Design for graceful degradation and recovery from GNSS interruptions

In guidance, navigation, and control (GN&C) of small unmanned aerial vehicles (UAVs), estimates of the vehicle's kinematic states are generated by an integrated navigation system. In current applications, the system of choice is an inertial navigation system (INS) aided by measurements from a global navigation satellite system (GNSS) receiver. One of the shortcomings of these integrated GNSS/INSs is the problem of temporary or prolonged GNSS outages. These outages can occur because of temporary signal loss due to obstructions, a prolonged outage due to interference or jamming, or deliberate action by the GN&C system to isolate a failed receiver or reject an anomalous signal in space. In these instances, the position, velocity, and attitude solutions generated by processing the inertial measurement unit (IMU) outputs alone in INSs quickly drift. To mitigate this drift, alternate aiding signals such as cameras, radars , light detection and ranging (LIDAR), or other signals of opportunity have been used. When the only kinematic state of interest is attitude (e.g., UAV stabilization), IMUs aided by magnetometers and airspeed sensors have been used to mechanize attitude heading reference systems (AHRSs). A system architecture that uses an AHRS and airspeed measurements to mechanize a dead-reckoning (DR) navigator aided by the relative range measurement between cooperating vehicles is discussed in the works of Mohtarzadeh (2014) and Gebre-Egziabher et. al (2004). The purpose of this article is to present a decentralized filtering approach to design an integrated navigation system for small UAVs that seamlessly switches between GNSS-available and GNSS-denied modes of operation. When GNSS services are denied, it gracefully degrades to a less optimal operational mode while maintaining sufficient accuracy to allow continued guidance and control of a small UAV. When GNSS services are restored, it smoothly transitions back to a high-accuracy operational mode. The decentralized filter presented in this article is based on the idea of fusing the output from a set of parallel filters. Each parallel filter can be a stand-alone system that provides an estimate of all or a subset of the vehicle states needed for UAV GN&C. As shown and discussed, this approach can easily be implemented on most existing UAV FCSs. This is because it does not require additional sensors beyond those already found on most FCSs on the market today. The filter addresses the second and third challenges noted in the preceding paragraph, namely, the ability to unwind the effect of a failed sensor and provide smooth transitions to and from GNSS-denied operations. Finally, the performance of the proposed filtering approach is validated using data from flight tests of a small UAV.