Analysis of UAV state estimation modeling based on AHRS and INS configurations

This paper reports the results of an in-water laboratory experimental evaluation of the attitude estimation accuracy of a low-cost micro-electro-mechanical systems (MEMS) attitude and heading reference system (AHRS), and the effect of the accuracy of this sensor on Doppler-based underwater navigation. We report a comparative analysis of Doppler navigation obtained employing MEMS AHRS in comparison to Doppler navigation obtained with a high-accuracy inertial navigation system (INS) including a true-North-seeking gyrocompass and high precision accelerometers. The data indicate that Doppler navigation performance with MEMS AHRS is sensitive to instrument calibration including Doppler/AHRS alignment calibration, calibration of AHRS magnetometers for hard-iron & soft-iron errors, and calibration of AHRS angular rate sensors. When carefully calibrated, MEMS AHRS Doppler navigation error is shown to be within an order-of-magnitude of that obtained with high-end INS for the conditions and vehicle trajectories studied. The goal of this evaluation is to quantify Doppler navigation performance using MEMS AHRS. These results may be useful in the development of lower-cost Doppler navigation systems for small and low-cost underwater vehicles.

Nine degrees of freedom (DOF) inertial measurement units (IMUs) comprised of three-axis magnetometers, three-axis accelerometers, and three-axis angular rate sensors are commonly used in attitude and heading reference systems (AHRSs). Two classes of AHRSs exist: systems that estimate true-North heading and systems that estimate magnetic-North heading. True-North heading AHRSs require high-end angular rate sensors which are sensitive enough to dynamically estimate Earth-rate (typically fiber-optic and ring-laser gyros), while magnetic-North AHRSs employ gyros that are not sensitive enough to dynamically estimate Earth-rate (i.e. all MEMS gyros). Thus, magnetic-North AHRSs employ magnetometers for estimating heading. This paper will focus on this class of magnetic-North AHRSs. These systems fuse IMU measurements to generate estimates of the instrument's roll, pitch, and magnetic heading. However, their accuracy is limited by sensor measurement bias that is unknown a priori. Hence, accurate sensor bias estimation and compensation is essential for true attitude estimation. This paper reports a novel adaptive sensor bias observer for sensor measurement biases in 9-DOF IMUs. The algorithm requires smaller angular movements of the instrument than other reported sensor bias calibration methods, does not require a priori knowledge of local fields like the local magnetic field or the local gravity vector, and does not require knowledge of the attitude of the instrument. Stability proofs, preliminary simulations, and a full-scale vehicle experimental evaluation are reported.

Autonomous unmanned aerial vehicle (UAV) system is depending on state estimation feedback to control flight operation. Estimation on the correct state improves navigation accuracy and achieves flight mission safely. One of the sensors configuration used in UAV state is Attitude Heading and Reference System (AHRS) with application of Extended Kalman Filter (EKF) or feedback controller. The results of these two different techniques in estimating UAV states in AHRS configuration are displayed through position and attitude graphs.

The paper is intended to discuss errors in measurements of angular velocity and linear acceleration by means of electronic inertial sensors incorporated into the Attitude and Heading Reference Systems (AHRS) as well as Inertial Navigation Systems (INS). The mathematical equations are found out to establish how these errors affect deviations of flight parameters (such as linear speed and coordinates of the aircraft position indicated by navigation systems) that are calculated from the measurements and imaged e.g. in helmetmounted cueing systems. Some issues related to diagnostics of inertial navigation systems are addressed as well, both the most recent ones (e.g. the TOTEM-3000 central station with laser sensors installed on-board of the W-3PL GŁUSZEC helicopter) integrated via digital data buses MIL-1553B or ARINC-429 as well as more outdated solutions, such as IKW-1 and IKW-8 system used for Su-22 aircrafts. The methods of examination of the data processing paths for signals received from inertial sensors are presented with guidelines for development of the computer system for assessment of technical condition exhibited by systems of inertial navigation with prolonged technical resource.

