A new Gaussian mixture method with exactly exploiting the negative information for GMTI radar tracking in a low-observable environment

Yu, Miao and Gong, Liyun (2018) A new Gaussian mixture method with exactly exploiting the negative information for GMTI radar tracking in a low-observable environment. Aerospace Science and Technology, 80 . pp. 1-10. ISSN 1270-9638

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Abstract

This paper investigates the problem of ground vehicle tracking with a Ground Moving Target Indicator (GMTI) radar. In
practice, the movement of ground vehicles may involve several different manoeuvring types (acceleration,
deceleration, standstill, etc.). Consequently, the GMTI radar may lose measurements when the radial velocity of the
ground vehicle is below a threshold when it stops, i.e. falling into the Doppler blind region. Besides, there will be false
alarms in low-observable environments where there exist high noises interferences. In this paper, we develop a novel
algorithm for the GMTI tracking in a low-observable environment with false alarms while exactly incorporating the
‘negative information’ (i.e., the target is likely to stop when no measurements are recorded) based on the Bayesian
inference framework. For the Bayesian inference implementation, the Gaussian mixture approximation method is
adopted to approximate related distributions, while different filtering algorithms (including both extended Kalman filter
and its generalization for interval-censored measurements) are applied for updating the Gaussian mixture
components. Target state estimation can be directly obtained through the Gaussian mixture model for the GMTI
tracking at every time instance. We have compared the developed method with other state-of-the-art ones and the
simulation results show that the proposed method substantially outperforms the existing methods for the GMTI
tracking problem.

Keywords:GMTI, Algorithm Based Object Recognition and Tracking
Subjects:G Mathematical and Computer Sciences > G120 Applied Mathematics
G Mathematical and Computer Sciences > G340 Statistical Modelling
Divisions:College of Science > School of Computer Science
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ID Code:31019
Deposited On:27 Feb 2018 12:45

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