  Sensor-fault detection is crucial for the safe operation of autonomous
vehicles. This paper introduces a novel kinematics-based approach for detecting
and identifying faulty sensors, which is model-independent, rule-free, and
applicable to ground and aerial vehicles. This method, called kinematics-based
sensor fault detection (KSFD), relies on kinematic relations, sensor
measurements, and real-time single and double numerical differentiation. Using
onboard data from radar, rate gyros, magnetometers, and accelerometers, KSFD
uniquely identifies a single faulty sensor in real time. To achieve this,
adaptive input and state estimation (AISE) is used for real-time single and
double numerical differentiation of the sensor data, and the single and double
transport theorems are used to evaluate the consistency of data. Unlike
model-based and knowledge-based methods, KSFD relies solely on sensor signals,
kinematic relations, and AISE for real-time numerical differentiation. For
ground vehicles, KSFD requires six kinematics-based error metrics, whereas, for
aerial vehicles, nine error metrics are used. Simulated and experimental
examples are provided to evaluate the effectiveness of KSFD.
