Attitude Heading Reference System Research Articles

MEMS IMU: Development and flight test results

The paper presents a MEMS IMU, being a component of integrated standby instrument system (ISIS). MEMS IMU determines aircraft roll, pitch and heading, when the main inertial navigation system or attitude heading reference system is a failure. Design features, software algorithms, and calibration procedure are outlined. Results from the unit flight tests onboard TU-204 SM aircraft are presented.

LANDSLIDE MAPPING USING IMAGERY ACQUIRED BY A FIXED-WING UAV

Abstract. In Taiwan, the average annual rainfall is about 2,500 mm, about three times the world average. Hill slopes where are mostly under meta-stable conditions due to fragmented surface materials can easily be disturbed by heavy typhoon rainfall and/or earthquakes, resulting in landslides and debris flows. Thus, an efficient data acquisition and disaster surveying method is critical for decision making. Comparing with satellite and airplane, the unmanned aerial vehicle (UAV) is a portable and dynamic platform for data acquisition. In particularly when a small target area is required. In this study, a fixed-wing UAV that equipped with a consumer grade digital camera, i.e. Canon EOS 450D, a flight control computer, a Garmin GPS receiver and an attitude heading reference system (AHRS) are proposed. The adopted UAV has about two hours flight duration time with a flight control range of 20 km and has a payload of 3 kg, which is suitable for a medium scale mapping and surveying mission. In the paper, a test area with 21.3 km2 in size containing hundreds of landslides induced by Typhoon Morakot is used for landslides mapping. The flight height is around 1,400 meters and the ground sampling distance of the acquired imagery is about 17 cm. The aerial triangulation, ortho-image generation and mosaicking are applied to the acquired images in advance. An automatic landslides detection algorithm is proposed based on the object-based image analysis (OBIA) technique. The color ortho-image and a digital elevation model (DEM) are used. The ortho-images before and after typhoon are utilized to estimate new landslide regions. Experimental results show that the developed algorithm can achieve a producer's accuracy up to 91%, user's accuracy 84%, and a Kappa index of 0.87. It demonstrates the feasibility of the landslide detection algorithm and the applicability of a fixed-wing UAV for landslide mapping.

Adaptive UAV Attitude Estimation Employing Unscented Kalman Filter, FOAM and Low-Cost MEMS Sensors

Navigation employing low cost MicroElectroMechanical Systems (MEMS) sensors in Unmanned Aerial Vehicles (UAVs) is an uprising challenge. One important part of this navigation is the right estimation of the attitude angles. Most of the existent algorithms handle the sensor readings in a fixed way, leading to large errors in different mission stages like take-off aerobatic maneuvers. This paper presents an adaptive method to estimate these angles using off-the-shelf components. This paper introduces an Attitude Heading Reference System (AHRS) based on the Unscented Kalman Filter (UKF) using the Fast Optimal Attitude Matrix (FOAM) algorithm as the observation model. The performance of the method is assessed through simulations. Moreover, field experiments are presented using a real fixed-wing UAV. The proposed low cost solution, implemented in a microcontroller, shows a satisfactory real time performance.

Implementation of INS in Three Dimensional Space using Mems based Ahrs

Inertial Navigation System (INS) is required for implementing point to coordinate and target coordinate calculation. INS is used to update the position of Gimbal Payload Assembly (GPA) in three dimensional space.INS will provide accurate attitude, position and velocity of the system.INS shall be of light weight and shall be in small size to accommodate with in the gimbal assembly. Micro Electro Mechanical System (MEMS) based Attitude Heading Reference System (AHRS) and related processing electronics in used to meet weight and volume requirement. AHRS consists of MEMS based triaxial accelerometer, triaxial gyro, triaxial magnetometer and temperature sensor. AHRS will provide the basic information i.e acceleration & angle rates (or) delta angle & delta velocity vectors of the system. An algorthim to be developed to get the attitude and orientation of GPA in North East Down(NED) frame using Texas Instrument (TI) makes TMS320F2812 DSP Processor and AHRS. In this paper distance travelled by the system can be calculated by using MEMS based AHRS.

