Sensor registration refers to the estimation/compensation of systematic (bias) errors, in contrast to the random errors from sensor noise. Various methods have been proposed for bias estimation of multiple optical sensors using common targets of opportunity. However, the proposed solutions required the use of multiple (two or more) optical sensors and the need to solve the data association problem arising from the fusion of measurements from multiple sensors. In order to remove these constraints, we provide in this paper a new methodology using a single exoatmospheric target of opportunity seen in a single satellite borne sensor's field of view to estimate the sensor's biases simultaneously with the state of the target. The satellite based sensor sees the target from a changing direction as a function of its position, allowing the target in this nonlinear tracking system to be observable. The sensor provides the Line Of Sight (LOS) measurements of azimuth and elevation to the target. Sensor pointing calibration is the key precondition of accurate tracking of a target in a space based system. Statistical tests on the results of simulations, and the evaluation of the Cramér-Rao Lower Bound (CRLB) on the covariance of the bias estimates show that this method is statistically efficient.

Achieving successful collaboration among a swarm of Unmanned Aerial Vehicles (UAVs) requires an accurate relative localization system. In scenarios where GPS is intermittently lost, UAVs require an alternative navigation system to conduct autonomous missions during GPS outages. Without an alternative, UAVs must rely solely on dead reckoning navigation based on the Inertial Measurement Unit (IMU), which can lead to the accumulation of errors. This paper presents a range only aided navigation system, where an Extended Kalman Filter (EKF) combines IMU data with range data from sensors that measure the distance between nodes in the swarm. This method mitigates the growth of position, navigation, and timing (PNT) errors during periods of GPS signal loss. To demonstrate the effectiveness of this approach, a combination of software simulations and hardware experiments were conducted. These involved a swarm of six drones in various scenarios, including circular, square, and blue-angels flight trajectories. The trajectories were designed to minimize significant geometric dilution of precision (GDOP). Statistical analysis of the simulation results indicates that this approach improves the estimates compared with dead reckoning alone, thereby reducing the growth of velocity and location errors when GPS is intermittently unavailable.

As bias estimation methods are developed, it becomes necessary to obtain the bound on bias estimation for more complex bias and sensor models. 3-dimensional (3-D) sensors, such as radars commonly used in applications, contain both scale and additive biases in sine space which result in a nonlinear estimation problem that may have poor observability and accuracy depending on the geometry of the sensors. By converting the sine space and range measurements to Cartesian using an unbiased conversion, it is possible, via creation of pseudomeasurements, to eliminate the need to estimate the target's state thereby reducing the sensor bias estimator complexity. The present paper evaluates the Cramer-Rao Lower Bound (CRLB) for estimating scale and additive biases in sine space for 3-D sensors and compares it with a Maximum Likelihood (ML) formulation implemented via Iterated Least Squares (ILS), which is thereby shown to be statistically efficient. Additionally, the importance of measurement diversity is investigated with respect to the CRLB.

Accurate estimation of accelerometer biases in Inertial Measurement Units (IMUs) is crucial for reliable Unmanned Aerial Vehicle (UAV) navigation, particularly in GPS-denied environments. Uncompensated biases lead to an unbounded accumulation of position error and increased velocity error, resulting in significant navigation inaccuracies. This paper examines the effects of accelerometer bias on UAV navigation accuracy and introduces a vision-aided navigation system. The proposed system integrates data from an IMU, altimeter, and optical flow sensor (OFS), employing an Extended Kalman Filter (EKF) to estimate both the accelerometer biases and the UAV position and velocity. This approach reduces the accumulation of velocity and positional errors. The efficiency of this approach was validated through simulation experiments involving a UAV navigating in circular and straight-line trajectories. Simulation results show that the proposed approach significantly enhances UAV navigation performance, providing more accurate estimates of both the state and accelerometer biases while reducing error growth through the use of vision aiding from an Optical Flow Sensor.