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Title: An enhanced particle filtering method for GMTI radar tracking
Authors: Yu, Miao
Liu, Cunjia
Li, Baibing
Chen, Wen-Hua
Keywords: Particle filtering
Target tracking
Doppler blind region
Generalized EKF
Issue Date: 2016
Publisher: © The Authors. Published by the IEEE
Citation: YU, M. ...et al., 2016. An enhanced particle filtering method for GMTI radar tracking. IEEE Transactions on Aerospace and Electronic Systems, 52(3), pp.1408-1420.
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, i.e. falling into the Doppler blind region. In this paper, to incorporate the information gathered from normal measurements and knowledge on the Doppler blindness constraint, we develop an enhanced particle filtering method for which the importance distributions are inspired by a recent noise related doppler blind (NRDB) filtering algorithm for GMTI tracking. Specifically, when constructing the importance distributions, the proposed particle filter takes the advantages of the efficient NRDB algorithm by applying the extended Kalman filter and its generalization for interval-censored measurements. In addition, the linearization and Gaussian approximations in the NRDB algorithm are corrected by the weighting process of the developed filtering method to achieve a more accurate GMTI tracking performance. The simulation results show that the proposed method substantially outperforms the existing methods for the GMTI tracking problem.
Description: This is an Open Access Article. It is published by IEEE under the Creative Commons Attribution 3.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/3.0/
Sponsor: This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) Grant number EP/J011525/1. Dr. Miao Yu’s involvement was supported by the EPSRC Grant number EP/K014307/1 and the MOD University Defence Research Collaboration in Signal Processing.
Version: Published version
DOI: 10.1109/TAES.2016.140561
URI: https://dspace.lboro.ac.uk/2134/20314
Publisher Link: http://dx.doi.org/10.1109/TAES.2016.140561
ISSN: 1557-9603
Appears in Collections:Published Articles (Aeronautical and Automotive Engineering)

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