Design and implementation of SMO for a nonlinear MIMO AHRS

In a low-cost attitude heading reference system (AHRS), the measurements made by MEMS inertial and magnetic sensors are affected by large parameter uncertainties, stochastic noises and unknown disturbances. In this paper, considering the robustness of the sliding mode observers (SMO) against both structured and unstructured uncertainties as well as exogenous inputs, the process of design and implementation of a nonlinear SMO is proposed for a low-cost AHRS. For simultaneous estimation of orientation variables and calibration biases of gyroscopes, a nonlinear and non-affine model of the AHRS is considered. Therefore, based on the Lie-algebraic method, the estimation algorithm is designed for a general class of non-affine nonlinear MIMO systems. In the proposed observer, owing to decreasing the required assumptions for coordinate transformation in recent literatures, the design process of the SMO is simplified. The gain matrices of the proposed SMO are obtained through ensuring the stability and the convergence of estimation errors based on Lyapunov's direct method. The expected tracking performance of the robust state and parameter estimation algorithm compared to that of the extended Kalman filter (EKF) is evaluated through simulations and real experiments of a strapped AHRS on a ground vehicle.

적응형 퍼지-칼만 필터를 이용한 자세추정 성능향상

본 논문은 다중 센서 융합의 성능을 높이기 위해 적응형 퍼지-칼만 필터를 적용하고 교차검증법(cross-validation)으로 퍼지시스템 입 출력 소속 함수의 매개변수를 조정하는 방법을 제안한다. 적응형 퍼지-칼만 필터는 가속도의 변화량과 칼만 필터의 잔여오차를 입력으로 시스템잡음, 측정잡음을 추정하여 칼만 이득을 변화시킨다. 적용된 퍼지-칼만 필터는 잡음들을 가우시안 분포로 가정한 이전 방법과 비교하여 비선형/비가우시안 잡음에 강인한 추정 결과를 보여준다. 본 논문에서 제안한 퍼지-칼만 필터를 평가하기 위해 가속도센서/자이로센서를 융합하여 2축 자세추정시스템(Attitude Heading Reference System)을 설계하였고 무인항공기에 사용되는 자세추정센서 NAV420CA-100과 비교하여 성능을 검증하였다. This paper describes the parameter adjustment method of fuzzy membership function to improve the performance of multi-sensor fusion system using adaptive fuzzy-Kalman filter and cross-validation. The adaptive fuzzy-Kanlman filter has two input parameters, variation of accelerometer measurements and residual error of Kalman filter. The filter estimates system noise R and measurement noise Q, then changes the Kalman gain. To evaluate proposed adaptive fuzzy-Kalman filter, we make the two-axis AHRS(Attitude Heading Reference System) using fusion of an accelerometer and a gyro sensor. Then we verified its performance by comparing to NAV420CA-100 to be used in various fields of airborne, marine and land applications.

Study on AHRS Sensor for Unmanned Underwater Vehicle

In this paper, for the accurate estimation of the position and orientation of the UUV (unmanned underwater vehicle), an AHRS (Attitude Heading Reference System) was developed using the IMU (inertial measurement unit) sensor which provides information on acceleration and orientation in the object coordinate and the initial alignment algorithm and the E-KF (extended Kalman Filter). The initial position and orientation of the UUV are estimated using the initial alignment algorithm with 3-axis acceleration and geomagnetic information of the IMU sensor. The position and orientation of the UUV are estimated using the AHRS composed of 3-axis acceleration, velocity, and geomagnetic information and the E-KF. For the performance test of the orientation estimation of the AHRS, a testbed using IMU sensor(ADIS16405) and DSP28335 coded with an E-KF algorithm was developed and its performance was verified through tests.

An enhanced fuzzy H∞ estimator applied to low‐cost attitude‐heading reference system

