67
Views
0
CrossRef citations to date
0
Altmetric
Articles

Recursive implementation of GLRT-based radar target detection

&
Pages 169-184 | Received 01 Aug 2014, Accepted 04 Nov 2014, Published online: 18 Dec 2014
 

Abstract

We consider recursive implementation of the natural frequency-based radar target detection for an augmented data vector. In the previous study, it was shown that, the probability of detection can be calculated using the probability density function (PDF) of the non-central chi-square distribution and that non-centrality of the chi-square distribution is dependent on the eigenvectors of a matrix. The essential idea in this paper is that the eigenvectors of the augmented matrix can be recursively calculated without computationally intensive eigendecomposition. To do that, we make use of how the QR factorization of the row-augmented matrix can be updated from the QR factorization of the original matrix to get the probability of detection recursively. The recursive formulation is validated by comparing the detection performance using the recursive method with that using non-recursive method.

Additional information

Funding

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2A10012245).

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 561.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.