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Monte Carlo analysis refers to the process of evaluating the actual navigation software in a simulation of the application The simulation of the actual process starting from randomized input conditions is evaluated over expected trajectories with additive stochastic process inputs There are two main motivations for Monte Carlo analysis The rst motivation is to test the navigation software in a controlled environment prior to eld testing To allow analysis of computation time and latency related issues, the analysis may run the navigation code on the application computer with hardware connections to other computational hardware that implements the application simulation This is referred to as hardware-in-the-loop testing This testing may detect sign errors, detect latency or synchronization issues, or highlight the importance of previously unmodeled states or nonlinearities A second motivation is to perform statistical analysis of the navigation system actual performance in comparison with the lter estimate of its statistical performance (ie, P) In addition, the analyst is interested in determining whether the mean estimation error is unbiased For statistically valid results, numerous simulations calculating the lter estimation error will be required The error statistics will have to be calculated over the ensemble of simulations The accuracy of the statistical results determined by either Monte Carlo or covariance analysis techniques is dependent on the accuracy of the reality or truth model that is assumed

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[Solved] Convert PDF to TIFF using C# .NET 2.0 - CodeProject
Free source code and tutorials for Software developers and Architects.; Updated: 9 Jan 2014.

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In the design of a state estimation system, there is usually a con icting set of desires Better performance may be achieved by either using more or better sensors, or by appending more error states to the model; however, either approach may increase the system cost Covariance analysis techniques provide a systematic approach to evaluating alternative system implementations When performing a covariance analysis as described in Section 61, the result is the covariance of the output error due to all error sources For an analysis of system design tradeo s, it is usually more useful to be able to calculate the output error variance due to each source independently The output error variance due to each error source allows the

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