PurposeThe purpose of this paper is to consider the process of design and implementation of an enhanced fuzzy H∞ (EFH∞) estimation algorithm to determine the attitude and heading angles of ground vehicles, which are frequently affected by considerable exogenous disturbances. To detect the changes of disturbances, a fuzzy system is designed based on expert knowledge and experiences of a navigation engineer. In the EFH∞ estimator, the intensity bounds of disturbances affecting the measurements are updated using a heuristic combination of three change‐detection indices. Performance of the proposed estimator is evaluated by Monte‐Carlo simulations and field tests of three kinds of vehicles using a manufactured attitude‐heading reference system (AHRS). In both simulations and real tests, the proposed estimator results in a superior performance compared to those of the recently developed and standard H∞ estimators.Design/methodology/approachDesign, implementation and real tests of the EFH∞ estimator are considered for an AHRS specialized for vehicular applications. In the AHRS, three‐axis accelerometers (TAA) and three‐axis magnetometers (TAM) may be affected by large disturbances due to non‐gravitational accelerations and local magnetic fields. Therefore, the design parameters of EFH∞ estimator including the theoretic bound of disturbance intensity and the attenuation level are adaptively tuned using a fuzzy combination of three change‐detection indices. Once a sensor is affected by an exogenous disturbance, the fuzzy system will increase the scale factor of the corresponding measurement disturbance to place more confidence on the data of the AHRS dynamics including measurements of gyros with respect to the data coming from the TAA and TAM.FindingsAn intelligent fault detector is proposed for considering changes of disturbances to adjust the upper bounds of the estimator's disturbances and the length of data to update the fuzzy system inputs. The EFH∞ estimator is suitable to attenuate the effects of disturbances changes on accurate estimation of the attitude and heading angles, intelligently.Originality/valueThe paper provides a fuzzy state estimator for adaptively adjusting the theoretic disturbance matrices according to the actual intensity of the disturbances affecting the AHRS dynamics and the measurement sensors.

基于低成本多传感器的自适应组合滤波

An integrated Micro Attitude Heading Reference System(MAHRS) based on Micro-electro-mechanical System(MEMS) gyroscopes,accelerometers,and magnetic-sensors was researched for Micro Aerial Vehicles(MAV).As traditional algorithms could not keep the attitude accuracy of the MAV during a long-time maneuvering,an adaptive attitude estimation filtering algorithm with low-cost multi-sensors was presented for the MAHRS.Firstly,this algorithm was used to establish the models of acceleration and magnetic-disturbance and took them into a state equation.In this way,it could maintain the attitude accuracy of loads during the long-time maneuvering.Then,this algorithm adopted the federated filter mode to reduce the interaction between acceleration and magnetic disturbance to improve its precision and reliability.Moreover,the algorithm made the estimation variance(P) and measurement variance(R) remain steady adaptively during different maneuvering states.By comparing with other different algorithms for the MAHRS,It shows that the presented algorithm is more accurate and reliable than other algorithms during various maneuvering states.

Indoor Hovering Control of Small Ducted-fan Type OAV Using Ultrasonic Positioning System

This study focused on a development of attitude stabilization and a position (hovering) control scheme of the SFR (Small Flying Robot). The platform type of the SFR is a ducted-fan type OAV having a diameter of 30 cm. The attitude of the SFR was successfully stabilized by a PD control method. In the attitude control loop, the feedback states were angle and angular rates, which were outputs from an AHRS (Attitude Heading Reference System). The position of the SFR was controlled precisely by the LQI (Linear Quadratic Integration) method with a Kalman filter. In the position control loop, an ultrasonic positioning system was used for the 3D position reference, which was developed in a previous research. The proposed simple mathematical model could analyze dynamics and stability of the SFR. Also, the stabilities of the attitude control and the precision of the position control were verified by comparing and analyzing data of experiments and simulations.

Exploiting Attitude Sensing in Vision-Based Navigation for an Airship

An Attitude Heading Reference System (AHRS) is used to compensate for rotational motion, facilitating vision-based navigation above smooth terrain by generating virtual images to simulate pure translation movement. The AHRS combines inertial and earth field magnetic sensors to provide absolute orientation measurements, and our recently developed calibration routine determines the rotation between the frames of reference of the AHRS and the monocular camera. In this way, the rotation is compensated, and the remaining translational motion is recovered by directly finding a rigid transformation to register corresponding scene coordinates. With a horizontal ground plane, the pure translation model performs more accurately than image-only approaches, and this is evidenced by recovering the trajectory of our airship UAV and comparing with GPS data. Visual odometry is also fused with the GPS, and ground plane maps are generated from the estimated vehicle poses and used to evaluate the results. Finally, loop closure is detected by looking for a previous image of the same area, and an open source SLAM package based in 3D graph optimization is employed to correct the visual odometry drift. The accuracy of the height estimation is also evaluated against ground truth in a controlled environment.

Development of a Hardware-In-Loop (HIL) Simulator for Spacecraft Attitude Control Using Momentum Wheels

In this paper, a Hardware-In-the-Loop simulator to simulate attitude control of space craft using momentum wheels is developed. The simulator consists of a spherical air bearing system allowing rotation and tilt in all three axes, three momentum wheels for actuation, and an AHRS (Attitude Heading Reference System). The simulator processes various types of data in PC104 and wirelessly communicates with a host PC using TCP/IP protocol. A simple low-cost momentum wheel assembly set and its drive electronics are also developed. Several experiments are performed to test the performance of the momentum wheels. For the control performance test of the simulator, a PID controller is implemented. The results of experimental demonstrations confirm the feasibility and validity of the Hardware-In-the-Loop simulator developed in the current study.

