Software projects are required to be tracked during their execution for controlling them. According to state-space approach, the tracking technique consists of software project state transition equation and software project status measurement equation. A key factor in tracking software projects is to represent the project with uncertainty involved in the parameters. Traditional and hybrid software project tracking technique is designed with state space approach and simulated using discrete event simulation in plan-space and execution-space. The uncertainty considered here is epistemological and is modeled as a normal distribution using an approximation method. The initial state of the project in execution-space also has an uncertainty associated with it. The project tracking technique consists of project state transition equation and project status measurement equation, in plan-space and is formulated with Monte Carlo method in execution-space. The project status is derived using project measurement equation as a function of project state. The project state is derived using project state transition equation. The software product is developed iteratively and incrementally. With Monte Carlo simulation runs, simulation result shows the uncertainty propagation iteration-wise, both individually and totally; and the effect of uncertainty on project status is shown by showing project status in execution-space and plan-space. Besides, the project completion somewhere during the last iteration is shown with simulation.

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This paper concerns the application of an iterated extended risk sensitive filter (IERSF) to target tracking problems. The relative merits of IERSF vis-à-vis the extended risk sensitive filter (ERSF) for a bearingsonly tracking problem using root mean square error (RMSE) and robustness with uncertainty in initial condition are explored. An ERSF weakness, specifically an accumulation error in the computation of innovation steps due to approximating nonlinear functions at a recently available prior estimate, is presented. By using the IERSF with proper tuning of risk factor and local iteration, the filtering divergence may be overcome, and a stable, robust, and unbiased estimation is satisfactorily obtained. With numerical simulation results, the tracking performance of IERSF is compared with the performance of ERSF and extended Kalman filter. The IERSF results in reduced estimation error without much of an increase in burden of the associated computational algorithm. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).

The lithium-sulfur (Li-S) batteries are high energy storage systems that can be used for electric grid and solar power air vehicle applications. Such applications require an accurate state of charge (SOC) estimator to control and optimize battery performance. Modelling and estimation of discharging Li-S are highly challenging than other batteries as the discharge voltage of Li-S batteries has highly nonlinear and typical characteristics than the Lithium-Ion batteries. For Li-S battery SOC estimation, literature has proposed filters and machine learning techniques, but no literature on sliding mode observer (SMO). This paper presents the SMO for discharging Li-S SOC estimation and compares it to the extended Kalman filter (EKF). Both estimators use a first-order equivalent circuit network (ECN) model of Li-S cell parameters given in the literature. The performance of such ECN model based SOC estimators influenced by the Q-uncertainty, which is a perturbation in the form of process noise state-space model. Therefore, this work studies an optimal trade-off characteristic of SMO and EKF over the Q-uncertainty. With constant and mixed-amplitude pulse load current sequences, numerical simulation has performed. Simulation results illustrate that the SMO is optimal, converges to the true SOC than the EKF when the perturbation increased.

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