Optimizing of ANFIS for estimating INS error during GPS outages.

Global positioning system (GPS) has been extensively used for land vehicle navigation systems. However, GPS is incapable of providing permanent and reliable navigation solutions in the presence of signal evaporation or blockage. On the other hand, navigation systems, in particular, inertial navigation systems (INSs), have become important components in different military and civil applications due to the recent advent of micro-electro-mechanical systems (MEMS). Both INS and GPS systems are often paired together to provide a reliable navigation solution by integrating the long-term GPS accuracy with the short-term INS accuracy. This article presents an alternative method to integrate GPS and INS systems and provide a robust navigation solution. This alternative approach to Kalman filtering (KF) utilizes artificial intelligence based on adaptive neuro-fuzzy inference system (ANFIS) to fuse data from both systems and estimate position and velocity errors. The KF is usually criticized for working only under predefined models and for its observability problem of hidden state variables, sensor error models, immunity to noise, sensor dependency, and linearization dependency. The training and updating of ANFIS parameters is one of the main problems. Therefore, the challenges encountered implementing an ANFIS module in real time have been overcome using particle swarm optimization (PSO) to optimize the ANFIS learning parameters since PSO involves less complexity and has fast convergence. The proposed alternative method uses GPS with INS data and PSO to update the intelligent PANFIS navigator using GPS/INS error as a fitness function to be minimized. Three methods of optimization have been tested and compared to estimate the INS error. Finally, the performance of the proposed alternative method has been examined using real field test data of MEMS grade INS integrated with GPS for different GPS outage periods. The results obtained outperform KF, particularly during long GPS signal blockage

Wavelet-based pre-filtering for low cost inertial sensors

This study proposed to de-noise the IMU signal by effectively band-limiting the signal at the output of each inertial measurement sensor prior to its mechanization and further processing by the Strapdown INS (SDINS) algorithm. Wavelet Multi-Resolution Algorithm (WMRA) is utilized to improve the performance of the inertial sensors by removing their short term noise. The aim of this study is to reveal how WMRA is utilized to improve the performance of the inertial measurement unit systems and investigate how wavelet analysis can be used to analyse and de-noise output of the low-cost inertial sensors. The proposed multi-level decomposition was applied to real accelerometer and gyroscopes data obtained from MEMS IMU (MotionPak II). Different level of decomposition and thresholding filter was evaluated to obtain optimal results. Analysis of the results demonstrate reducing the INS position and velocity error for the specific IMU

A review of navigation systems (integration and algorithms)

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 novel intelligent predictor for low-rate global positioning system (GPS) system

Global positioning system (GPS) is the most common instrument utilized for navigational purpose. Unfortunately these satellite signals may get lost due to signal blockage. On the other hand, inertial navigation systems (INSs) can address this problem and overcome the non-availability of GPS signals for a short period of time due to the inherent sensors errors. In such case, INSs can benefit from aiding such as GPS. The difference in sampling rate between the GPS and INS must be overcome to realize the integration of the two systems. In general, Kalman filter (KF) is used to predict GPS data in order to integrate signals from high data rate systems, like INSs, with GPS that have low data rate. However, KF is usually criticized for working under predefined linear dynamic error models. In this paper, adaptive neuro fuzzy inference system (ANFIS) trained using genetic algorithm (GA) was adopted to predict the mislaid reading data for GPS to be synchronized with those of INS data. Hence, the gap between the two systems reading data is solved to provide synchronization between the INS and GPS systems. So, it is possible to compare the reading data of both systems. Three strategies have been proposed and the results shows superior performance in predicting missed GPS data with lowest mean square error