Design and Real Time Implementation of a Fuzzy Tuned H∞ Estimator in a Low Cost AHRS

In this paper a Fuzzy Tuned extended H∞ (FTH∞) estimator is designed and implemented in an Attitude Heading Reference System (AHRS), which is specialized to vehicular applications. In AHRS, 3-axis accelerometers are allotted to measure the earth's gravity field vector and then to update the roll and pitch angles obtained from gyros’ dynamic. Therefore, the AHRS on an accelerated vehicle will be affected by large disturbances. Additionally, in ground vehicles, the measurements of 3-axis magnetometers are corrupted by both soft and hard iron time varying disturbances. The FTH∞ estimator is an extended H∞*** filter in which only noise and attenuation bounds are tuned based on fuzzy linguistic if-then rules. The mentioned features make the estimator more reliable and suitable for hardware implementation. FTH∞ estimator relies on a consistent fuzzy combination of two change detection tests; namely, likelihood ratio and averaged norm error. Real-time implementation of new attitude-heading estimator is performed on a TMS320VC5416 Digital Signal Processor (DSP). Incorporating this powerful and small size DSP with micro electro mechanical inertial and resistive magnetic sensors leads to a low cost, small size and low power consumption AHRS. Performance of the AHRS was evaluated in Monte Carlo simulations of vehicle's attitude-headings. The FTH∞ estimator results in a superior performance compared to that of the extended H∞ estimator in simulation and real tests.

A Low-cost Attitude Heading Reference System by Combination of GPS and Magnetometers and MEMS Inertial Sensors for Mobile Applications

This paper describes a prototype system for attitude and heading determination. A L1-only GPS receiver is integrated with microelectromechanical gyroscopes, accelerometers and magnetometers. In contrast to a multi-antenna/multi-receiver GPS attitude determination system, this system uses a single antenna/single receiver configuration to derive standalone velocity and acceleration solutions from the GPS L1 carrier phase measurements. No reference station is needed to form differences of carrier phase measurements for the velocity and acceleration calculation. The GPSderived acceleration is further used in the attitude determination by combination with the three-dimension acceleration sensed by the accelerometers. The magnetometers sense the Earth’s magnetic field intensity, and can give the heading estimation regardless of the status of the host platform. To satisfy real-time applications, infinite impulse response differentiators instead of finite impulse response differentiators are used to derive the acceleration from GPS. The algorithms have been implemented and their efficiency demonstrated by experiments.

Automotive-grade MEMS sensors used for general aviation

The attitude heading reference system (AHRS) provides data for primary flight instruments, head-up displays, autopilots, and moving map navigation systems. Advances in solid-state MEMS rate sensors, coupled with Kalman filter algorithms designed to mitigate high drift rates, provide the basis for low-cost, high-performance AHRS for general aviation. This paper describes the performance of a low cost, miniaturized AHRS using automotive-grade MEMS sensors. The performance of the system is detailed. The implications for certification of this class of system and fault tolerance are discussed.

Software algorithms in air data attitude heading reference systems

An introduction to air data attitude heading reference systems (ADAHRS) is presented along with the developments and discussion of software algorithms used in such systems. Both Kalman filtering and fuzzy logic adaptive signal processing are presented and discussed. Quaternions are used in this paper as the basis for the discussion of both algorithms. Flight results from Archangel Systems AHR150 ADAHRS are also presented and discussed.

Quaternion-Based Kalman Filter for Micro-machined Strapdown Attitude Heading Reference System

AbstractA Kalman filter used in strapdown AHRS (Attitude Heading Reference System) based on micro-machined inertial sensors is introduced. The composition and principle of the system are described. The attitude algorithm and error model of the system are derived based on the quaternion formulation. The real-time quaternion-based Kalman filter is designed. Simulation results show that accuracy of the system is better than 0.04 degree without disturbance of lateral acceleration and reduced to 0.44 degree with lateral acceleration of 0.1 g/1Hz. Primary tests show that the static accuracy of the system is + / − 0.2 degree for pitch and roll, and + / − 0.3 degree for azimuth.