Time filter

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Ushaq M.,Beihang University | Cheng F.J.,Beihang University | Ali J.,Center for Control and Instrumentation
Applied Mechanics and Materials | Year: 2013

The Strapdown Inertial Navigation System (SINS) renders excellent attitude, position and velocity solutions on short term basis, but when used as stand-alone navigation system, its accuracy deteriorates with the passage of time.On the other hand GPS has long-standing stability with a consistent precisiongenerally having only bounded random errors in position and velocity. Integrated navigation system is used to augment the complementary features of SINS and GPS. In integrated navigation system external fixes for position and/or velocity and/or attitude are used to contain the growing errors of SINS. Kalman filter is generally used as integration tool for integrated navigation system. Kalman filter algorithm is based on the assumptions that the system model and the measurement models are linear and the system random errors and measurement random errors are Gaussian in nature expressed withfixed covariances. But in real navigation systems these assumptions are seldom fulfilled and hence Kalman filter renders unsatisfactory results. Adaptive Kalman filter provides the solution to the problem by adjusting the system noise covariance and measurement noise covariance in real time in the light of actual measurement errors or actual dynamics of thevehicle. In this paper an innovation and residual based adaption of measurement noise covariance and system noise covariance is presented. The presented scheme has been applied on an SINS/GPS Integrated Navigation Systemand it has been validated that the scheme provide significantly better results as compared to standard Kalman filter on occurrence slowly growing errors as well as excessive random errors in GPS measurements. © (2013) Trans Tech Publications, Switzerland.

Ushaq M.,Beihang University | Cheng F.J.,Beihang University | Ali J.,Center for Control and Instrumentation
Applied Mechanics and Materials | Year: 2013

The complementary characteristics of the Strapdown Inertial Navigation System (SINS) and external non-inertial navigation aids like Global Positioning System (GPS) and Celestial Navigation System (CNS) make the integrated navigation system an appealing and cost effective solution for various applications. SINS exhibits position errors owing to imperfection in initialization of the inertial measurement unit (IMU) and the inherent accelerometer biases and gyroscope drifts. SINS also suffer from diverging azimuth errors and an exponentially increasing vertical channel error. Pitch and roll errors also exhibit unbounded growth with time. To mitigate this behavior of SINS, periodic corrections are opted for through measurements from external noninertial navigation aids. These corrections can be in the form of position fixing, velocity fixing and/or attitude fixing from external aids like GPS, GLONASS (Russian Satellite Navigation System), BEIDU(Chinese Satellite Navigation System) and Celestial Navigation Systems (CNS) etc. In this research work GPS and CNS are used as external aids for SINS and the navigation solutions of all three systems (SINS, GPS and CNS) are fused using Federated Kalman Filter (FKF). The FKF differs from the conventional Central Kalman Filter (CKF) because each measurement is processed in Local Filters (LFs), and the results are combined in a Master Filter (MF). The SINS acts as a cardinal system in the combination, and its data is available as measurement input for the local filter and master filter. In this research work, information from the GPS and the CNS are dedicated to the corresponding LFs. Each LF provides its solutions to the master filter where all information is fused together forming a global solution. Simulation for the proposed architecture has validated the effectiveness of the scheme, by showing the substantial precision improvement in the solutions of position, velocity and attitude as compared to the pure SINS or any other standalone system. © (2013) Trans Tech Publications, Switzerland.

Ali J.,Center for Control and Instrumentation | Ushaq M.,Beihang University | Majeed S.,Center for Control and Instrumentation
Journal of Marine Science and Technology (Japan) | Year: 2012

The strapdown inertial navigation system (SINS) is able to provide continuous estimates of a vehicle’s velocity, position and attitude. As a rule, the SIMS component known as a high accuracy strapdown inertial measurement unit (SIMU) is an exceptionally expensive system. Less expensive SIMUs comprised of low cost sensors suffer from degraded performance, but this can be compensated for, in part, by addition of a velocity data recorder (VDR) to accompany the SINS. In this configuration, the need frequently arises to align the SINS of a submarine to in order to avoid a long run-up of the inertial system before a start command is issued. This in-motion alignment (IMA) can be accomplished by integrating SINS data with some external aiding source, such as the VDR, by using some form of measurement matching method. Accordingly, this paper demonstrates a consistent IMA scheme for a low-cost SIMU using a robust Kalman/H1 filter structure. An error model of the SINS is derived in which the state vector includes attitude, velocity, position and sensor errors. Velocity information from the VDR is used as a measurement to the proposed filter. All significant equations concerning navigation are presented in conjunction with argument. Results show the advantages of the approach and emphasize diverse aspects of the SINS. © JASNAOE 2012.

