Improvement of an integrated global positioning system and inertial navigation system for land navigation application

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 attenuation or blockage. On the other hand, navigation systems, in particular, inertial navigation systems (INS), have become important components in different military and civil applications due to the recent advent of microelectro- mechanical systems (MEMS). Both INS and GPS are not so far apart and they are often paired together to provide a reliable navigation solution by integrating the long-term GPS accuracy with the short-term INS accuracy. Therefore, this work is concerned to presents an alternative method to integrate GPS and INS systems and provide a robust navigation solution with trusted position and velocity information. Cascaded de-noising method based on discrete wavelet transform (DWT) is exploited in this work to filter out the MEMS inertial sensors. In addition, in this work a GPS predictor is developed to incorporate information from the accelerometers and gyroscopes at high rates and information from GPS measurements at low rates to improve the vehicle strapdown inertial navigation system (SDINS) with the aid of GPS. This work also presents a new method for de-noising the GPS and INS data and estimate the INS error using wavelet multi-resolution analysis algorithm (WMRA) based particle swarm optimization (PSO) with a well designed structure appropriate for practical and real time implementations due to its very short optimizing time and elevated accuracy. The proposed hybrid method is simple, easy to implement and can be used to automate the INS-error estimation step used in the proposed integrated GPS/INS navigator. Moreover, three alternative GPS/INS integration structures have been proposed. The developed navigators utilize artificial intelligence (AI) based on adaptive neuro-fuzzy inference system (ANFIS), to fuse data from both systems and estimate position and velocity errors. Most integration systems based on Kalman filter (KF) which 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 proposed GPS/INS integration has been evaluated during various GPS signal conditions including continuous and non-continuous satellites signals. Finally, performance evaluation for the proposed integrated GPS/INS navigator provides a reliable navigation solution including position and velocity information. A comparative study using different structures for GPSIINS integrations are conducted to test the performance in terms of accuracy and time required for training mode. The experimental results using real field test data show also the improvements in predicting the INS error for both position and velocity. The integrated GPSIINS system is able to maintain satisfactory accuracy with the maximum error less than 0.82, 0.78, and 0.83 m for position and 0.0414, 0.0273, and 0.0415 m1s for velocity in all directions during maximum GPS outages of 200 second while it requires less than 9 and 5 seconds for learning mode in position and velocity respectively.

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