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 , .