Data compression using SVD and Fisher information for radar emitter location

  • Authors:

  • Mark L. Fowler;Mo Chen;J. Andrew Johnson;Zhen Zhou

  • Affiliations:

  • Department of Electrical and Computer Engineering, State University of New York at Binghamton, Binghamton, NY 13902, United States;Department of Electrical and Computer Engineering, State University of New York at Binghamton, Binghamton, NY 13902, United States;Department of Electrical and Computer Engineering, State University of New York at Binghamton, Binghamton, NY 13902, United States;Department of Electrical and Computer Engineering, State University of New York at Binghamton, Binghamton, NY 13902, United States

  • Venue:
  • Signal Processing
  • Year:
  • 2010

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Abstract

This paper presents a method that achieves large compression ratios for radar pulse trains exploited in time-difference-of-arrival/frequency-difference-of-arrival (TDOA/FDOA) emitter location; this method exploits pulse-to-pulse redundancy to get a compression ratio much higher than possible by using standard methods. We show how to use (i) the singular value decomposition (SVD) to exploit redundancy between radar pulses, and (ii) a Fisher information-based distortion criterion to enable elimination of pulses that are negligible to the FDOA estimation task. To use the SVD it is necessary to first gate the pulses, place them in a matrix and then align the pulses to get a matrix that is close to rank one. Finally, we employ reasonable coding schemes for the information to be sent and assess the achievable compression level. With such SVD-based coding the compression ratio depends on the number of pulses intercepted and the number of samples taken per pulse; depending on the scenario the compression ratio can range from about 4 up to 100 or more. Fisher information-based removal of pulses is shown to improve the compression ratio beyond what is achievable using only the SVD approach while having negligible impact on the FDOA accuracy; it does, however, degrade the TDOA accuracy. However, we demonstrate that the SVD method includes an inherent de-noising effect (common in SVD-based signal processing) that provides an improvement in TDOA accuracy over the case of no compression processing; thus, the overall impact on TDOA/FDOA accuracy can be negligible while providing significant compression ratios for typical radar signals.