Abstract
This study establishes a target motion model and an observation model under the condition of colored noise by using the Kalman filter based on an improved IMM (interactive multiple model) for maneuvering target tracking. To improve the overall performance of IMM algorithm, we proposed to combine the CV (constant velocity) and CA (constant acceleration) models with the statistical model, in which its acceleration extremum is not fixed. Since the system model information is implicit in the current measurement, the Markov transition probability is computed online and real-timely, so as to obtain more accurate a posterior estimation and improve the model fusion accuracy. Monte Carlo simulations are carried out for the experiments and the results reveal that the proposed algorithm can get better performance in comparison with traditional IMM which adopts the statistical model and CV-CA models.
Highlights
In military and civil fields, such as missile and aero-traffic management, reliable and accurate tracking is always the main purpose of target tracking systems
In order to verify the effectiveness of the proposed algorithm in this study, the improved Interactive Multiple Model (IMM) algorithm is compared with the classical "current" statistical model for the maneuvering target tracking as can be seen from Fig. 2 to 4
To overcome the acceleration extremum preset dependence problem and its fixed acceleration deficiency in the "current" statistical model, an improved interacting multiple model algorithms for maneuvering target tracking is presented in this study
Summary
In military and civil fields, such as missile and aero-traffic management, reliable and accurate tracking is always the main purpose of target tracking systems. Due to the system model information that is implicit in the current measurement, the Markov transition probability is computed online and real-timely, so that more accurate a posterior estimation and model fusion accuracy can be obtained in this way.
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