Targets tracked by radar may exhibit mean-reverting motion, such as motion along a nominal trajectory or weaving behavior. Conventional target tracking models typically do not account for this characteristic, which limits estimation and prediction performance. This study investigates the mixed Ornstein–Uhlenbeck model, also known as the second-order continuous auto-regressive model, as a framework for representing mean-reverting motion to model targets that follow a nominal trajectory or to model targets that display oscillatory motion. The model contains two unknown transition parameters per dimension, which are estimated offline by maximum likelihood estimation and online by joint estimation and a particle filter. Results show that joint estimation provides the best performance in both state and parameter estimation. Joint estimation is applied to real-world weaving motion from jet skis, water scooters, and RHIBs. The proposed approach improves position estimates and, in particular, velocity estimates, while also providing accurate trajectory predictions. These findings demonstrate that the mixed Ornstein–Uhlenbeck model can significantly enhance target-state estimation and prediction for mean-reverting motion.

This thesis details the design of a selection operator used in a Genetic Algorithm. The Genetic Algorithm is used for loudspeaker filter design of three way loudspeakers for which tournament selection was chosen as selection operator. A methodology is proposed and used to tune the parameters of tournament selection, which is based on diversity and fitness of the population. Besides basic tournament selection, two new adaptive selection operators based on tournament selection are proposed to improve its functionality. The first adaptive selection operator uses noise proportional to the fitness variance of the population to improve the efficiency of the genetic algorithm. The second adaptive selection operator uses a convergence stage to speed up the convergence towards the optimal filter. After the presented tuning process in this thesis, the latter adaptive selection operator was found to perform better. The optimal selection operator and parameters found in this thesis will not translate to every application, because they heavily depend on the design and the application of the genetic algorithm. However, the presented comparison of selection operators, the provided performance metrics and design methodology can still be used to guide the choice and the tuning process of a selection operator used in any genetic algorithm.