Anti-interference self-alignment algorithm by attitude optimization estimation for SINS on a rocking base

The performance of a strapdown inertial navigation system(SINS) largely depends on the accuracy and rapidness of the initial alignment. A novel anti-interference self-alignment algorithm by attitude optimization estimation for SINS on a rocking base is presented in this paper. The algorithm transforms the initial alignment into the initial attitude determination problem by using infinite vector observations to remove the angular motions,the SINS alignment is heuristically established as an optim...     

New celestial assisted INS initial alignment method for lunar explorer

In the future lunar exploration programs of China,soft landing,sampling and returning will be realized.For lunar explorers such as Rovers,Landers and Ascenders,the inertial navigation system(INS) will be used to obtain high-precision navigation information.INS propagates position,velocity and attitude by integration of sensed accelerations,so initial alignment is needed before INS can work properly.However,traditional ground-based initial alignment methods cannot work well on the lunar surface because of its low rotation rate(0.55%).For solving this problem, a new autonomous INS initial alignment method assisted by celestial observations is proposed,which uses star observations to help INS estimate its attitude,gyroscopes drifts and accelerometer biases.Simulations show that this new method can not only speed up alignment,but also improve the alignment accuracy.Furthermore, the impact factors such as initial conditions,accuracy of INS sensors,and accuracy of star sensor on alignment accuracy are analyzed in details,which provide guidance for the engineering applications of this method.This method could be a promising and attractive solution for lunar explorer’s initial alignment.     

Design and application of single-antenna GPS/accelerometers attitude determination system

In view of the problem that the current single-antenna GPS attitude determination system can only determine the body attitude when the sideslip angle is zero and the multiantenna GPS/SINS integrated navigation system is of large volume, high cost, and complex structure, this approach is presented to determine the attitude based on vector space with single-antenna GPS and accelerometers in the micro inertial measurement unit （MIMU）. It can provide real-time and accurate attitude information. Subsequently, the single-antenna GPS/SINS integrated navigation system is designed based on the combination of position, velocity, and attitude. Finally the semi- physical simulations of single-antenna GPS attitude determination system and single-antenna GPS/SINS integrated navigation system are carried out. The simulation results, based on measured data, show that the single-antenna GPS/SINS system can provide more accurate navigation information compared to the GPS/SINS system, based on the combination of position and velocity. Furthermore, the single-antenna GPS/SINS system is characteristic of lower cost and simpler structure. It provides the basis for the application of a single-antenna GPS/SINS integrated navigation system in a micro aerial vehicle （MAV）.     

Novel approach to GPS/SINS integration for IMU alignment

Strapdown inertial navigation system(SINS) requires an initialization process that establishes the relationship between the body frame and the local geographic reference.This process,called alignment,is generally used to estimate the initial attitude angles.This is possible because an accurate determination of the inertial measurement unit(IMU) motion is available based on the measurement obtained from global position system(GPS).But the update frequency of GPS is much lower than SINS.Due to the non-synchronous data streams from GPS and SINS,the initial attitude angles may not be computed accurately enough.In addition,the estimated initial attitude angles may have relatively large uncertainties that can affect the accuracy of other navigation parameters.This paper presents an effective approach of matching the velocities which are provided by GPS and SINS.In this approach,a digital high-pass filter,which implements a pre-filtering scheme of the measured signal,is used to filter the Schuler cycle of discrete velocity difference between the SINS and GPS.Simulation results show that this approach improves the accuracy greatly and makes the convergence time satisfy the required accuracy.     

Accuracy improvement of GPS/MEMS-INS integrated navigation system during GPS signal outage for land vehicle navigation

To improve the reliability and accuracy of the global positioning system (GPS)/micro electromechanical system (MEMS)inertial navigation system (INS) integrated navigation system,this paper proposes two different methods.Based on wavelet threshold denoising and functional coefficient autoregressive (FAR) modeling,a combined data processing method is presented for MEMS inertial sensor,and GPS attitude information is also introduced to improve the estimation accuracy of MEMS inertial sensor errors.Then the positioning accuracy during GPS signal short outage is enhanced.To improve the positioning accuracy when a GPS signal is blocked for long time and solve the problem of the traditional adaptive neuro-fuzzy inference system (ANFIS) method with poor dynamic adaptation and large calculation amount,a self-constructive ANFIS (SCANFIS) combined with the extended Kalman filter (EKF) is proposed for MEMS-INS errors modeling and predicting.Experimental road test results validate the efficiency of the proposed methods.     