Samar R.,Center for Control and Instrumentation | Rehman A.,Center for Control and Instrumentation
Mechatronics | Year: 2011

This paper presents an integrated guidance and control design scheme for an unmanned air vehicle (UAV), and its flight test results. The paper focuses on the longitudinal control and guidance aspects, with particular emphasis on the terrain-following problem. An introduction to the mission, and the terrain-following problem is given first. Waypoints for climb and descent are defined. Computation of the reference trajectory in the vertical plane is discussed, including a terrain-following (TF) algorithm for real-time calculation of climb/descent points and altitudes. The algorithm is particularly suited for online computation and is therefore useful for autonomous flight. The algorithm computes the height at which the vehicle should fly so that a specified clearance from the underlying terrain is always maintained, while ensuring that the vehicle's rate of climb and rate of descent constraints are not violated. The output of the terrain-following algorithm is used to construct a smooth reference trajectory for the vehicle to track. The design of a robust controller for altitude tracking and stability augmentation of the vehicle is then presented. The controller uses elevators for pitch control in the inner loop, while the reference pitch commands are generated by the outer altitude control loop. The controller tracks the reference trajectory computed by the terrain-following algorithm. The design of an electromechanical actuator for actuating the control surfaces of the vehicle during flight is also discussed. The entire guidance and control scheme is implemented on an actual experimental vehicle and flight test results are presented and discussed. © 2010 Elsevier Ltd. All rights reserved.

Samar R.,Center for Control and Instrumentation | Kamal W.A.,Center for Control and Instrumentation
Cognitive Computation | Year: 2012

In this paper, a path planning approach is developed and demonstrated for an unmanned aerial vehicle (UAV); the algorithm is applicable for autonomous robot path planning also. The main contribution of the paper is the development of an extension to the Bellman-Ford algorithm that enables incorporation of constraints directly into the algorithm during run-time. This, therefore, provides a framework for path planning, which does not cause violation of the dynamical constraints of the vehicle (or robot), such as its angle of turn. Furthermore, a procedure for computing a number of sub-optimal paths is developed so that a range of options is available for selection; the optimality of the paths is also proved. These sub-optimal paths are generated in an order of priority (optimality). An objective function is developed that models different conflicting objectives in a unified framework; these objectives can be assigned different weights. The objectives may include minimizing the length of the path, keeping the path as straight as possible, visiting areas of interest, avoiding obstacles, approaching the terminal point from a given direction, etc. The algorithm is tested for complex mission objectives, and results are discussed. © 2011 Springer Science+Business Media, LLC.

Ali J.,Center for Control and Instrumentation | Mirza M.R.U.B.,Center for Control and Instrumentation
Nonlinear Dynamics | Year: 2010

The Kalman filter is a familiar minimum mean square estimator for linear systems. In practice, the filter is frequently employed for nonlinear problems. This paper investigates into the application of the Kalman filter's nonlinear variants, namely the extended Kalman filter (EKF), the unscented Kalman filter (UKF) and the second order central difference filter (CDF2). A low cost strapdown inertial navigation system (SINS) integrated with the global position system (GPS) is the performance evaluation platform for the three nonlinear data synthesis techniques. Here, the discrete-time nonlinear error equations for the SINS are implemented. Test results of a field experiment are presented and performance comparison is made for the aforesaid nonlinear estimation techniques. © Springer Science+Business Media B.V. 2010.

Ali J.,Center for Control and Instrumentation
IET Science, Measurement and Technology | Year: 2010

The integration of strapdown inertial navigation system (SINS) with an astronavigation system (ANS) using fuzzy logic to adapt the Kalman filter is investigated in this study. A method is proposed that is based on fuzzy rules to adjust the parameters of a traditional Kalman filter. Simulation results for a spacecraft application demonstrate the validity of the method for improving the navigation system reliability, adaptability and performance. © 2010 © The Institution of Engineering and Technology.

Ali J.,Center for Control and Instrumentation | Ullah Baig Mirza M.R.,Center for Control and Instrumentation
Measurement: Journal of the International Measurement Confederation | Year: 2011

Alignment is the process whereby the orientation of the axes of an inertial navigation system is determined with respect to the reference system. In this paper, the initial alignment error equations of the strapdown inertial navigation system (SINS) with large initial azimuth error have been derived with inclusion of nonlinear characteristics. Simulations have been carried out to validate and corroborate the stationary alignment case employing a strapdown inertial measurement unit (SIMU). A performance comparison between the extended Kalman filter (EKF), the unscented Kalman filter (UKF) and the second-order divided difference filter (DDF2) demonstrate that the accuracy of attitude error estimation using the DDF2 is better than that of using the EKF or the UKF. © 2011 Elsevier Ltd. All rights reserved.

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