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The integration of Inertial Navigation System (INS) and Global Positioning System (GPS) can produce accurate results if four or more GPS satellites are tracked. However, in GPS attenuated signal environment the errors of a low accuracy MEMS/GPS system rapidly grow to the unacceptable level. A tightly coupled integration scheme is utilized to improve the performance and reliability of the low accuracy integrated system in the areas such as tunnels, tall buildings, urban canyon, and forest canopy. This model is capable to detect the GPS fault and to track the errors of the integrated system even when less than four satellites are being tracked. Practically in INS/GPS integration, the system noises are not known correctly. Therefore, an Adaptive Kalman filter is proposed to merge the data of the two systems accurately. The algorithms are tested using the real data of MEMS-IMU (STIM300) and a single frequency NovAtel GPS receiver for land navigations. The integration results indicate a significant improvement in the accuracy of attitude, velocity and position parameters. Moreover, gyro drift which is the main source of errors in INS parameters is significantly reduced.

A robust integration algorithm for Strapdown Inertial Navigation System/Celestial Navigation System (SINS/CNS) is designed and investigated in this paper. The main objective of the developed integration scheme is the estimation and correction of unacceptable SINS' errors in long time navigation missions. A Narrow Field of View (NFOV) star tracker is used as a CNS aid in this study. A mathematical model is designed to estimate the platform misalignment angles and transformations of different frames are exercised. Linearized Kalman Filter (LKF), with a modified measurement model, encompassing the position errors, is proposed for fusing the data of SINS and CNS. Finally, simulation results are produced to show the integrity and validity of the integration algorithm for estimation and correction of the SINS errors in extended time flights.

In this paper, a method for real-time fault detection and isolation is presented, which has been tested successfully for unmanned aerial vehicles. In this method, two parity vectors are constructed to achieve the fault detection and isolation. In addition, the maximum likelihood estimate is used to estimate and correct the fault vector. Simulation results show that not only can the faulty system be detected accurately in time by this method, but also the fault can be repaired efficiently. Thus, the fault tolerance of the integrated system is improved remarkably by this method.

This paper discusses the techniques of attitude, velocity ad position estimation from GNSS carrier phase measurements, and investigates the performance of the lower precision MEMS-based INS/GNSS system based on carrier phase measurements. Double differenced carrier phase measurements provide more accurate velocity and position estimation compared to code and Doppler measurements. However, integer ambiguity is required to be removed for precise positioning. Multiples antennae approach is used to derive the attitude information from carrier phase measurements in order to control the large initial misalignment angles for initialization of the integration process or to utilize during benign dynamics. Lever arm effect is considered to compensate for the separation of GNSS antenna and IMU location. The derived three GNSS observables are used to correct the INS through optimal Kalman filtering in a closed loop. Simulation results indicates the effectiveness of the integrated system for airborne as well as for land navigation vehicles. This paper discusses the techniques of attitude, velocity ad position estimation from GNSS carrier phase measurements, and investigates the performance of the lower precision MEMS based INS/GNSS system based on carrier phase measurements. Double differenced carrier phase measurements provide more accurate velocity and position estimation compared to code and Doppler measurements. However, integer ambiguity is required to be removed for precise positioning. Multiples antennae approach is used to derive the attitude information from carrier phase measurements in order to control the large initial misalignment angles for initialization of the integration process or to utilize during benign dynamics. Lever arm effect is considered to compensate for the separation of GNSS antenna and IMU location. The derived three GNSS observables are used to correct the INS through optimal Kalman filtering in a closed loop. Simulation results indicates the effectiveness of the integrated system for airborne as well as for land navigation vehicles.

The two-stage Kalman filter requires the accurate information of unknown random bias. Unfortunately, this algebraic constraint is seldom satisfied for practical systems. The adaptive solution of estimating a set of dynamic state in the presence of a random bias employing a two-stage Kalman estimator is addressed. The solution performed well when the information of unknown random bias was inaccurate. The validity of the solution is verified with a simulative example.

In test-launch control system of missiles, the relations between observed information and fault causes are complicated. Neural network is an effective method to diagnose this type of faults. But, to recede the complex of neural network is a main job in diagnosis. The rough sets theory was introduced in fault diagnosis via neural network to eliminate the unnecessary attributes and disclose the redundancy of condition attributes. Using the decision table, this approach extracted the diagnosis rules from the set of fault samples directly. A case study was used to illustrate the application of the proposed approach. Result shows that the approach is valid.