1,059 research outputs found

    Transfer Alignment Technique for Shipboard Missile Strapdown Inertial Navigation System using an Adaptive Kalman Filter

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    Missile Guidance system needs accurate estimates from Inertial Navigation System (INS) for guiding the vehicle towards the target. In this paper a target point, specified before launch, in a battlefield scenario is considered for a landmark using missile Strap Down Inertial Navigation System (SDINS) aided by Master INS (MINS) placed on a moving platform. Azimuth information of the missile is one of the most critical navigation states for estimation on the moving platform before launching the missile for precise impact. An Adaptive Kalman Filter (AKF) based on the error state model is formulated. The 7-state AKF with 4-measurement forms the core, where the filter gain of the innovation sequence (measurements) is evaluated. This approach of adaptively computing the gain is tested in a laboratory, on a van and in a ship trial, culminating in a successful guided missile launch. Mean and the covariance of the measurement residuals were used in a unique way to compute adaptive gain after the accumulation of initial samples. A Master INS (with advanced Gyros) whose accuracy is much higher than the accuracy of the missile’s SDINS is used for velocity matching algorithm before the launch with execution of an S-maneuver for generation of accelerations towards observing the states more appropriately. Estimated error states were used in a feedback mode to get near the true orientation of the Missile’s slave INS. Error quaternions are used for this purpose in the feedback and the gains were selected using offline matrix Riccati equation solution in a discrete domain as used in the modern control system. The results were very encouraging with less than 5 arc minutes of error in azimuth

    Advanced Navigation System for Aircraft Applications

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    Various forms of navigation are present in today’s world, leading from satellite based navigation to several archaic forms of navigation like star gazing. Now, lots of technologies are available to achieve this but with certain limitations. For example, FOG based navigation provides accuracy with in 0.10-100 range which is not sufficient for various military applications. Therefore, there is a need to design a system which will have better accuracy and thus requires development of ring laser gyro-based inertial systems. This paper concentrates on the aided navigation system based on ring laser gyro of 0.01 deg/hr class and GPS - GLONASS to further enhance the capability of system in terms of accuracy. The usage of such systems not only provides accurate results momentarily but it also persists for longer duration with the aid of GPS - GLONASS for applications like aircraft, ship and long range missiles. The system provides accuracy of the level of 1 Nm/hr in pure navigation and 30 m with the aid of GPS - GLONASS. Apart from this, the availability of gyro-compass and baro-inertial algorithms further enhances the system capabilities and made them self dependent to the major extent.Defence Science Journal, 2013, 63(2), pp.131-137, DOI:http://dx.doi.org/10.14429/dsj.63.425

    A study of redundancy management strategy for tetrad strap-down inertial systems

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    Algorithms were developed that attempt to identify which sensor in a tetrad configuration has experienced a step failure. An algorithm is also described that provides a measure of the confidence with which the correct identification was made. Experimental results are presented from real-time tests conducted on a three-axis motion facility utilizing an ortho-skew tetrad strapdown inertial sensor package. The effects of prediction errors and of quantization on correct failure identification are discussed as well as an algorithm for detecting second failures through prediction

    Modeling of Inertial Rate Sensor Errors Using Autoregressive and Moving Average (ARMA) Models

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    In this chapter, a low-cost micro electro mechanical systems (MEMS) gyroscope drift is modeled by time series model, namely, autoregressive-moving-average (ARMA). The optimality of ARMA (2, 1) model is identified by using minimum values of the Akaike information criteria (AIC). In addition, the ARMA model based Sage-Husa adaptive fading Kalman filter algorithm (SHAFKF) is proposed for minimizing the drift and random noise of MEMS gyroscope signal. The suggested algorithm is explained in two stages: (i) an adaptive transitive factor (a1) is introduced into a predicted state error covariance for adaption. (ii) The measurement noise covariance matrix is updated by another transitive factor (a2). The proposed algorithm is applied to MEMS gyroscope signals for reducing the drift and random noise in a static condition at room temperature. The Allan variance (AV) analysis is used to identify and quantify the random noise sources of MEMS gyro signal. The performance of the suggested algorithm is analyzed using AV for static signal. The experimental results demonstrate that the proposed algorithm performs better than CKF and a single transitive factor based adaptive SHFKF algorithm for reducing the drift and random noise in the static condition

    Kernel-based fault diagnosis of inertial sensors using analytical redundancy

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    Kernel methods are able to exploit high-dimensional spaces for representational advantage, while only operating implicitly in such spaces, thus incurring none of the computational cost of doing so. They appear to have the potential to advance the state of the art in control and signal processing applications and are increasingly seeing adoption across these domains. Applications of kernel methods to fault detection and isolation (FDI) have been reported, but few in aerospace research, though they offer a promising way to perform or enhance fault detection. It is mostly in process monitoring, in the chemical processing industry for example, that these techniques have found broader application. This research work explores the use of kernel-based solutions in model-based fault diagnosis for aerospace systems. Specifically, it investigates the application of these techniques to the detection and isolation of IMU/INS sensor faults – a canonical open problem in the aerospace field. Kernel PCA, a kernelised non-linear extension of the well-known principal component analysis (PCA) algorithm, is implemented to tackle IMU fault monitoring. An isolation scheme is extrapolated based on the strong duality known to exist between probably the most widely practiced method of FDI in the aerospace domain – the parity space technique – and linear principal component analysis. The algorithm, termed partial kernel PCA, benefits from the isolation properties of the parity space method as well as the non-linear approximation ability of kernel PCA. Further, a number of unscented non-linear filters for FDI are implemented, equipped with data-driven transition models based on Gaussian processes - a non-parametric Bayesian kernel method. A distributed estimation architecture is proposed, which besides fault diagnosis can contemporaneously perform sensor fusion. It also allows for decoupling faulty sensors from the navigation solution

    Guide to measurement of winds with instrumented aircraft

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    Aircraft measurement techniques are reviewed. Review of past and present applications of instrument aircraft to atmospheric observations is presented. Questions to be answered relative to measuring mean wind profiles as contrasted to turbulence measurements are then addressed. Requirements of instrumentation and accuracy, data reduction, data acquisition, and theoretical and certainty analysis are considered

    Autonomous Navigation for Mars Exploration

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    The autonomous navigation technology uses the multiple sensors to percept and estimate the spatial locations of the aerospace prober or the Mars rover and to guide their motions in the orbit or the Mars surface. In this chapter, the autonomous navigation methods for the Mars exploration are reviewed. First, the current development status of the autonomous navigation technology is summarized. The popular autonomous navigation methods, such as the inertial navigation, the celestial navigation, the visual navigation, and the integrated navigation, are introduced. Second, the application of the autonomous navigation technology for the Mars exploration is presented. The corresponding issues in the Entry Descent and Landing (EDL) phase and the Mars surface roving phase are mainly discussed. Third, some challenges and development trends of the autonomous navigation technology are also addressed

    Reusable Agena study. Volume 2: Technical

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    The application of the existing Agena vehicle as a reusable upper stage for the space shuttle is discussed. The primary objective of the study is to define those changes to the Agena required for it to function in the reusable mode in the 100 percent capture of the NASA-DOD mission model. This 100 percent capture is achieved without use of kick motors or stages by simply increasing the Agena propellant load by using optional strap-on-tanks. The required shuttle support equipment, launch and flight operations techniques, development program, and cost package are also defined
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