Navigation Scheme for the LSTS SEACON Vehicles: Theory and Application

A kind of marine strapdown attitude and heading reference system (AHRS) based on the principle of strapdown inertial navigation system (INS) is discussed here. With an electromagnetic (EM) log aided, the oscillations included in the attitude and heading errors are bounded by damping network. Furthermore, in order to decrease attitude and heading errors aroused by EM log measurements, we introduce an adaptive network-based fuzzy inference system to control the damping ratio automatically in terms of the vessel maneuvers conditions. The results of test demonstrate the validity of proposed method.

PurposeThis paper aims to develop an adaptive unscented Kalman filter (AUKF) formulation for orientation estimation of aircraft and UAV utilizing low‐cost attitude and heading reference systems (AHRS).Design/methodology/approachA recursive least‐square algorithm with exponential age weighting in time is utilized for estimation of the unknown inputs. The proposed AUKF tunes its measurement covariance to yield optimal performance. Owing to nonlinear nature of the dynamic model as well as the measurement equations, an unscented Kalman filter (UKF) is chosen against the extended Kalman filter, due to its better performance characteristics. The unscented transformation of the UKF is shown to equivalently capture the effect of nonlinearities up to second order without the need for explicit calculations of the Jacobians.FindingsIn most conventional AHRS filters, severe problems can occur once the system suddenly experiences additional acceleration, resulting in erroneous orientation angles. On the contrary in the high dynamic accelerative mode of the new proposed filter the errors would not suddenly increase, since the additional to cruise accelerations are being continuously estimated resulting in substantially more accurate orientation estimation. This feature causes the associated filter errors to gradually increase, in the event of continuous vehicle acceleration, up to a point of zero additional acceleration that subsequently causes a subsidence of the error back to zero.Practical implicationsThe proposed filtering methodology can be implemented for orientation estimation of aircraft and UAV that are equipped with low‐cost AHRSs.Originality/valueTraditional AHRS algorithms utilize the accelerometers output for the computation of roll and pitch angles and magnetometer output for the heading angle. Moreover, these angles are also calculated from the gyroscopes output as well, but with errors that increase with time. Of course for some applications of AHRS system, orientation errors can be damped out with a proportional‐integral controller. In such a case, the filter cut‐off frequency is usually selected experimentally. But, for high accelerating vehicles utilizing AHRS, the system errors can become very large. A possible remedy to this problem could be to use more advanced nonlinear filter algorithms such as the one proposed.

Fundamental to vision enhancement systems for vehicles are sensors that provide precise, real-time attitude and heading information. While attitude and heading reference systems (AHRS) based on high-performance inertial components exist, such systems are traditionally very expensive and inapplicable to price-sensitive applications/markets. This paper demonstrates an AHRS that combines small, low-cost, solid- state inertial sensors with an advanced GPS-derived attitude determination technology. The resulting AHRS is inexpensive, rugged, and provides information with the accuracy and responsiveness needed to drive many enhanced/synthetic-vision displays, such as tunnel-in-the-sky guidance displays. The instrument also provides GPS position, velocity, and time information. Further, by using complementary and overlapping sources of information for integrity checking, the GPS/Inertial AHRS has robustness to individual sensor failures and can operate safely if GPS were to become unavailable.

An Attitude and Heading Reference System (AHRS) comprising accelerometers, gyroscopes and magnetometers can provide roll, pitch and heading information. AHRS is utilized in many applications such as navigation, augmented/virtual reality, and mobile mapping. The AHRS mechanization involves integration of angular rate measurement to provide high rate orientation but with unbounded drifts due to accumulation of random noise. To reduce drifts, mechanization output is combined with absolute measurement from magnetometer and accelerometer using Extended Kalman Filter(EKF). EKF accuracy is greatly affected by process covariance matrix (Q) and measurement noise covariance matrix(R). Conventional stochastic modeling approaches to determine Q and R parameters do not guarantee best performance. This paper proposes a systematic procedure for EKF parameters optimization using a hybrid statistical and genetic algorithms (GA) approach. The proposed approach has been verified on real data collected by an inertial measurement unit. Results showed that the Q and R can be optimized within few GA iterations outperforming conventional EKF parameter estimation methods.