Optimal two-iteration sculling compensation mathematical framework for SINS velocity updating

A new two-iteration sculling compensation mathematical framework is provided for modern-day strapdown inertial navigation system（SINS） algorithm design that utilizes a new concept in velocity updating. The principal structure of this framework includes twice sculling compensation procedure using incremental outputs from the inertial system sensors during the velocity updating interval. Then, the moderate algorithm is designed to update the velocity parameter. The analysis is conducted in the condition of sculling motion which indicates that the new mathematical framework error which is smaller than the conventional ones by at least two orders is far superior. Therefore, a summary is given for SINS software which can be designed with the new mathematical framework in velocity updating.     

SINS/CNS/GPS integrated navigation algorithm based on UKF

A new nonlinear algorithm is proposed for strapdown inertial navigation system (SINS)/celestial navigation system (CNS)/global positioning system (GPS) integrated navigation systems. The algorithm employs a nonlinear system error model which can be modified by unscented Kalman filter (UKF) to give predictions of local filters. And these predictions can be fused by the federated Kalman filter. In the system error model, the rotation vector is introduced to denote vehicle’s attitude and has less variables tha...     

ROBUST CONSENSUS FOR MULTI-AGENT SYSTEMS OVER UNBALANCED DIRECTED NETWORKS

In this paper, a distributed consensus protocol is proposed for discrete-time single-integer multi-agent systems with measurement noises under general fixed directed topologies, The time-varying control gains satisfying the stochastic approximation conditions are introduced to attenuate noises, thus the closed-loop multi-agent system is intrinsically a linear time-varying stochastic difference system. Then the mean square consensus convergence analysis is developed based on the Lyapunov technique, and the construction of the Lyapunov function especially does not require the typical balanced network topology condition assumed for the existence of quadratic Lyapunov function. Thus, the proposed consensus protocol can be applicable to more general networked multi-agent systems, particularly when the bidirectional and/or balanced information exchanges between agents are not required. Under the proposed protocol, it is proved that the state of each agent converges in mean square to a common random variable whose mathematical expectation is the weighted average of agents＇ initial state values; meanwhile, the random variable＇s variance is bounded.     

Observability analysis of feature aided terminal guidance systems

Feature aided design of estimators and guidance laws can significantly improve the interception performance of the terminal guidance system. The achieved enhancement can be effectively assessed by observability analysis methods. This paper first analyzes and discusses the existing assessment methods in a typical endgame scenario with target orientation observations. To get over their deficiencies, a novel singular value decomposition(SVD) method is proposed. Employing both theoretical analysis and numerical simulation, the proposed method can represent the degree of state observability which is enhanced by integrating target features more completely and quantitatively.     

New rapid transfer alignment method for SINS of airborne weapon systems

Transfer alignment is an effective alignment method for the strapdown inertial navigation system （SINS） of airborne weapon systems. The traditional transfer alignment methods for large misalignment angles alignment use nonlinear transfer align- ment models and incorporate nonlinear filtering. A rapid transfer alignment method with linear models and linear filtering for ar- bitrary misalignment angles is presented. Through the attitude quaternion decomposition, the purpose of transfer alignment is converted to estimate a constant quaternion. Employing special manipulations on measurement equation, velocity and attitude linear measurement equations are derived. Then the linear trans- fer alignment model for arbitrary misalignment angles is built. An adaptive Kalman filter is developed to handle modeling errors of the measurement noise statistics. Simulation results show feasibili- ty and effectiveness of the proposed method, which provides an alternative rapid transfer alignment method for airborne weapons.     

Novel sensor selection strategy for LPI based on an improved IMMPF tracking method

Sensor platforms with active sensing equipment such as radar may betray their existence, by emitting energy that will be intercepted by enemy surveillance sensors. The radar with less emission has more excellent performance of the low probability of intercept（LPI）. In order to reduce the emission times of the radar, a novel sensor selection strategy based on an improved interacting multiple model particle filter（IMMPF） tracking method is presented. Firstly the IMMPF tracking method is improved by increasing the weight of the particle which is close to the system state and updating the model probability of every particle. Then a sensor selection approach for LPI takes use of both the target＇s maneuverability and the state＇s uncertainty to decide the radar＇s radiation time. The radar will work only when the target＇s maneuverability and the state＇s uncertainty exceed the control capability of the passive sensors. Tracking accuracy and LPI performance are demonstrated in the Monte Carlo simulations.     

