Significant developments and technical trends in the area of navigation systems are reviewed. In particular, the integration of the Global Positioning System (GPS) and Inertial Navigation System (INS) has been an important development in modern navigation. The review concentrates also on the analysis, investigation, assessment and performance evaluation of existing integrated navigation
systems of accuracy, performance, low cost and all the issues that aid in optimizing their operating efficiency. The integration of GPS and INS has been successfully used in practice during the past decades. However, much of the work has focused on the use of a high accuracy Inertial Measurement Unit (IMU), which is an inertial sensors block without navigation solution output, and hence, this research area is also reviewed in this paper.

A Review of Navigation Systems (Integration and Algorithms)

To improve the ability of autonomous navigation for Unmanned Surface Vehicles (USVs), multi-sensor integrated navigation based on Inertial Navigation System (INS), Celestial Navigation System (CNS) and Doppler Velocity Log (DVL) is proposed. The CNS position and the DVL velocity are introduced as the reference information to correct the INS divergence error. The autonomy of the integrated system based on INS/CNS/DVL is much better compared with the integration based on INS/GNSS alone. However, the accuracy of DVL velocity and CNS position are decreased by the measurement noise of DVL and bad weather, respectively. Hence, the INS divergence error cannot be estimated and corrected by the reference information. To resolve the problem, the Adaptive Information Sharing Factor Federated Filter (AISFF) is introduced to fuse data. The information sharing factor of the Federated Filter is adaptively adjusted to maintaining multiple component solutions usable as back-ups, which can improve the reliability of overall system. The effectiveness of this approach is demonstrated by simulation and experiment, the results show that for the INS/CNS/DVL integrated system, when the DVL velocity accuracy is decreased and the CNS cannot work under bad weather conditions, the INS/CNS/DVL integrated system can operate stably based on the AISFF method.

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Performance Enhancement of a USV INS/CNS/DVL Integration Navigation System Based on an Adaptive Information Sharing Factor Federated Filter.

This paper addresses the theoretical and experimental development of a calibration scheme to overcome the intrinsic limitations of a low-cost Micro-Electrical-Mechanical System (MEMS) based Inertial Measurement Unit (IMU). The two-stage calibration algorithm was developed and tested successfully on a six-degree of freedom prototype MEMS IMU to determine the deterministic and stochastic errors of the sensor. This paper makes use of artificial observations known as pseudo-velocity measurements resulting from a specific scheme of rotation to calibrate the IMU in the laboratory environment. The proposed structure is then modified and utilised as a basis for the IMU's error estimation in outdoor navigation applications. For this purpose, the designed calibration method is applied to an integrated GPS/MEMS IMU system, showing improved navigational and road sign positioning performance in a test vehicle.

Practical considerations in precise calibration of a low-cost MEMS IMU for road-mapping applications

The LEGO Mindstorms kit is widely used in many areas of education and research, covering a number of applications from benchtop demonstrations to postgraduate research, laboratories, and projects [1][3]. Although the kit was initially designed as a toy for children over 12 years of age, its use in university courses is increasing yearly. The kit has attracted the interest of people working in many areas, including artificial intelligence, embedded systems, control systems, robotics, and operating systems. The LEGO Mindstorms kit is inexpensive, easily reconfigurable, reprogrammable, versatile, and robust, which makes it well suited for use in teaching [4].

A Monoball Robot Based on LEGO Mindstorms [Focus on Education]

WKNN indoor location algorithm based on zone partition by spatial features and restriction of former location

The objective of this paper is to analyze the estimation performance of gyroscope drifts in INS/GPS integrated navigation systems, while system is in motion. Strapdown inertial navigation system (SINS) error model is augmented by inertial sensor bias and drift and GPS clock bias and drift to improve the integration performance. To estimate the augmented state error, instead of GPS position, GPS pseudoranges and pseudorange rates are used. In tightly-coupled integration GPS and INS under different motion conditions, it is observed that the vertical gyro drift estimation is very weak in all cases of stationary, linear motion, and three-axis sway motion via Kalman filter covariance analysis. In order to solve the vertical gyroscope drift estimation problem, zero update velocity (ZUPT) algorithm is proposed to improve the observability of vertical gyro drift. Computer simulation is carried out by this algorithm to validate the feasibility of the concept. The simulation results demonstrate that vertical gyro drift can be better estimated for the sake of compensation afterward.

Hui Jia

Papers

Gyroscope Drift Estimation in Tightly-coupled INS/GPS Navigation System