Tri-axial linear accelerometers are key components in a great variety of applications and, in particular, in navigation systems. Nonidealities such as scale factors, cross coupling, bias, and other higher-order nonlinearities affect the output of this sensor, leading, in general, to prohibitive errors. On the other hand, these coefficients are often slowly time-varying, which renders offline calibration less effective. One such coefficient that usually varies greatly over time and between power-ons is the bias. This paper details the calibration of an accelerometer unit and presents also a dynamic filtering solution for the bias, which also includes the estimation of the gravity in body-fixed coordinates. Simulation and experimental results obtained with a motion rate table are presented and discussed to illustrate the performance of the proposed algorithms.

This technical note presents the design, analysis, and performance evaluation of a novel globally asymptotically stable (GAS) filter for attitude estimation. The design is sensor-driven and departs from traditional solutions as no explicit representations of the attitude are considered. The proposed solution yields unique estimates and it does not suffer from drawbacks such as singularities, topological limitations for achieving global stabilization, or unwinding phenomena. The performance of the overall attitude estimation solution is evaluated with the design and implementation of an Attitude and Heading Reference System (AHRS) based on a single low-cost Inertial Measurement Unit. The performance of the proposed AHRS is assessed experimentally using a high precision motion rate table, which provides ground truth signals for comparison with the resulting estimates.

This paper develops a navigation system based on complementary filtering for position and attitude estimation, with application to autonomous surface crafts. Using strapdown inertial measurements, vector observations, and global positioning system (GPS) aiding, the proposed complementary filters provide attitude estimates in Euler angles representation and position estimates in Earth frame coordinates, while compensating for rate gyro bias. Stability and performance properties of the proposed filters under operating conditions usually found in oceanic applications are derived, and the tuning of the filter parameters in the frequency domain is emphasized. The small computational requirements of the proposed navigation system make it suitable for implementation on low-power hardware and using low-cost sensors, providing a simple yet effective multirate architecture suitable to be used in applications with autonomous vehicles. Experimental results obtained in real time with an implementation of the proposed algorithm running on-board the DELFIMx catamaran, an autonomous surface craft developed at ISR/IST for automatic marine data acquisition, are presented and discussed.