Low-cost Integrated Navigation System Research Articles

Synergy in Future Avionics: An Overview of Multiple Technologies for Small Air Traffic Segment in the COAST project

The paper describes research and development activities under Clean Sky 2 Cost optimized Avionic System (COAST) program. The main goal of this development was to deliver technology enablers at TRL 5 for affordable cockpit and avionics. The target segment for the technology enablers is aircraft with 1 to 19 passengers and small cargo aircraft belonging to CS-23 category. The main aim is to provide overall summer during the whole COAST program development per individual technology. Sections are divided per each technology with their results and overall contribution to the program. The Clean Sky 2 COAST program covered the development of following technologies: Cockpit Architecture SAT avionic system architecture, Flight Management Tactical Separation System (TSS), Advanced Weather Awareness System (AWAS), Flight Reconfiguration System (FRS), Navigation and Surveillance Dual Frequency Multi-Constellation GNSS Receiver (GNSS), Low-cost Integrated Navigation System (NAV), Affordable Integrated Surveillance System (SURV), Platform technologies Compact Computing Platform (CCP), High Integrity Electronics for health monitoring (HIE), Integrated Mission Management System Integrated Mission Management System (IMMS). These technologies were part of several flight test campaigns which took place in the Czech Republic with Evektor company using EV-55 aircraft.

A low-cost integrated navigation system based on factor graph nonlinear optimization for autonomous flight

A low-cost integrated navigation system based on factor graph nonlinear optimization for autonomous flight

A cross product calibration method for micro-electro mechanical system gyroscope in unmanned aerial vehicle attitude determination system

This paper presents a novel calibration method for micro-electro mechanical system gyroscope in attitude measurement system of small rotor unmanned aerial vehicles. This method is based on an observation vector and its cross product, which is especially valuable for the in-field calibration without the aid of external equipment. By analysing the error model of the tri-axial gyroscope, the principle of calibration is proposed. Compared with other algorithms, numerical simulations are performed to evaluate the effectiveness of integral form of the cross product calibration method. Experiment on the hex-rotor unmanned aerial vehicle platform shows that the proposed method has great advantages in low-cost integrated navigation system.

Improved Position Estimation of Real Time Integrated Low-Cost Navigation System Using Unscented Kalman Filter

Low cost Micro Electrical Mechanical Systems (MEMS) inertial sensors have nominated their use in domains such as navigation systems but these sensors are noisy and characterized by their measurements drift and large errors. In this work, an integrated navigation system is implemented and its performance is evaluated through experimental work in both post processing and real time domains. The real time processing is built on multi-platform 32-bit ARM core ATMEL microcontroller while at the same time raw sensors measurements are saved for post processing under MATLAB. North and East position errors measurements were used in this work to evaluate position improvement calculated using MEMS inertial sensors integrated with position and velocity from Global Positioning System (GPS) receiver readings at slow rates. For such evaluation, the calibrated measurements from gyroscopes and accelerometers are fed to a mechanization process to build an inertial navigation system (INS). The INS drifting navigation solution is fused with a GPS measurement through Unscented Kalman Filter (UKF). UKF does not require linearization of the system model such as the status of the commonly used Extended Kalman Filter (EKF). The results of the experimental work show that the implemented low cost integrated navigation system based on UKF can achieve a level of accuracy superior than other (EKF) based expensive systems.

Application of Low Cost Integrated Navigation System in Precision Agriculture

To improve the positioning accuracy of farming vehicle in precision agriculture, an integrated positioning system is proposed based on Global Navigation Satellite System (GNSS)/Strapdown Inertial Navigation System (SINS)/Wireless Sensor Networks (WSN) with low cost and high reliability. The principles of commonly used localization technologies in vehicle positioning are compared and the Received Signal Strength Indication (RSSI) based measurement method is chosen as the integrated positioning system for information fusion considering the complexity of the algorithm, positioning accuracy and hardware requirements in the application scenario. The research of wireless signal propagation loss model of farmland environment was conducted. A set of GNSS/SINS/WSN integrated positioning system is designed and implemented based on the requirement analysis of low cost, high reliability and precision. Simulation experiments were conducted and the results show that integrated positioning system can improve the position accuracy of agricultural machinery and meet the precision positioning requirements of agricultural machinery.

