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This paper discusses the application of four nonlinear estimation techniques on two benchmark target tracking problems. The first problem is a generic air traffic control (ATC) scenario, which involves nonlinear system equations with linear measurements. The second study is a classical ground surveillance problem, where a moving airborne platform with a sensor is used to track a moving target. The tracking scenario is set in two dimensions, with the measurement providing nonlinear bearing-only observations. These two target tracking problems provide a good benchmark for comparing the following nonlinear estimation techniques: the common extended and unscented Kalman filters (EKF/UKF), the particle filter (PF), and the relatively new smooth variable structure filter (SVSF). The results of applying the SVSF on the two target tracking problems demonstrate its stability and robustness. Both of these attributes make use of the SVSF advantageous over other popular methods. The filters performances are quantified in terms of robustness, resilience to poor initial conditions and measurement outliers, and tracking accuracy and computational complexity. The purpose of this paper is to demonstrate the effectiveness of applying the SVSF on nonlinear target tracking problems, which in the past have typically been solved by Kalman or particle filters.

This article discusses the application of the smooth variable structure filter (SVSF) on a target tracking problem. The SVSF is a relatively new predictor-corrector method used for state and parameter estimation. It is a sliding mode estimator, where gain switching is used to ensure that the estimates converge to true state values. An internal model of the system, either linear or nonlinear, is used to predict an a priori state estimate. A corrective term is then applied to calculate the a posteriori state estimate, and the estimation process is repeated iteratively. The results of applying this filter on a target tracking problem demonstrate its stability and robustness. Both of these attributes make using the SVSF advantageous over the well-known Kalman and extended Kalman filters. The performances of these algorithms are quantified in terms of robustness, resilience to poor initial conditions and measurement outliers, tracking accuracy and computational complexity.

The Severe Acute Respiratory Syndrome COVID-19 virus (SARS-CoV-2) has had enormous impacts, indicating need for non-pharmaceutical interventions (NPIs) using Artificial Intelligence (AI) modeling. Investigation of AI models and statistical models provides important insights within the province of Ontario as a case study application using patients' physiological conditions, symptoms, and demographic information from datasets from Public Health Ontario (PHO) and the Public Health Agency of Canada (PHAC). The findings using XGBoost provide an accuracy of 0.9056 for PHO, and 0.935 for the PHAC datasets. Age is demonstrated to be the most important variable with the next two variables being Hospitalization and Occupation. Further, AI models demonstrate identify the importance of improved medical practice which evolved over the six months in treating COVID-19 virus during the pandemic, and that age is absolutely now the key factor, with much lower importance of other variables that were important to mortality near the beginning of the pandemic. An XGBoost model is shown to be fairly accurate when the training dataset surpasses 1000 cases, indicating that AI has definite potential to be a useful tool in the fight against COVID-19 even when caseload numbers needed for effective utilization of AI model are not large.

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