSS) method for the integrity monitoring of multi-sensor integrated navigation systems. The traditional EKF update estimation is remodeled as a weighted least square form to involve the system propagation into the new measurement vector, which reconstructed as a ‘pseudo-snapshot’ model. The integrity risk caused by the system propagation is considered as one fault hypothesis in the following fault detection and protection level determination. Then, the integrity evaluation is executed in positioning domain enhanced by solution separation with sensor exclusion. The above two operations have indispensable roles and inseparable relationship from the aspect of integrity functional realization. The performance of a tightly coupled integration simulation, a loosely coupled multi-sensor integration simulation and an actual kinematic vehicle experiment verified the feasibility and superiority of the proposed method. The KFSS structure can detect fault in propagation period and step fault, ramp fault and simultaneous faults in observations effectively. The protection levels can be reduced positively both in horizontal and vertical directions, which is positive to bound the position error more accurately and reduce the redundant space effectively. It is of great significance for tighter integrity requirements.

To improve the robustness and reliability of multi-sensor navigation and reduce the uncertainties and complexity of sensor management in challenging environment, a resilient interactive sensor-independent-update (ISIU) method is proposed. Inspired by the interactive cooperation theory, the contributions can be divided into two aspects. Firstly the priority of trust of navigation sensors is introduced into the information fusion in the form of transition probability matrix defined by Markov chain. Secondly every observable sensor is integrated with the propagated system in an elemental filter with sensor-independent-update structure. The multi-sensor integration is implemented in state estimation domain enhanced by interactive information fusion rather than in measurement domain implemented in traditional filter method. The overall estimation is determined by the weighted sum of average from every filter estimate. This weight of every model is dynamic updated by the prior transition information and posterior model likelihood. The same independent structure is also applied to adopt new available sensor to realize plug-and-play navigation. The kinematic vehicle experiment in sub-urban and urban canyon environment verified the superiority of the proposed method. The ISIU method shows better accuracy and reliability compared to classical Kalman filters. The introduction of priority of sensors and decoupled measurement update process make it robust and insensitive to sensor measurement noise and outliers. The interactive sensor-independent-update structure has the natural function of fault detection and exclusion without additional operations. The effect of dynamic sensor selection is achieved in this processing. The proposed ISIU method is pretty suitable for resilient navigation in challenging environments.

Spoofing can seriously threaten the use of the Global Positioning System (GPS) in critical applications such as positioning and navigation of autonomous vehicles. Research into spoofing generation will contribute to assessment of the threat of possible spoofing attacks and help in the development of anti-spoofing methods. However, the recent commercial off-the-shelf (COTS) spoofing generators are expensive and the technology implementation is complicated. To address the above problem and promote the GPS safety-critical applications, a spoofing generator using a vector tracking-based software-defined receiver is proposed in this contribution. The spoofing generator aims to modify the raw signals by cancelling the actual signal component and adding the spoofing signal component. The connections between the spreading code and carrier, and the states of the victim receiver are established through vector tracking. The actual signal can be predicted effectively, and the spoofing signal will be generated with the spoofing trajectory at the same time. The experimental test results show that the spoofing attack signal can effectively mislead the victim receiver to the designed trajectory. Neither the tracking channels nor the positioning observations have abnormal changes during this processing period. The recent anti-spoofing methods cannot detect this internal spoofing easily. The proposed spoofing generator can cover all open-sky satellites with a high quality of concealment. With the superiority of programmability and diversity, it is believed that the proposed method based on an open source software-defined receiver has a great value for anti-spoofing research of different GNSS signals.

Pedestrian navigation using smartphone built-in sensors attracted wide attention with the booming Location-based Service (LBS). Pedestrian Dead Reckoning (PDR) and Global Navigation Satellite Navigation (GNSS) integration is recognized as a reliable solution for smartphone-based pedestrian navigation. However, GNSS is vulnerable under some conditions. Multi-path, None-Line-Of-Sight (NLOS), and signal blockage all pose negative impacts on GNSS position accuracy. PDR position errors increase with the pedestrian walking distance without GNSS. Aiming at improving the smartphone-based pedestrian position accuracy under GNSS signal challenging conditions, in this paper, we propose a cooperative PDR/GNSS integration method with Factor Graph Optimization (FGO). A factor graph is constructed to represent the relationship between the multiple agents’ states, measurements and inter-ranging information. Optimal estimation is realized considering all the historical measurements and inter-ranging measurements between these agents. Field tests were carried out to assess the performance of the proposed cooperative navigation method. Results manifest that the proposed method can improve the position accuracy especially under the GNSS signals challenging conditions.