Model-based robust estimation and fault detection for MEMS-INS/GPS integrated navigation systems

In micro-electro-mechanical system based inertial navigation system（MEMS-INS）/global position system（GPS） integrated navigation systems, there exist unknown disturbances and abnormal measurements. In order to obtain high estimation accuracy and enhance detection sensitivity to faults in measurements, this paper deals with the problem of model-based robust estimation（RE） and fault detection（FD）. A filter gain matrix and a post-filter are designed to obtain a RE and FD algorithm with current measurements, which is different from most of the existing priori filters using measurements in one-step delay. With the designed filter gain matrix, the H-infinity norm of the transfer function from noise inputs to estimation error outputs is limited within a certain range; with the designed post-filter, the residual signal is robust to disturbances but sensitive to faults. Therefore, the algorithm can guarantee small estimation errors in the presence of disturbances and have high sensitivity to faults. The proposed method is evaluated in an integrated navigation system, and the simulation results show that it is more effective in position estimation and fault signal detection than priori RE and FD algorithms.

NONLINEAR FILTERING ALGORITHM FOR IN S INITIAL ALIGNMENT

The initial alignment error equation of an INS (Inertial Navigation System) with large initial azimuth error has been derived and nonlinear characteristics are included. When azimuth error is fairly small, the nonlinear equation can be reduced to a linear one. Extended Kalman filter, iterated filter and second order filter formulas are derived for the nonlinear state equation with linear measurement equation. Simulations results show that the accuracy of azimuth error estimation using extended Kalman filter is better than that of using standard Kalman filter while the iterated filter and second order filter can give even better estimation accuracy.

STUDY OF FAST TRANSFER ALIGNMENT ON MOVING BASE

This paper holds that the key to improve the hitting rate of air-to-air missiles is to decrease the error of initial alignment of the inertial navigation system. Therefore, considering that the alignment should be completed within a specified short time, this paper presents the theory of transfer alignment and the computing way of accuracy in an air-to-air missile, where the platform inertial navigation system, or master INS, is adopted on aircraft, and the strap-down inertial navigation system, or slave INS, is used on missile. It emphasizes the idea of transfer alignment, that is, calibration of the slave INS is based on the master platform, and adopts a reasoning measure to deal with the installing-error-angle. And it is proved by simulation that the transfer alignment can be quickly achieved.

A novel temperature-compensation method based on correlation analysis for multi-FOG INS

Aimed at improving the bias stability of Fiber-Optic Gyroscope(FOG)-based inertial navigation systems in environments of various ambient temperatures,a novel temperaturecompensation method based on a correlation analysis of the same batch of FOGs is proposed.The empirical mode decomposition method was employed to filter the high-frequency noises of the FOGs.Then,the correlation information of the multiple FOGs was used to analyze the feasibility of the method.Eventually,the same residual error of the FOGs was compensated via the simple piecewise linear models.The experimental results indicate that excellent compensation effects for both high-and low-accuracy FOGs are achieved using the proposed method.Specifically,the accuracies of high-accuracy FOGs are improved by approximately 33.9%,20%,and 31.2%,while those of low-accuracy FOGs are improved by approximately 39.1%,20.8%,and 26.1%.The method exhibits the merits of simplicity,validity,and stability,and thus is expected to be widely used in engineering applications.

Multiple Fading Factors Kalman Filter for SINS Static Alignment Application

To solve the problem that the standard Kalman filter cannot give the optimal solution when the system model and stochastic information are unknown accurately, single fading factor Kalman filter is suitable for simple systems. But for complex systems with multi-variable, it may not be sufficient to use single fading factor as a multiplier for the covariance matrices. In this paper, a new multiple fading factors Kalman filtering algorithm is presented. By calculating the unbiased estimate of the innovation sequence covariance using fenestration, the fading factor matrix is obtained. Adjusting the covariance matrix of prediction error Pk｜k-1 using fading factor matrix, the algorithm provides different rates of fading for different filter channels. The proposed algorithm is applied to strapdown inertial navigation system （SINS） initial alignment, and simulation and experimental results demonstrate that, the alignment accuracy can be upgraded dramatically when the actual system noise characteristics are different from the pre-set values. The new algorithm is less sensitive to uncertainty noise and has better estimation effect of the parameters. Therefore, it is of significant value in practical applications.

Mahalanobis distance-based fading cubature Kalman filter with augmented mechanism for hypersonic vehicle INS/CNS autonomous integration

Inertial Navigation System/Celestial Navigation System(INS/CNS)integration,especially for the tightly-coupled mode,provides a promising autonomous tactics for Hypersonic Vehicle(HV)in military demands.However,INS/CNS integration is a challenging research task due to its special characteristics such as strong nonlinearity,non-additive noise and dynamic complexity.This paper presents a novel nonlinear filtering method for INS/CNS integration by adopting the emerging Cubature Kalman Filter(CKF)to handle the strong INS error model nonlinearity caused by HV's high dynamics.It combines the state-augmentation technique into the nonlinear CKF to decrease the negative effect of non-additive noise in inertial measurements.Subsequently,a technique for the detection of dynamic model uncertainty is developed,and the augmented CKF is modified with fading memory to tackle dynamic model uncertainty by rigorously deriving the fading factor via the theory of Mahalanobis distance without artificial empiricism.Simulation results and comparison analysis prove that the proposed method can effectively curb the adverse impacts of non-additive noise and dynamic model uncertainty for inertial measurements,leading to improved performance for HV navigation with tightly-coupled INS/CNS integration.