An Attitude and Heading Reference System (AHRS) provides orientation information of an object such as an Unmanned Air Vehicle by fusing sensor data from a magnetic and inertial measurement unit (MIMU). A MIMU consists of three components, which are a gyroscope, an accelerometer and a magnetometer. There are different fusion or filter algorithms available of which we have chosen the Mahony, the Madgwick, and the weighted filter. One of the shortcomings of all algorithms is that there is a control parameter involved in each which needs to be determined before applying the algorithms. Only an algorithm with an optimized control parameter achieves the best orientation information. Thus, a Particle Swarm Optimization algorithm is used to identify the control parameter of each filter algorithm. Experimental results show the influence the control parameter has on each algorithm.

This study presents a wireless attitude heading and reference system (AHRS) adapted for use in marine environments where it was used to characterize the motion of a pneumatic rotary feed spreader representative of designs commonly utilized in large-scale salmon aquaculture. The attitude of the spreader affects the distribution of feed pellets over the cage surface and may have significant influence on the feeding efficiency of fish. Measurement and analysis of the motion were performed over a range of different airspeeds. The results suggested that an angular momentum originating from the connection between the spreader and the feed supply pipe may cause a substantial roll offset. At low airspeeds the yaw angle of the outlet pipe appeared to be most influential on the roll and pitch of the spreader. At higher airspeeds the rotational velocity became increasingly influential. The roll and pitch of the spreader may explain the non-uniform surface pellet distribution observed in a separate study.

This paper presents a design scheme, detailed implementation and evaluation of attitude and heading reference system (AHRS) which consists of low cost MEMS sensors, magnetometers and related data fusion algorithms, considerations for the selection of attitude estimation were presented, the simulation and experimental test were performed to assess the structure of algorithms, computational burden and convergence from the engineering view of point.

Attitude and heading reference system (AHRS) is the term used to describe a rigid body’s angular orientation in three-dimensional space. This paper describes an AHRS determination and control system developed for navigation systems by integrating gyroscopes, accelerometers, and magnetometers signals from low-cost MEMS-based sensors in a complementary adaptive Kalman filter. AHRS estimation based on the iterative Kalman filtering process is required to be initialized first. A new method for AHRS initialization is proposed to improve the accuracy of the initial attitude estimates. Attitude estimates derived from the initialization and iterative adaptive filtering processes are compared with the orientation obtained from a high-end reference system. The improvement in the accuracy of the initial orientation as significant as 45% is obtained from the proposed method as compared with other selected techniques. Additionally, the computational process is reduced by 96%.

The airborne attitude and heading reference system (AHRS) is an inertial system that provides the aircraft with navigation parameters in real time. A new coarse transfer alignment method is proposed for low-accuracy AHRS with a large misalignment angle between AHRS and master inertial navigation system (MINS). The new coarse alignment method is derived from the quaternion-optimization-based in-flight alignment (IFA) method. By resolving the alignment problem in body frame, the proposed method makes full use of the navigation information of MINS and transforms the alignment problem into a quaternion optimization problem. Its accuracy is improved by presenting a two-step iterative algorithm. Compared with the quaternion-optimization-based IFA method, the proposed method is more robust and accurate by introducing the attitude information of MINS. The simulation and experiment results show that the proposed method is applicable to both large and small misalignment angles, and it serves as a nice coarse alignment that permits the implementation of linear error model in fine alignment process.

In this paper, we propose a novel Attitude and Heading Reference System (AHRS) based analysis method for sport wheelchair propulsion. Wireless inertial measurement units were attached to each wheel and an AHRS calculation was used to obtain the rotation angle of the sensors. The sensor rotation angle from the AHRS contains both the wheel rotation angle and the wheelchair turn angle in the horizontal plane. A coordinate system conversion from global coordinates to viewing coordinates was required to extract the wheel rotation angle. Both the distance travelled and the velocity were calculated using the angle. Four different trials were performed with two inertial sensors on each wheel. Straight-line distance tests gave high agreement among four sensors (28.164±0.028m). The difference between two sensors on the same wheel did not increase even for trials over 10 minutes, which implies a reduction in the effect of gyroscope drift. The velocity curves demonstrate agreement for acceleration and deceleration accompanied by push and traveling resistance.