How do I implement an adaptive thresholding algorithm for underwater sonar
Your question has received quite few contributions, probably because of a lacking content. During a recent conference , I came across the PhD thesis: Détection en Environnement non Gaussien (Detection in a non-Gaussian environment). Since it is in French, I reproduce the abstract here:


  For a long time, radar echoes coming from the various returns of the
  transmitted signal on many objects of the environment (clutter) have
  been exclusively modelled by Gaussian vectors. The related optimal
  detection procedure was then performed by the classical matched
  filter. Then, the technological improvement of radar systems showed
  that the true nature of the clutter could not be considered as
  Gaussian any more. Though the optimality of the matched filter is no
  more valid in such cases, CFAR techniques (Constant False Alarm Rate)
  were proposed for this detector in order to adapt the value of the
  detection threshold to the multiple local variations of the clutter.
  In spite of their diversity, none of these techniques turned to be
  either robust or optimal in these situations. With the modelling of
  the clutter by non-Gaussian complex processes, such as SIRP
  (Spherically Invariant Random Process), optimal structures of coherent
  detection have been found. These models describe many non-Gaussian
  laws, like K-distribution or Weibull laws, and are acknowledged in the
  literature to model many experimental situations in a relevant way. To
  identify the law of their characteristic component (namely the
  texture) without statistical a priori on the model, we propose, in
  this thesis, to tackle the problem by a Bayesian approach. Two new
  estimation methods of the texture law emerge from this proposition:
  the first one is a parametric method, based on a Padé approximation of
  the moment generating function, and the second one results from a
  Monte Carlo estimation. These estimations are carried out on reference
  clutter data and lead to two new optimal detection strategies,
  respectively named PEOD (Padé Estimated Optimum Detector) and BORD
  (Bayesian Optimum Detector Radar). The asymptotic expression of the
  BORD (convergence in law), called the "Asymptotic BORD", is
  established together with its law. This last result gives access to
  the optimal theoretical performances of the Asymptotic BORD, and may
  also applied to the BORD if the data correlation matrix is
  non-singular. The detection performances of BORD and those of
  Asymptotic BORD are evaluated on experimental ground clutter data. We
  obtained results that validate both the relevance of SIRP model for
  the clutter, the optimality of the BORD and its adaptability to any
  type of environment.


The math should be readable. If it is of any help, you can track English reference by the author or PhD thesis committee.