Jianye Liu

Augmentation of XNAV System to an Ultraviolet Sensor-Based Satellite Navigation System

X-ray pulsar-based navigation (XNAV) using one X-ray detector is investigated as an augmentation to the capability of an ultraviolet (UV) sensor-based satellite autonomous navigation system. The satellite state dynamics are analyzed to establish the dynamical equations of the satellite autonomous navigation system. A time transformation equation that accounts for relativistic effects is presented and the measurement model of the XNAV system is derived using pulse phase information from only one pulsar. The measurement model of the UV sensor-based satellite navigation system is presented using the Earth image information from the UV sensor. In order to integrate the measurements from the X-ray sensor and the UV sensor, a federated filter is developed to provide the optimal simultaneous estimation of position and velocity of the satellite. The concept is demonstrated on a GPS orbit and a geosynchronous orbit and it is found that the performance of the integrated satellite navigation system is improved with respect to that of the UV sensor-based satellite navigation system.

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.

Path planning for indoor UAV based on Ant Colony Optimization

UAV autonomous navigation is very useful in many applications, and path planning is one of the key technologies for UAV autonomous navigation. In this paper, the path planning problem to find the optimal path from the start location to the destination in an indoor environment is studied based on Ant Colony Optimization (ACO) algorithm. The workspace of UAV is modeled by applying the grid method, which is usually used in ground robot planning. With the help of 3D gird and new climbing weight parameter, a modified algorithm is presented to solve the premature convergence and low efficiency problem of traditional Ant Colony Optimization algorithm. The simulation results show that these improvements make the search of the optimal path rapidly and efficiently.

Performance analysis of a federated ultra-tight global positioning system/inertial navigation system integration algorithm in high dynamic environments

The Doppler frequency changes rapidly due to high dynamics of vehicle, which leads to the loose lock and even the abnormal performance of global positioning system (GPS) receiver. To solve this problem, a federated ultra-tight integration algorithm based on pre-filters is proposed to optimal estimate both receiver tracking control commands and inertial navigation system (INS) navigation solutions. Firstly, the INS error model and GPS receiver tracking loop structure are built to present the fundamental architecture of the proposed ultra-tightly coupled system. Meanwhile, in order to reduce the load of the integrated filter, the pre-filters are incorporated to the ultra-tightly coupled system, and the state variables are fed into the integrated Kalman filter. Secondly, the intrinsic relevance between the phase and frequency biases of replica signals and INS states is analyzed to accomplish the deep fusion of INS and tracking loop. Finally, semi-physical simulations are performed by using a GPS signal simulator to generate signals of two high dynamic trajectories. The experimental results indicate that the proposed ultra-tight integration algorithm can achieve a good performance on reliable positioning and robust tracking in high dynamic environments, compared with the conventional approaches such as tightly coupled integration strategy and third-order phase-locked loops.

Integrating monocular vision and laser point for indoor UAV SLAM

In order to realize the autonomous navigation of an unmanned aerial vehicle while exploring unknown indoor environments and meet the requirements of the indoor UAV positioning and indoor environmental reconstruction, a new strategy is presented based on monocular vision and a laser source. Combining laser-assisted odometry and a scan correlation algorithm with inertial information through an Extended Kaiman Filter (EKF) to realize the purpose localization and mapping. The laser-assisted distance measurement is presented in our paper depends on visual and geometric principle, which has the same effect of laser range finder after error corrections. The pose estimates can be obtained by laser scan matching which is used Sum of Gaussian (SoG) as the essential elements of mapping and localization. At last, in order to improve the autonomy of navigation, EKF is used to fuse inertial information. Experiments in an indoor environment validate the significant engineering reference value of the proposed algorithm.

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.

An Improved Multi-Sensor Fusion Navigation Algorithm Based on the Factor Graph

An integrated navigation system coupled with additional sensors can be used in the Micro Unmanned Aerial Vehicle (MUAV) applications because the multi-sensor information is redundant and complementary, which can markedly improve the system accuracy. How to deal with the information gathered from different sensors efficiently is an important problem. The fact that different sensors provide measurements asynchronously may complicate the processing of these measurements. In addition, the output signals of some sensors appear to have a non-linear character. In order to incorporate these measurements and calculate a navigation solution in real time, the multi-sensor fusion algorithm based on factor graph is proposed. The global optimum solution is factorized according to the chain structure of the factor graph, which allows for a more general form of the conditional probability density. It can convert the fusion matter into connecting factors defined by these measurements to the graph without considering the relationship between the sensor update frequency and the fusion period. An experimental MUAV system has been built and some experiments have been performed to prove the effectiveness of the proposed method.

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.

Chi-square and SPRT combined fault detection for multisensor navigation

A fault-detection algorithm for a redundant multisensor navigation system for hypersonic cruise vehicles (HCVs) is proposed. The algorithm comprehensively diagnoses failures according to the failure level monitored by the sequential probability ratio test (SPRT) and chi-square test as well as the failure trend monitored by the SPRT. A test statistics feedback-reset loop is also added to shorten the recovery time after failure ceases. Simulations indicate improvements in both failure detection and recovery speed, contributing to improved accuracy and stability in HCV fault-tolerant navigation.