2023_programme: Modeling sonar performance using J-divergence

• Session: 15. Towards Automatic Target Recognition. Detection, Classification and Modelling
  Organiser(s): Johannes Groen, Yan Pailhas, Roy Edgar Hansen, Jessica Topple and Narada Warakagoda
• Lecture: Modeling sonar performance using J-divergence [invited]
  Paper ID: 1856
  Author(s): Abraham Douglas
  Presenter: Abraham Douglas
  Abstract: This presentation demonstrates the use of J-divergence as a performance measure for detection in a sonar system. The inherent inaccuracies between system-level performance and the cell-level (PD,PF) detector operating point used in traditional analysis open the door to using approximate performance measures such as J-divergence. The properties of J-divergence making it an appealing choice are covered: summing to accrue J-divergence across multiple independent measurements (e.g., from multiple source signals, waveforms, or arrays), a data-processing inequality dictating that processing cannot improve J-divergence, and an asymptotic relationship to the traditional (PD,PF) operating point. Simple forward models of J-divergence are presented for matched filters and energy detectors when applied to the standard signal models in Gaussian noise. A “design” J-divergence, which is chosen by the desired qualitative performance level, is used in simple equations to obtain the “design” SNR required to achieve it. This provides a direct replacement for the detection threshold (DT) term in the sonar equation that is easier to evaluate and apply. Example applications of J-divergence are presented illustrating its utility in the modeling and adaptation of current systems as well as the design and analysis of new ones.
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• Corresponding author: Dr Douglas Abraham
  Affiliation: University of Washington Applied Physics Laboratory
  Country: United States
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