Initial data truncation for multivariate output of discrete-event simulations using the Kalman filter

The focus of this research is to provide methods for generating precise parameter estimates in the face of potentially significant parameter variations such as system component failures. The standard multiple model adaptive estimation (MMAE) algorithm uses a bank of Kalman filters, each based on a different model of the system. Parameter discretization within the MMAE refers to selection of the parameter values assumed by the elemental Kalman filters, and dynamically redeclaring such discretization yields a moving-bank MMAE. An online parameter discretization method is developed based on the probabilities associated with the generalized Chi-squared random variables formed by residual information from the elemental Kalman filters within the MMAE. This algorithm is validated through computer simulation of an aircraft navigation system subjected to interference/jamming while attempting a successful precision landing of the aircraft.

A maneuver target interception algorithm based on multiple model adaptive estimator (MMAE) is researched and a modified differential game guidance law is given. The efficiency of this simplified MMAE algorithm lies mainly in the exploitation of the special structure of the evader maneuver command hypothesis space to reduce the number of MMAE elemental filters running in parallel and to realize the reduction of the computational load. To realize the reduction of the miss distance resulting from the estimation delay, the modified differential game guidance law increases the lateral acceleration command for small values of time-to-go of the pursuer to the maximum available value by enhancing the gain of the command. The Monte Carlo simulations are carried out for the homing performance comparison and the results demonstrate the superiority and effective of the proposed methods.

The mean and covariance of a Kalman filter residual are computed for specific cases in which the Kalman filter model differs from a linear model that accurately represents the true system (the truth model). Multiple model adaptive estimation (MMAE) uses a bank of Kalman filters, each with a different internal model, and a hypothesis testing algorithm that uses the residuals from this bank of Kalman filters to estimate the true system model. At most, only one Kalman filter's model will exactly match the truth model and will produce a residual whose mean and standard deviation have already been analyzed. All of the other filters use internal models that mismodel the true system. We compute the effects of a mismodeled input matrix, output matrix, and state transition matrix on these residuals. The computed mean and covariance are compared to simulation results of flight control failures that correspond to mismodeled input matrices and output matrices.

Multiple model adaptive estimation (MMAE) is a method of estimating unknown system parameters by modeling different discrete parameter values in several filters that are run in parallel and compared. The parameters for this research are failure status conditions associated with flight control actuators and sensors on the LAMBDA unmanned research vehicle, an experimental aircraft. Six actuator failures and eight sensor failures are modeled, along with the fully functional aircraft, in fifteen elemental Kalman filters. These filters propagate and update their own aircraft state estimates in real time. A probability computation representing the likelihood of each elemental filter's match to the true condition of the aircraft is used to generate relative probabilities for each filter's hypothesis. The MMAE algorithm is extended for the identification of dual failures through the use of a hierarchical structure of filter banks. Aircraft state excitation is required for effective MMAE performance. Sinusoidal dither signals are applied to the command inputs of a flight control system which controls pitch rate, roll rate, and sideslip angle. >

A Multiple Model Adaptive Wave Filter for Dynamic Ship Positioning

n/a

In this paper we apply the infinite-dimensional sampled-data Kalman filter (ISKF) to a system characterized by the stochastic heat equation for the purpose of estimating the temperature distribution along a slender (one-dimensional) cylindrical rod using a simple linear measurement model. The key to applying the ISKF is the development of an essentially equivalent finite-dimensional discrete-time model from an infinite-dimensional continuous-time dynamics model. In addition to estimating the temperature of the rod, we employ a bank of elemental filters via the multiple model adaptive estimation (MMAE) technique to estimate unknown model parameters such as the thermal diffusivity constant of the slender cylindrical rod.

In this paper, we model the generalized power system state estimation problem as a hybrid estimation problem in which the network configuration and voltage phasor states are treated as discrete and continuous states, respectively. We propose the Multiple model Adaptive Power system State Estimator (MAPSE), which implements a multiple model adaptive estimator with a bank of Unscented Kalman Filters (UKFs) to provide a running estimate of the voltage phasor states and a probability distribution for the candidate network configurations. The MAPSE updates the network configuration probability distribution by applying a Bayesian recursion to the measurement residuals from each of the UKFs at each timestep. Simulation on a modified version of the 33 node distribution network demonstrates successful network configuration detection and accurate voltage phasor state estimation.

