Abstract

In this paper, we extend the multiple transition mode track- before-detect (TBD) algorithm to track multiple low observable targets in compound Gaussian sea clutter. The proposed TBD framework uses the un-thresholded fast time radar measurements to track multiple targets in low signal-to-clutter ratios (SCRs). The TBD is implemented using particle filtering (PF), and we derive the generalized likelihood ratio needed to update the particle weights. The maximum likelihood estimate of the texture and the covariance matrix of the speckle are also derived and implemented using a fixed point algorithm. The tracking performance of the proposed algorithm is investigated using three low observable targets that enter and leave the field of view (FOV) at different time steps and under varying environmental conditions.

Original languageEnglish (US)
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2539-2543
Number of pages5
Volume2015-August
ISBN (Print)9781467369978
DOIs
StatePublished - Aug 4 2015
Event40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Brisbane, Australia
Duration: Apr 19 2014Apr 24 2014

Other

Other40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015
CountryAustralia
CityBrisbane
Period4/19/144/24/14

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Multiple target track-before-detect in compound Gaussian clutter'. Together they form a unique fingerprint.

  • Cite this

    Ebenezer, S. P., & Papandreou-Suppappola, A. (2015). Multiple target track-before-detect in compound Gaussian clutter. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (Vol. 2015-August, pp. 2539-2543). [7178429] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2015.7178429