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Publication Detail
Adaptive waveform design for target classification
  • Publication Type:
    Conference
  • Authors:
    Cheng Y, Brennan PV
  • Publication date:
    01/12/2013
  • Pagination:
    680, 684
  • Published proceedings:
    Proceedings of 2013 Science and Information Conference, SAI 2013
  • ISBN-13:
    9780989319300
  • Status:
    Published
Abstract
For active sensors, waveform/signal optimization is of great importance to improve system performance. In this paper, the adaptive waveform parameter is designed to improve the classification performance by minimizing the Bayesian error probability for the optimal decision of a symmetric binary hypothesis testing problem. It is well known that the probability of error can be bounded by the Chernoff divergence between the distributions of the two hypotheses. Therefore, by maximizing the Chernoff divergence between the two distributions of the hypotheses, the optimal waveform parameter is obtained to enhance the classification performance. Simulation results prove that the adaptive optimal waveform outperforms the fixed parameter waveform. © 2013 The Science and Information Organization.
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