Extended Kalman filtering and Interacting Multiple Model for tracking maneuvering targets in sensor netwotrks

This paper consider the nonlinear state estimate problem for tracking maneuvering targets. Two methods are introduced to overcome the difficulty of non-linear model. The first method uses interacting multiple model (IMM) which includes 2, 3, 4 and 10 models. These models are linear, each model stands for an operation point of the nonlinear model. Two model sets are designed using equal-distance model-set design for each. The effect of increasing the number of models, separation between them and noise effect on the accuracy is introduced. The second method uses Second order Extended Kalman Filter (EKF2) which is a single nonlinear filter. Both methods are evaluated by simulation using two scenarios. A comparison between them is evaluated by computing their accuracy, change of operation range and computational complexity (computational time) at different measurement noise. Based on this study for small range of variation of nonlinear parameter, and low noise the EKF2 introduced quick and accurate tracking. For a large range of nonlinearity and good separation between models of IMM, at minimum noise large and small numbers of models of IMM introduced best accuracy but as the noise increase large number keeps higher accuracy until the large numbers and small numbers of IMM introduced bad accuracy. At high noise optimizing number of models and separation between model sets, IMM introduces better accuracy.