Jizhou Lai

Noncommutativity Error Analysis of Strapdown Inertial Navigation System under the Vibration in UAVs

Noncommutativity error of a strapdown inertial navigation system (SINS) in an unmanned aerial vehicles (UAV) vibration environment is analysed. The traditional analysis of noncommutativity errors is based on a coning motion model, which is inconsistent with a UAVs vibration environment. In this paper the UAVs vibration form is discussed and is modelled as a sinusoidal angular vibration and a random angular vibration. Then, SINS motion models under these two forms of vibration are built up and the formulas for the noncommutativity errors are derived separately. In addition, the effect of a multi-sample algorithm is explored, which is an effective method for compensating for noncommutativity errors in cases of coning motion. Finally, the UAVs vibration environment is simulated and it is indicated that the simulation results of the SINSs noncommutativity errors are consistent with theoretical analysis.

Performance Analysis of Adaptive Neuro Fuzzy Inference System Control for MEMS Navigation System

Characterized by small volume, low cost, and low power, MEMS inertial sensors are widely concerned and applied in navigation research, environmental monitoring, military, and so on. Notably in indoor and pedestrian navigation, its easily portable feature seems particularly indispensable and important. However, MEMS inertial sensor has inborn low precision and is impressionable and sometimes goes against accurate navigation or even becomes seriously unstable when working for a period of time and the initial alignment and calibration are invalid. A thought of adaptive neuro fuzzy inference system (ANFIS) is relied on, and an assistive control modulated method is presented in this paper, which is newly designed to improve the inertial sensor performance by black box control and inference. The repeatability and long-time tendency of the MEMS sensors are tested and analyzed by ALLAN method. The parameters of ANFIS models are trained using reasonable fuzzy control strategy, with high-precision navigation system for reference as well as MEMS sensor property. The MEMS error nonlinearity is measured and modulated through the peculiarity of the fuzzy control convergence, to enhance the MEMS function and the whole MEMS system property. Performance of the proposed model has been experimentally verified using low-cost MEMS inertial sensors, and the MEMS output error is well compensated. The test results indicate that ANFIS system trained by high-precision navigation system can efficiently provide corrections to MEMS output and meet the requirement on navigation performance.

Analysis of coning motion caused by turntable's vibration in rotation inertial navigation system

Turntable is an important part of rotation inertial navigation system (RINS). Due to machining accuracy, there exists certain angle error between vertical axis of the turntable and table rotation axis, which leads to tables vibration and further the loss of RINSs accuracy. Firstly, the error of turntables vibration is modeled. And the analysis results show that inertial measurement unit (IMU) does coning error because of turntables vibration. Then, the conning error caused by coning motion is analyzed, and the effect of multi-sample compensation algorithm based on the equivalent rotation vector is discussed. At the same time, the effect of rotation manner to RINSs coning error is discussed, and the result shows that back and forth rotation has a good compensation effect to the error. Finally, the above theoretical analyses are verified through simulation.

Autonomous cyanobacterial harmful algal blooms monitoring using multirotor UAS

ABSTRACT In this article, we present a framework to use an autonomous multirotor unmanned aerial system (UAS) for cyanobacterial harmful algal blooms (CHABs) monitoring. The requirements of CHABS monitoring, and the factors are analysed. Formulae are developed to describe the relation between the image qualities and the UAS performance. Experiments are designed to investigate the influences of the flight parameters (the flight altitude, the flight speed and the shutter time of the camera) on the image resolution. CHABs detection experiments were carried out at Kingston, Canada. Experimental results show that the autonomous UAS is a promising enabling technology to contribute to a novel CHABs detection method.

Stochastic error simulation method of fiber optic gyros based on performance indicators

Abstract Gyro simulation is an important process of inertial navigation theory research, with the major difficulty being the stochastic error modeling. One commonly used stochastic model for a fiber optic gyro (FOG) is a Gaussian white (GW) noise plus a first order Markov process. The model parameters are usually obtained by using time series analysis methods or the Allan variance method through FOG static experiment. However, in a real life situation, a FOG may not be used. In this paper, a simulation method is proposed for estimating the stochastic errors of FOG. When using this method, the model parameters are set based on performance indicators, which are chosen as the angle random walk (ARW) and bias stability. During the research, the ARW and bias stability indicators of the GW noise and the first order Markov process are analyzed separately. Their analytical expressions are derived to reveal the relation between the model parameters and performance indicators. In order to verify the theory, a large number of simulations were carried out. The results show that the statistical performance indicators of the simulated signals are consistent with the theory. Furthermore, a simulation of a VG951 FOG is designed in this research. The Allan variance curve of the simulated signal is in agreement with the real one.

