Differential geometry measures of nonlinearity for filtering with nonlinear dynamic and linear measurement models

被引:7
|
作者
La Scala, Barbara F. [1 ]
Mallick, Mahendra [2 ]
Arulampalam, Sanjeev [3 ]
机构
[1] Univ Melbourne, Dept Elect & Elect Engn, Melbourne, Vic 3010, Australia
[2] Sci Applicat Int Corp, San Diego, CA 92121 USA
[3] Def Sci & Technol Org, Edinburg, SA 5111, Australia
关键词
nonlinear filtering; differential geometry measures of nonlinearity; parameter-effects curvature; intrinsic curvature; bearing-only tracking;
D O I
10.1117/12.735142
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
in our previous work, we presented an algorithm to quantify the degree of nonlinearity of nonlinear filtering problems with linear dynamic models and nonlinear measurement models. A quantitative measure of the degree of nonlinearity was formulated using differential geometry measures of nonlinearity, the parameter-effects curvature and intrinsic curvature. We presented numerical results for a number of practical nonlinear filtering problems of interest such as the bearing-only filtering, ground moving target indicator filtering, and video filtering problems. In this paper, we present an algorithm to compute the degree of nonlinearity of a nonlinear filtering problem with a nonlinear dynamic model and a linear measurement model. This situation arises for the bearing-only filtering problem with modified polar coordinates and log polar coordinates. We present numerical results using simulated data.
引用
收藏
页数:12
相关论文
共 50 条
  • [1] Differential geometry measures of nonlinearity for the video filtering problem
    Mallick, Mahendra
    La Scala, Barbara F.
    SIGNAL AND DATA PROCESSING OF SMALL TARGETS 2008, 2008, 6969
  • [2] Differential geometry measures of nonlinearity for ground moving target indicator (GMTI) filtering
    Mallick, M
    La Scala, BF
    2005 7th International Conference on Information Fusion (FUSION), Vols 1 and 2, 2005, : 219 - 226
  • [3] On System Gains, Nonlinearity Measures, and Linear. Models for Nonlinear Systems
    Schweickhardt, Tobias
    Allgower, Frank
    IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2009, 54 (01) : 62 - 78
  • [4] Linear or Nonlinear? Comparing Measures of Nonlinearity
    Tahiyat, Malik M.
    Choudhury, M. A. A. Shoukat
    12TH INTERNATIONAL SYMPOSIUM ON PROCESS SYSTEMS ENGINEERING (PSE) AND 25TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING (ESCAPE), PT B, 2015, 37 : 1697 - 1702
  • [5] Differential geometry measures of nonlinearity for the video tracking problem
    Mallick, Mahendra
    La Scala, Barbara F.
    SIGNAL PROCESSING, SENSOR FUSION, AND TARGET RECOGNITION XV, 2006, 6235
  • [6] Linear control of nonlinear systems based on nonlinearity measures
    Schweickhardt, Tobias
    Allgoewer, Frank
    JOURNAL OF PROCESS CONTROL, 2007, 17 (03) : 273 - 284
  • [7] Measurement and identification of nonlinear systems consisting of linear dynamic blocks and one static nonlinearity
    Vandersteen, G
    Schoukens, J
    IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1999, 44 (06) : 1266 - 1271
  • [8] Differential geometry measures of nonlinearity for the bearing-only tracking problem
    Mallick, M
    La Scala, BF
    Arulampalam, MS
    SIGNAL PROCESSING, SENSOR FUSION, AND TARGET RECOGNITION XIV, 2005, 5809 : 288 - 300
  • [9] Measurement and identification of nonlinear systems consisting out of linear dynamic blocks and one static nonlinearity
    Vandersteen, G
    Schoukens, J
    IMTC/97 - IEEE INSTRUMENTATION & MEASUREMENT TECHNOLOGY CONFERENCE: SENSING, PROCESSING, NETWORKING, PROCEEDINGS VOLS 1 AND 2, 1997, : 853 - 858
  • [10] Approximate Conditional Mean Particle Filtering for Linear/Nonlinear Dynamic State Space Models
    Yee, Derek
    Reilly, James P.
    Kirubarajan, Thia
    Punithakumar, K.
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2008, 56 (12) : 5790 - 5803