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Estimation Theory has been a fundamental tool in the fields like communications; Signal processing, Ocean and Space research and Biomedical Engineering etc. Many problems in Science and Engineering require estimation of the states of system that change over time, using a sequence of noisy measurements made on that system. For example, estimating the states of an arbitrary satellite, estimating the target motion parameters in the ocean environment and so on. State estimation theory is "one of the best mathematical practices to analyse the variants in the states of the system or process" and this approach is used to generate the optimal estimate of the true state of the system. The state estimation processes may be Linear or Nonlinear based on the dynamics of the system and observation models. In this book, the performance is analysed and compared for various nonlinear state space estimation models using decision based filters (Single model filters) for tracking applications.