Seyed Alireza Banani and Bita Imam
Maneuvering Target Tracking, Particle Filter, Glint Noise, Kinematics
In this paper a comprehensive algorithm is presented to alleviate the undesired effects of degrading factors on optimality of the Kalman filter for tracking maneuvering targets in 3-dimensional (3D) space. These effects are: target maneuvering, observed glint noise distribution, colored noise spectrum, unknown noise parameters, and nonlinearity of the system. Particle filter is used as a nonlinear state estimator to deal with the nonlinearities of the observation equations. To identify the dynamics of the maneuvering targets, we also have proposed a new hard decision switching algorithm to be substituted for the conventional interacting multiple model (IMM) approach. The method is strictly causal and can be implemented for an online tracking system. The algorithm performance has been verified by illustrating some simulation results.
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