Robust H_∞ Stabilization of Uncertain Linear Time-Delay System with Delayed/Undelayed State Feedback Controllers

1. INTRoDUCTIONThe dynamic behavior of many physical processes consists of inherent time--delays and uncertainties (due tomode1ing errors3 measu-rement errors9 1inearization approkimations, and so on), which can be modeled by anuncertain 1inear system with state/contro1 delay [1ro5l. Since the presence of time-delay is frequelltly a cause ofinstability, stability testing and robust stabilization of uncertain time--delay systems have gained considerableatteniion around the world. In this di…     

Unscented Kalman filter for SINS alignment

In order to improve the filter accuracy for the nonlinear error model of strapdown inertial navigation system (SINS) alignment, Unscented Kalman Filter (UKF) is presented for simulation with stationary base and moving base of SINS alignment. Simulation results show the superior performance of this approach when compared with classical suboptimal techniques such as extended Kalman filter in eases of large initial misalignment. The UKF has good performance in case of small initial misalignment.     

Research on ballistic missile laser SIMU error propagation mechanism

It is necessary that the laser inertial system is used to further improve the fire accuracy and quick reaction capability in the ballistic missile strapdown inertial navigation system. According to the guidance controlling method and the output and error model of ballistic missile laser SIMU, the mathematical model of error propagation mechanism is set up and any transfer environmental function of error coefficient that affects the fire accuracy is deduced. Also, the missile longitudinal/lateral impact point is calculated using MATLAB. These establish the technical foundation for further researching the dispersion characteristics of impact point and reducing the laser guidance error.     

GPSNISNAV integrated relative navigation and attitude determination system for ultra-close spacecraft formation flying

For the improvement of accuracy and better fault-tolerant performance, a global position system (GPS)/vision navigation (VISNAV) integrated relative navigation and attitude determination approach is presented for ultra-close spacecraft formation flying. Onboard GPS and VISNAV system are adopted and a federal Kalman filter architecture is used for the total navigation system design. Simulation results indicate that the integrated system can provide a total improvement of relative navigation and attitude estimation performance in accuracy and fault-tolerance.     

Research on improved integrated FDD/FTC with effectiveness factors

This paper investigates the integrated fault detection and diagnosis(FDD) with fault tolerant control(FTC) method of the control system with recoverable faults.Firstly,a quasi-linear parameter-varying(QLPV) model is set up,in which effectiveness factors are modeled as time-varying parameters to quantify actuators and sensors faults.Based on the certainty equivalency principle,replacing the real time states in the nonlinear term of the QLPV model with the estimated states,the parameters and states can be estimated by a two-stage Kalman filtering algorithm.Then,a polynomial eigenstructure assignment(PEA) controller with time-varying parameters and states is designed to guarantee the performance of the system with recoverable faults.Finally,mathematical simulation is performed to validate the solution in a satellite closed-loop attitude control system,and simulation results show that the solution is fast and effective for on-orbit real-time computation.     

Sensor fault-tolerant observer applied in satellite attitude control

The observing failure and feedback instability might happen when the partial sensors of a satellite attitude control sys- tem (SACS) go wrong. A fault diagnosis and isolation (FDI) method based on a fault observer is introduced to detect and isolate the fault sensor at first. Based on the FDI result, the object system state-space equation is transformed and divided into a corresponsive triangular canonical form to decouple the normal subsystem from the fault subsystem. And then the KX fault-tolerant observers of the system in different modes are designed and embedded into online monitoring. The outputs of all KX fault-tolerant observers are selected by the control switch process. That can make sense that the SACS is part-observed and in stable when the partial sensors break down. Simulation results demonstrate the effectiveness and superiority of the proposed method.     

OPTIMAL TARGET TRAJECTORY ESTIMATION AND FILTERING USING NETWORKED SENSORS

Target tracking using distributed sensor network is in general a challenging problem because it always needs to deal with real-time processing of noisy information. In this paper the problem of using nonlinear sensors such as distance and direction sensors for estimating a moving target is studied. The problem is formulated as a prudent design of nonlinear filters for a linear system subject to noisy nonlinear measurements and partially unknown input, which is generated by an exogenous system. In the worst case where the input is completely unknown, the exogenous dynamics is reduced to the random walk model. It can be shown that the nonlinear filter will have optimal convergence if the number of the sensors are large enough and the convergence rate will be highly improved if the sensors are deployed appropriately. This actually raises an interesting issue on active sensing： how to optimally move the sensors if they are considered as mobile multi-agent systems？ Finally, a simulation example is given to illustrate and validate the construction of our filter.     

Fast fine acquisition algorithm of GPS receiver aided by INS information

Acquisition time of global position system (GPS) receiver, which is the main factor contributes to time to first fix (TTFF), can be shortened by estimating the Doppler frequency shift through external inertial navigation system (INS) information and almanac data and reducing the searching area. The traditional fast acquisition is analyzed, the fast acquisition of the GPS receiver aided is presented by INS information, and the signal is fine captured by spectrum zooming. Then the algorithm is simulated by sampled GPS intermediate frequency (IF) signal and the result verifies that this acquisition can dramatically improve the capability of GPS receiver and reduce its acquisition time.