Attitude determination and calibration using a recursive maximum likelihood-based adaptive Kalman filter

An adaptive Kalman filter design that utilizes recursive maximum likelihood parameter identification is discussed. At the center of this design is the Kalman filter itself, which has the responsibility for attitude determination. At the same time, the identification algorithm is continually identifying the system parameters. The approach is applicable to nonlinear, as well as linear systems. This adaptive Kalman filter design has much potential for real time implementation, especially considering the fast clock speeds, cache memory and internal RAM available today. The recursive maximum likelihood algorithm is discussed in detail, with special attention directed towards its unique matrix formulation. The procedure for using the algorithm is described along with comments on how this algorithm interacts with the Kalman filter

A Study of Adaptive Kalman Filtering for Transfer Alignment

Prior to launching an inertially navigated weapon from the wing of an aircraft, the Inertial Measurement Unit (IMU) of the weapon must be in agreement with the master IMU of the aircraft. In order to correct the IMU of the weapon, it is required that the angles of alignment error between the two units be known. A model for the alignment error can be developed. A Kalman filter can then be used to estimate the angles of alignment error. The modeling of alignment error is complicated by the flexible nature of the aircraft. Since the environment of the aircraft can change dramatically during the alignment process, the model becomes time varying. This further compounds the complexity of the overall model of alignment error. A possible solution to the alignment problem for weapons attached to the wings of an aircraft with a flexible body is proposed. This solution centers around the use of an adaptive Kalman filter. The adaptive Kalman filter can concurrently identify the time varying dynamics of flexing and estimate the angles of alignment error. This capability might substantially simplify the alignment problem. Three adaptive Kalman filtering algorithms were investigated. These algorithms differ only in the method by which they identify the parameters of the system. The relative performance of these algorithms was determined by a simulation. The simulation was based on a simplified dynamic system. The simulation demonstrated that only one of the adaptive Kalman filters provided sufficient performance to be considered for use in the alignment problem. This adaptive Kalman filter identifies the parameters through a stochastic Newton algorithm. The use of this adaptive Kalman filter, along with an appropriately developed model, appear to provide a viable solution to the alignment of inertially guided missiles attached to the wings of an aircraft with a flexible body

Hidden AR Process and Adaptive Kalman Filter

The model of partially observed linear system depending on some unknown parameters is considered. An approximation of the unobserved component is proposed. This approximation is realized in three steps. First an estimator of the method of moments of unknown parameter is constructed. Then this estimator is used for defining the One-step MLE-process and finally the last estimator is substituted to the equations of Kalman filter. The solution of obtained equations provide us the approximation (adaptive Kalman filter). The asymptotic properties of all mentioned estimators and MLE and Bayesian estimators of the unknown parameters are described. The asymptotic efficiency of adaptive filtering is discussed.Comment: 41 page

ADAPTIVE KALMAN FILTER FORECASTING FOR ROAD MAINTAINERS

The article considers the road monitoring weather-stations which collects raw observations that are processed to be able to make the necessary forecasting for future decisions. For the road maintainers those predictions are crucial to make decisions daily. When it comes to the winter season when road safety is very important; however, the road condition is also affected by the snow and icing. In order to improve safety on the road network the road maintainers are trying to use every possible way to be able to provide it. A number of methods have been studied and compared to clarify the parameter required by Kalman filter, which can be improved by making forecasting more accurate. Several road monitoring weather-stations are merged into one region because they are relatively close to each other and it is assumed that there are common conditions in one region that may indicate changes in road conditions. The corresponding algorithms are applied for each region and then compared to each other. Adaptive Kalman filter is generalized in the relevant article in order to have a general understanding of how to correctly apply the approach. The main result of this article is a comparison with the different methods, which are finally compiled in a single table

Automated Rendezvous & Docking Using 3D Vision

The robustness and accuracy of a vision system for motion estimation of a tumbling target satellite are enhanced by an adaptive Kalman filter. This allows a vision-guided robot to complete the grasping of the target even if occlusion occurs during the operation. A complete dynamics model, including aspects of orbital mechanics, is incorporated for accurate estimation. Based on the model, an adaptive Kalman filter is developed that estimates not only the system states but also all the model parameters such as the inertia ratio, center-of-mass, and the rotation of the principal axes of the target satellite. An experiment is conducted by using a robotic arm to move a satellite mockup according to orbital mechanics while the satellite pose is measured by a laser camera system. The measurements are sent to the Kalman filter, which, in turn, drives another robotic arm to grasp the target. The results demonstrate successful grasping even if the vision system is blocked for several seconds

Algorithm of Impact Point Prediction for Intercepting Reentry Vehicles

Intercepting reentry vehicles is difficult because these move nearly at hypersonic speedsthat traditional interceptors cannot match. Counterparallel guidance law was developed fordefending a high speed target that guides the interceptor to intercept the target at a 180° aspectangle. When applying the counterparallel guidance law, it is best to predict the impact pointbefore launch. Estimation and prediction of a reentry vehicle path are the first steps in establishingthe impact point prediction algorithm. Model validation is a major challenge within the overalltrajectory estimation problem. The adaptive Kalman filter, consising of an extended Kalman filterand a recursive input estimator, accurately estimates reentry vehicle trajectory by means of aninput estimator which processes the model validation problem. This investigation presents analgorithm of impact point prediction for a reentry vehicle and an interceptor at an optimal interceptaltitude based on the adaptive Kalman filter. Numerical simulation using a set of data, generatedfrom a complicated model, verifies the accuracy of the proposed algorithm. The algorithm alsoperforms exceptionally well using a set of flight test data. The presented algorithm is effectivein solving the intercept problems

Adaptive Kalman Filter for Actuator Fault Diagnosis

International audienceAn adaptive Kalman filter is proposed in this paper for actuator fault diagnosis in discrete time stochastic time varying systems. By modeling actuator faults as parameter changes, fault diagnosis is performed through joint state-parameter estimation in the considered stochastic framework. Under the classical uniform complete observability-controllability conditions and a persistent excitation condition, the exponential stability of the proposed adaptive Kalman filter is rigorously analyzed. The minimum variance property of the combined state and parameter estimation errors is also demonstrated. Numerical examples are presented to illustrate the performance of the proposed algorithm