Nonlinear Attitude Filtering: A Comparison Study

This paper contains a concise comparison of a number of nonlinear attitude filtering methods that have attracted attention in the robotics and aviation literature. With the help of previously published surveys and comparison studies, the vast literature on the subject is narrowed down to a small pool of competitive attitude filters. Amongst these filters is a second-order optimal minimum-energy filter recently proposed by the authors. Easily comparable discretized unit quaternion implementations of the selected filters are provided. We conduct a simulation study and compare the transient behaviour and asymptotic convergence of these filters in two scenarios with different initialization and measurement errors inspired by applications in unmanned aerial robotics and space flight. The second-order optimal minimum-energy filter is shown to have the best performance of all filters, including the industry standard multiplicative extended Kalman filter (MEKF)

Attitude Estimation and Control Using Linear-Like Complementary Filters: Theory and Experiment

This paper proposes new algorithms for attitude estimation and control based on fused inertial vector measurements using linear complementary filters principle. First, n-order direct and passive complementary filters combined with TRIAD algorithm are proposed to give attitude estimation solutions. These solutions which are efficient with respect to noise include the gyro bias estimation. Thereafter, the same principle of data fusion is used to address the problem of attitude tracking based on inertial vector measurements. Thus, instead of using noisy raw measurements in the control law a new solution of control that includes a linear-like complementary filter to deal with the noise is proposed. The stability analysis of the tracking error dynamics based on LaSalle's invariance theorem proved that almost all trajectories converge asymptotically to the desired equilibrium. Experimental results, obtained with DIY Quad equipped with the APM2.6 auto-pilot, show the effectiveness and the performance of the proposed solutions.Comment: Submitted for Journal publication on March 09, 2015. Partial results related to this work have been presented in IEEE-ROBIO-201

A Unifying Approach to Quaternion Adaptive Filtering: Addressing the Gradient and Convergence

A novel framework for a unifying treatment of quaternion valued adaptive filtering algorithms is introduced. This is achieved based on a rigorous account of quaternion differentiability, the proposed I-gradient, and the use of augmented quaternion statistics to account for real world data with noncircular probability distributions. We first provide an elegant solution for the calculation of the gradient of real functions of quaternion variables (typical cost function), an issue that has so far prevented systematic development of quaternion adaptive filters. This makes it possible to unify the class of existing and proposed quaternion least mean square (QLMS) algorithms, and to illuminate their structural similarity. Next, in order to cater for both circular and noncircular data, the class of widely linear QLMS (WL-QLMS) algorithms is introduced and the subsequent convergence analysis unifies the treatment of strictly linear and widely linear filters, for both proper and improper sources. It is also shown that the proposed class of HR gradients allows us to resolve the uncertainty owing to the noncommutativity of quaternion products, while the involution gradient (I-gradient) provides generic extensions of the corresponding real- and complex-valued adaptive algorithms, at a reduced computational cost. Simulations in both the strictly linear and widely linear setting support the approach

Performance Evaluation of Neural Networks for Animal Behaviors Classification: Horse Gaits Case Study

The study and monitoring of wildlife has always been a subject of great interest. Studying the behavior of wildlife animals is a very complex task due to the difficulties to track them and classify their behaviors through the collected sensory information. Novel technology allows designing low cost systems that facilitate these tasks. There are currently some commercial solutions to this problem; however, it is not possible to obtain a highly accurate classification due to the lack of gathered information. In this work, we propose an animal behavior recognition, classification and monitoring system based on a smart collar device provided with inertial sensors and a feed-forward neural network or Multi-Layer Perceptron (MLP) to classify the possible animal behavior based on the collected sensory information. Experimental results over horse gaits case study show that the recognition system achieves an accuracy of up to 95.6%.Junta de Andalucía P12-TIC-130

Rigid Body Attitude Estimation: An Overview and Comparative Study

The attitude estimation of rigid body systems has attracted the attention of many researchers over the years. The development of efficient estimation algorithms that can accurately estimate the orientation of a rigid body is a crucial step towards a reliable implementation of control schemes for underwater and flying vehicles. The primary focus of this thesis consists in investigating various attitude estimation techniques and their applications. Two major classes are discussed. The first class consists of the earliest static attitude determination techniques relying solely on a set of body vector measurements of known vectors in the inertial frame. The second class consists of dynamic attitude estimation and filtering techniques, relying on body vector measurements as well other measurements, and using the dynamical equations of the system under consideration. Various attitude estimation algorithms, including the latest nonlinear attitude observers, are presented and discussed, providing a survey that covers the evolution and structural differences of these estimation methods. Simulation results have been carried out for a selected number of such attitude estimators. Their performance in the presence of noisy measurements, as well as their advantages and disadvantages are discussed

Multiplicative Error State Kalman Filter vs Nonlinear Complimentary Filter for a High Performance Aircraft Attitude Estimation

Modern control law designs increasingly use aircraft attitude information to improve aircraft manoeuverability. Attitude information allows for gravity term compensations in the longitudinal as well as lateral directional control laws of a typical fighter aircraft. Methodologies and comparisons of multiplicative error state Kalman filter (MEKF) and nonlinear complimentary filter for estimation of attitudes of a high performance aircraft using its onboard autonomous sensors is presented. Shows a problem in pitch angle estimation beyond ± 80 deg in the MEKF and a solution is proposed for the same for the first time. Also presents novel aiding sensor modelling for the implementation of attitude heading reference system for this class of aircraft for the first time. The filter formulations are evaluated using full range manuoevering real flight test data