Decrease in Accuracy of a Rotational SINS Caused by its Rotary Table's Errors

We call a strapdown inertial navigation system (SINS) that uses the rotation auto-compensation technique (which is a common method to reduce the effect of the bias errors of inertial components) a ‘rotational SINS’. In a rotational SINS, the rotary table is an important component, rotating the inertial sensor assembly back and forth in azimuth to accomplish error modulation. As a consequence of the manufacturing process, errors may exist in rotary tables which decrease the navigation accuracy of rotational SINSs. In this study, the errors of rotary tables are considered in terms of installation error, wobble error and angular error, and the models of these errors are established for the rotational SINS. Next, the propagation characteristics of these errors in the rotational SINS are analysed and their effects on navigation results are discussed. Finally, the theoretical conclusions are tested by numerical simulation. This paper supplies a good reference for the development of low-cost rotational SINSs, which usually have low accuracy rotary tables and which may be used in robots, intelligent vehicles and unmanned aerial vehicles (UAVs).

Homologous fault monitoring technology of redundant INS in airborne avionics systems

Redundant technology plays an important role in improving the reliability and fault-tolerance of the airborne avionics systems.A Markov state transition model is introduced to the reliability analysis of the redundant inertial navigation system (RINS) in airborne navigation systems.An information processing mechanism based on difference filtering is put forward to strengthen the consistency between the outputs of the equal-precision inertia navigation system (INS).On this basis,the homologous fault monitoring algorithm is designed to realize the homologous fault monitoring of RINS.The simulation is carried out based on the above algorithms,and the results verify the effectiveness of the proposed fault monitoring algorithm based on difference filtering.Research results have good reference value for the configuration and design of RINS in airborne integrated avionics systems.

A dynamic model parameter identification method for quadrotors using flight data

The dynamic model parameter identification is important for unmanned aerial vehicle modeling and control. The unmanned aerial vehicle model parameters are usually identified through wind tunnel experiments, which are complex. In this paper, a model parameter identification method is proposed using the flight data for quadrotors. The parameters of the thrust, drag force, torque, rolling moment and pitching moment are estimated through Kalman filter. Global positioning system and inertial sensors are used as measurements. The observabilities of the model parameters and their degrees of observability are analyzed. Flight experiments are carried out to verify the proposed method. It is shown that the model parameters estimated by the proposed method have good accuracies, demonstrating the validity of the proposed method.

A Thrust Model Aided Fault Diagnosis Method for the Altitude Estimation of a Quadrotor

In this paper, a new fault diagnosis method is presented for the sensors in the vertical direction of a quadrotor. Different from the existing methods that treat the inertial sensors and the measurement sensors separately, the presented method is capable of dealing with both the z-axis accelerometer and the barometer. The knowledge of the thrust model is used to generate an analytical redundancy based fault diagnosis approach for altitude estimation. The filter design, fault detection, isolation, and recovery problems are addressed. An improved chi-test method is used for fault detection. Real-flight data is used to validate the proposed approaches, showing that the faults of the z-axis accelerometer and the barometer can both be detected and the thrust model of a quadrotor can be used to replace the faulty z-axis accelerometer.

An improved MCS/INS integrated navigation algorithm for multi-rotor UAV in indoor flight

Motion capture system is a kind of high-precision attitude and position measurement system. Precise navigation information is provided for multi-rotor UAV indoor autonomous flight. However, it is time-consuming in the stage of signal measurement, information decoding and data transmission in motion capture system, which makes the multi-rotor UAV get delay navigation information. In this paper, according to the problem that the system data delay is time-varying, a delay time calculation method is proposed. Fuse the onboard inertial sensor information with the delayed measurement data to compensate the delay error through kalman filter. The experiments show that the algorithm we proposed can effectively estimate the data latency of the motion capture system, and compensate the error online. The multi-rotor UAV flight performance is improved.