Sequential bearings-only-tracking initiation with particle filtering method.
The tracking initiation problem is examined in the context of autonomous bearings-only-tracking (BOT) of a single appearing/disappearing target in the presence of clutter measurements. In general, this problem suffers from a combinatorial explosion in the number of potential tracks resulted from the...
| Publicado en: | Scientific World Journal pp. 1 - 8 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=95413201&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 95413201 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1537744X 1BX5 jtl: Scientific World Journal issn: 1537744X maglogo: N pubinfo: dt: 2013 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 95413201 95413201 NLM24453865 95413201 10.1155/2013/489121 NLM24453865 95413201 ppf: 1 ppct: 7 formats: tig: atl: Sequential bearings-only-tracking initiation with particle filtering method. aug: au: Bin Liu Chengpeng Hao Liu, Bin Hao, Chengpeng affil: School of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, China sug: subj: Mathematics Methods Uncertainty Models, Theoretical Statistics Signal Processing, Computer Assisted Probability Comparative Studies Multicenter Studies Evaluation Research Validation Studies Questionnaires ab: The tracking initiation problem is examined in the context of autonomous bearings-only-tracking (BOT) of a single appearing/disappearing target in the presence of clutter measurements. In general, this problem suffers from a combinatorial explosion in the number of potential tracks resulted from the uncertainty in the linkage between the target and the measurement (a.k.a the data association problem). In addition, the nonlinear measurements lead to a non-Gaussian posterior probability density function (pdf) in the optimal Bayesian sequential estimation framework. The consequence of this nonlinear/non-Gaussian context is the absence of a closed-form solution. This paper models the linkage uncertainty and the nonlinear/non-Gaussian estimation problem jointly with solid Bayesian formalism. A particle filtering (PF) algorithm is derived for estimating the model's parameters in a sequential manner. Numerical results show that the proposed solution provides a significant benefit over the most commonly used methods, IPDA and IMMPDA. The posterior Cramér-Rao bounds are also involved for performance evaluation. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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