Bearing-only 2D maneuvering target tracking using smart interacting multiple model filter

In this paper, along with reviewing and analyzing the maneuvering target tracking model, the multiple-model Interacting Multiple Model algorithm is used to solve the maneuvering target tracking problem in the presence of measurement noise. In addition, for reliable estimation another method is proposed, which uses higher-order Markov models to describe the system behavior precisely. It means that the previous two models are used to predict the next model of target in order to present a more better algorithm than the first-order IMM algorithm. In this approach, two models are employed. For each model Extended Kalman Filter is used to randomly estimate states of the target. The final estimation of the maneuvering target consists of these two models. Final target estimation is obtained from a weighted sum of all state estimates. In addition, target tracking is presented with two modes for noise measurement: one is an adaptive method and the other is an assignment of an integer amount considering problem circumstances. In the end, the results are compared. (C)& nbsp;2022 Elsevier Inc. All rights reserved.

Automated summary

This paper reviews and analyzes the maneuvering target tracking model and proposes an improved algorithm for accurately estimating the target's state in the presence of measurement noise. The proposed method uses the multiple-model Interacting Multiple Model algorithm and introduces higher-order Markov models to describe the system behavior. The results show that the algorithm performs well in target tracking.