: We apply multiple model adaptive estimation (MMAE), a proven method of system identification widely used in engineering applications, to the problem of determining Bayesian probability distributions of the final cost and completion time of on-going research and development (R&D) programs, conditioned on actual cost of w9rk performed (ACWP) data. Modeling cumulative expenditures with Rayleigh distributions, we produce graphs of the results that give useful assessments of cost and schedule risks. The procedure is implemented in a convenient computer program. We give three examples of its application to actual data, and results of a Monte Carlo analysis verify the method. (KAR) P. 1

The problem of measuring the availability of generation units in electric power systems has been addressed in the literature by using a set of analytical equations and Monte Carlo Simulation (MCS). MCS, as a powerful simulation tool, is much easier to use than analytical approaches in measuring the availability of large applications. However, the simulated process using MCS entails deducing the operating state sequences for each component along the simulated time followed by combining all sequences for all components in order to deduce system availability. This simulated process is both lengthy and time-consuming. This paper introduces a new method to measure the availability of generation units and optimize their utilization using Discrete Event Simulation (DES) model. The experimental result of a real case study illustrates the efficiency of using DES.

The aim of this study was to verify the advanced inhomogeneous dose distribution produced by a volumetric arc therapy technique (RapidArc™) using 3D gel measurements and Monte Carlo (MC) simulations. The TPS (treatment planning system)-calculated dose distribution was compared with gel measurements and MC simulations, thus investigating any discrepancy between the planned dose delivery and the actual delivery. Additionally, the reproducibility of the delivery was investigated using repeated gel measurements. A prostate treatment plan was delivered to a 1.3 liter nPAG gel phantom using one single arc rotation and a target dose of 3.3 Gy. Magnetic resonance imaging of the gel was carried out using a 1.5 T scanner. The MC dose distributions were calculated using the VIMC-Arc code. The relative absorbed dose differences were calculated voxel-by-voxel, within the volume enclosed by the 90% isodose surface (VOI90), for the TPS versus gel and TPS versus MC. The differences between the verification methods, MC versus gel, and between two repeated gel measurements were investigated in the same way. For all volume comparisons, the mean value was within 1% and the standard deviation of the differences was within 2.5% (1SD). A 3D gamma analysis between the dose matrices were carried out using gamma criteria 3%/3 mm and 5%/5 mm (% dose difference and mm distance to agreement) within the volume enclosed by the 50% isodose surface (VOI50) and the 90% isodose surface (VOI90), respectively. All comparisons resulted in very high pass rates. More than 95% of the TPS points were within 3%/3 mm of both the gel measurement and MC simulation, both inside VOI50 and VOI90. Additionally, the repeated gel measurements showed excellent consistency, indicating reproducible delivery. Using MC simulations and gel measurements, this verification study successfully demonstrated that the RapidArc™ plan was both accurately calculated and delivered as planned.

Model-based filter banks and corresponding subband coding systems: Multirate state-space approach

This paper discusses the convenience of predicting, quantitatively, time-dependent reliability and availability levels associated with most building or civil engineering structures. Then, the paper reviews different approaches to these problems and proposes the use of discrete-event simulation as the most realistic way to deal with them, specially during the design stage. The paper also reviews previous work on the use of both Monte Carlo simulation and discrete-event simulation in this area and shows how discrete-event simulation, in particular, could be employed to solve uncertainty in time-dependent structural reliability problems. Finally, a case study is developed to illustrate some of the concepts previously covered in the paper.

The Monte Carlo and discrete-event simulation code associated with the Simulation 101 pre-conference workshop (offered at the 2006, 2007, and 2008 Winter Simulation Conferences) is available in both C and R. This paper begins with general instructions for downloading, compiling, and executing the software. This is followed by detailed explanations of two programs that are representative of the software suite: craps uses Monte Carlo simulation to estimate the probability of winning the dice game Craps, and ssq2 uses discrete-event simulation to estimate several measures of performance associated with a single-server queue.

The paper discusses the problem of aircraft airspeed estimation from ground speed measurement affected by the unmeasurable wind speed. The estimation has to be obtained along any flight trajectory where linearized dynamics and the associated trim conditions are available. Under these assumptions one feasible approach to this estimation task is to apply multiple model (MM) estimation based on a bank of robust Kalman filters (RKFs). The unmeasurable wind effect can be taken into consideration by using an unknown input observer running simultaneously with the RKF. The paper focuses on the implementation issues and investigates the effect of state and measurement noises on the resulting state and unknown input (wind speed) estimates. The bank of steady state RKFs are designed assuming LTI dynamics at the given trim points in the minimum variance unbiased estimation (MVUE) framework. In the undertaken simulation experiments the aircraft is represented first by its LTI, then by its LPV model.