Low Cost Integrated Navigation System for Unmanned Vessel

Abstract Large errors of low-cost MEMS inertial measurement unit (MIMU) lead to huge navigation errors, even wrong navigation information. An integrated navigation system for unmanned vessel is proposed. It consists of a low-cost MIMU and Doppler velocity sonar (DVS). This paper presents an integrated navigation method, to improve the performance of navigation system. The integrated navigation system is tested using simulation and semi-physical simulation experiments, whose results show that attitude, velocity and position accuracy has improved awfully, giving exactly accurate navigation results. By means of the combination of low-cost MIMU and DVS, the proposed system is able to overcome fast drift problems of the low cost IMU.

MEMS IMU and GPS/Beidou Integration Navigation System Using Interval Kalman Filter

This paper describes an integrated MEMS IMU and GNSS system for vehicles. The GNSS system is developed to accurately estimate the vehicle azimuth, and the integrated GNSS/IMU provides attitude, position and velocity. This research is aimed at producing a low-cost integrated navigation system for vehicles. Thus, an inexpensive solid-state MEMS IMU is used to smooth the noise and to provide a high bandwidth response. The integration system with uncertain dynamics modeling and uncertain measurement noise is also studied. An interval adaptive Kalman filter is developed for such an uncertain integrated system, since the standard extended Kalman filter (SKF) is no longer applicable, and a method of adaptive factor construction with uncertain parameter is proposed for the nonlinear integrated GNSS/IMU system. The results from the actual GNSS/IMU integrated system indicate that the filtering method proposed is effective.

ARM Based Low Cost Integrated Navigation System Technology Research

This paper mainly studies the integrated navigation system technology of GPS and microelectronic mechanical system (MEMS) concerning on embedded processor S3C2440A. The system has the advantages of low cost, fast speed and small size, etc., which give wide prospects in a personal navigation application. This paper introduces the system hardware circuit structure design, emphasize on GPS navigation, MEMS inertial device for auxiliary navigation system software algorithm, and use Matlab to do data simulation on system model. The results show that GPS/MEMS inertial component integrated navigation obviously improved relative to separate GPS navigation or Strapdown inertial navigation in the accuracy and application value.

Application of Interval Kalman Filtering Algorithm in Integrated Navigation Data Fusion

The traditional Kalman filter cannot be used directly when some system parameters such as certain elements of the system matrix are not precisely known or gradually change with time. Some uncertain parameters can be described as an interval model. An interval Kalman filtering algorithm is studied in this paper, which can be used to process a system with uncertain parameters. A simple inversion algorithm of interval matrix has been applied and its statistic performances and iterative form are similar to those of traditional Kalman filter. Simulation results show that such filtering algorithm can provide the real time accuracy error estimation and can be applied to such kind of low-cost integrated navigation system.

One Approach to the Integration of Low-Cost Inertial Sensors and Global Positioning System for Mobile Robots

AbstractMobile robots are reality these days. Advances in the new technologies, including relatively cheap sensors, new communication and internet technologies together with the increases in the computational speed provide significant advances in the mobile robots autonomy. Precise navigation parameters (position, velocity and attitude) play crucial role for mobile robots to respond autonomously. Supplying wrong or not precise navigation parameters to the Guidance and Control System can turn to be catastrophic for mobile robots. In navigation the trend is to use integrated systems. The idea is to integrate more navigation systems and to achieve better navigation accuracy. In this paper a low-cost integrated navigation system was developed where Extended Kalman Filter (EKF) was used for integration of the Global Positioning System (GPS) information and measurements from the single yaw gyro. Extensive practical experiments were carried out to validate the presented approach. The algorithm was tested on a mobile robot platform Itar Pejo and a car. The results show improvements of the integrated navigation system then the GPS and gyro used alone.

H∞ Sub-optimal Filter for Low-cost Integrated Navigation System

The integrated navigation system, specially the system based on GPS and INS, is a leading trend of navigation technology, and some of typical integrated navigation systems have been developed and tested in the past few years. On the other hand, because INS is very expensive in general, the application of GPS/INS system is also limited in some application fields. In this paper, a low-cost dynamic measurement unit (DMU) and GPS are integrated by using H∞ filter. At first, based on the mathematic description of H∞ filtering problem, a sub-optimal H∞ filter is discussed. Meanwhile, the relationship between H∞ filter and standard Kalman filter is studied. Aiming at the detail position/velocity integrated mode, the application of H∞ filter on low cost integrated navigation is researched through some simulations, including the performance of accuracy, robustness and stability.