To ensure navigation integrity for safety-critical applications, this paper proposes an efficient Fault Detection and Exclusion (FDE) scheme for tightly coupled navigation system of Global Navigation Satellite Systems (GNSS) and Inertial Navigation System (INS). Special emphasis is placed on the potential faults in the Kalman Filter state prediction step (defined as “filter fault”), which could be caused by the undetected faults occurring previously or the Inertial Measurement Unit (IMU) failures. The integration model is derived first to capture the features and impacts of GNSS faults and filter fault. To accommodate various fault conditions, two independent detectors, which are respectively designated for GNSS fault and filter fault, are rigorously established based on hypothesis-test methods. Following a detection event, the newly-designed exclusion function enables (a) identifying and removing the faulty measurements and (b) eliminating the effect of filter fault through filter recovery. Moreover, we also attempt to avoid wrong exclusion events by analyzing the underlying causes and optimizing the decision strategy for GNSS fault exclusion accordingly. The FDE scheme is validated through multiple simulations, where high efficiency and effectiveness have been achieved in various fault scenarios.

Weak-signal and high-dynamics are of two primary concerns of space navigation using GNSS (Global Navigation Satellite System) in the space service volume (SSV). The paper firstly defines a reference assumption third-order phase-locked loop (PLL) as the baseline of an onboard GNSS receiver, and proves the incompetence of this conventional architecture. Then an adaptive four-state Kalman filter (KF)-based algorithm is introduced to realize the optimization of loop noise bandwidth, which can adaptively regulate its filter gain according to the received signal power and line-of-sight (LOS) dynamics. To overcome the matter of losing lock in weak-signal and high-dynamic environments, an open loop tracking strategy aided by an inertial navigation system (INS) is recommended, and the traditional maximum likelihood estimation (MLE) method is modified in a non-coherent way by reconstructing the likelihood cost function. Furthermore, a typical mission with combined orbital maneuvering and non-maneuvering arcs is taken as a destination object to test the two proposed strategies. Finally, the experiment based on computer simulation identifies the effectiveness of an adaptive four-state KF-based strategy under non-maneuvering conditions and the virtue of INS-assisted methods under maneuvering conditions.

A coherent scheme of federated architecture for the ultra‐tight integration of global navigation satellite system (GNSS) and MEMS (micro‐electro‐mechanical system) inertial measurement unit (IMU) is presented. Inside the receiver baseband, both the controls for code numerically controlled oscillator (NCO) and carrier NCO are designed. They are each composed of two parts, one part is derived from the navigation solutions of outer loop, and the other part is given on the basis of the channel pre‐filter outputs from inner loop. Each tacking channel has two filters of cascaded structure, one is the traditional pre‐filter which is used to achieve GNSS signal tracking errors, and the second one is an extractor which is constructed to extract navigation solution derivation errors from the designed NCO controls and the signal tracking errors. For the ultra‐tight integration navigation filter, the receiver clock residual error model after compensating the receiver clock drift is derived in detail. Finally, semi‐physical simulation environment of the proposed ultra‐tight integration scheme is built and closed‐loop experiment under high dynamics scenario is put forward. The simulation results show that the proposed integration scheme can stably and robustly track the GNSS signals, and can achieve accurate navigation solutions even in harsh cases.