Switched Kalman filter‐interacting multiple model algorithm based on optimal autoregressive model for manoeuvring target tracking
Author(s) -
Jin Biao,
Jiu Bo,
Su Tao,
Liu Hongwei,
Liu Gaofeng
Publication year - 2015
Publication title -
iet radar, sonar and navigation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.489
H-Index - 82
eISSN - 1751-8792
pISSN - 1751-8784
DOI - 10.1049/iet-rsn.2014.0142
Subject(s) - autoregressive model , kalman filter , tracking (education) , computer science , extended kalman filter , algorithm , fast kalman filter , moving horizon estimation , invariant extended kalman filter , control theory (sociology) , artificial intelligence , mathematics , econometrics , psychology , pedagogy , control (management)
A manoeuvring target tracking algorithm based on the autoregressive (AR) model is proposed. First, the AR model is incorporated into the Kalman filter (KF) for target tracking. The closed‐form solution of the AR model coefficients is obtained by minimising the mean‐square tracking error, and subject to the polynomial constraint of target motion. Then, based on the AR model, the proposed algorithm is constructed by combining the KF with the interacting multiple model (IMM) filter, coupled with the proposed detection schemes for manoeuvre occurrence and termination, as well as for switching initialisation. Simulations are performed to demonstrate the effectiveness of the AR model, and the proposed algorithm is compared with the IMM filter and variable‐dimension filter in the manoeuvring scenario.
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