A Partially Uniform Target Birth Model for Gaussian Mixture PHD/CPHD Filtering

被引：51
|
作者：
Beard, Michael [1 ]
Vo, Ba Tuong [2 ]
Vo, Ba-Ngu [2 ]
Arulampalam, Sanjeev [1 ]
机构：
[1] Def Sci & Technol Org, Rockingham Dc, WA 6967, Australia
[2] Curtin Univ, Dept Elect & Comp Engn, Bentley, WA, Australia
来源：
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS | 2013年 / 49卷 / 04期
基金：
澳大利亚研究理事会;
关键词：
HYPOTHESIS DENSITY FILTER; MOTION ANALYSIS; PHD FILTERS; OBSERVABILITY; PERFORMANCE;
D O I：
10.1109/TAES.2013.6621859
中图分类号：
V [航空、航天];
学科分类号：
08 ; 0825 ;
摘要：
The conventional GMPHD/CPHD filters require the PHD for target births to be a Gaussian mixture (GM), which is potentially inefficient because careful selection of the mixture parameters may be required to ensure good performance. Here we present approximations which allow part of the birth PHD to be uniformly distributed, obviating the need to use a large GM to model target births. The benefits of this approach are demonstrated by simulations on a bearings-only filtering scenario.
引用
收藏
页码：2835 / 2844
页数：10
相关论文
共 50 条
  • [31] HP Trend Filtering Using Gaussian Mixture Model Weighted Heuristic
    Sayfullina, Luiza
    Westerlund, Magnus
    Bjork, Kaj-Mikael
    Toivonen, Hannu T.
    [J]. 2014 IEEE 26TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI), 2014, : 989 - 996
  • [32] Collaborative Filtering Based on Gaussian Mixture Model and Improved Jaccard Similarity
    Yan, Hangyu
    Tang, Yan
    [J]. IEEE ACCESS, 2019, 7 (118690-118701) : 118690 - 118701
  • [33] Crack Detection Based on Gaussian Mixture Model using Image Filtering
    Ogawa, Shujiro
    Matsushima, Kousuke
    Takahashi, Osamu
    [J]. 2019 INTERNATIONAL SYMPOSIUM ON ELECTRICAL AND ELECTRONICS ENGINEERING (ISEE 2019), 2019, : 79 - 84
  • [34] Moving Target Detection Algorithm Based on Gaussian Mixture Model
    Wang, Zhihua
    Kai, Du
    Zhang, Xiandong
    [J]. FIFTH INTERNATIONAL CONFERENCE ON DIGITAL IMAGE PROCESSING (ICDIP 2013), 2013, 8878
  • [35] Target Detection Algorithm Based on Improved Gaussian Mixture Model
    Wang, Xiaomeng
    Zhao, Dequn
    Sun, Guangmin
    Liu, Xingwang
    Wu, Yanli
    [J]. PROCEEDINGS OF THE 2015 2ND INTERNATIONAL CONFERENCE ON ELECTRICAL, COMPUTER ENGINEERING AND ELECTRONICS (ICECEE 2015), 2015, 24 : 846 - 850
  • [36] Multi-target tracking algorithm based on kernel density estimation Gaussian mixture PHD filter
    Zhou W.-D.
    Zhang H.-B.
    Qiao X.-W.
    [J]. Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, 2011, 33 (09): : 1932 - 1936
  • [37] Gaussian mixture PHD filter for multi-sensor multi-target tracking with registration errors
    Li, Wenling
    Jia, Yingmin
    Du, Junping
    Yu, Fashan
    [J]. SIGNAL PROCESSING, 2013, 93 (01) : 86 - 99
  • [38] Background Subtraction using Spatial Mixture of Gaussian Model with Dynamic Shadow Filtering
    Rumaksari, Atyanta N.
    Sumpeno, Surya
    Wibawa, Adhi D.
    [J]. 2017 INTERNATIONAL SEMINAR ON INTELLIGENT TECHNOLOGY AND ITS APPLICATIONS (ISITIA), 2017, : 296 - 301
  • [39] Gaussian-Student's t mixture distribution PHD robust filtering algorithm based on variational Bayesian inference
    Hu Z.
    Yang L.
    Hu Y.
    Yang S.
    [J]. High Technology Letters, 2022, 28 (02) : 181 - 189
  • [40] Gaussian-Student's t mixture distribution PHD robust filtering algorithm based on variational Bayesian inference
    胡振涛
    YANG Linlin
    HU Yumei
    YANG Shibo
    [J]. High Technology Letters, 2022, (02) : 181 - 189
12345