• Login
    About WHOAS
    View Item 
    •   WHOAS Home
    • Woods Hole Oceanographic Institution
    • Academic Programs
    • WHOI Theses
    • View Item
    •   WHOAS Home
    • Woods Hole Oceanographic Institution
    • Academic Programs
    • WHOI Theses
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of WHOASCommunities & CollectionsBy Issue DateAuthorsTitlesKeywordsThis CollectionBy Issue DateAuthorsTitlesKeywords

    My Account

    LoginRegister

    Statistics

    View Usage Statistics

    Adaptive matched field processing in an uncertain propagation environment

    Thumbnail
    View/Open
    Preisig_Thesis (31.56Mb)
    Date
    1992-01
    Author
    Preisig, James C.  Concept link
    Metadata
    Show full item record
    Citable URI
    https://hdl.handle.net/1912/5493
    Location
    Arctic Ocean
    DOI
    10.1575/1912/5493
    Keyword
    Adaptive signal processing
    Abstract
    Adaptive array processing algorithms have achieved widespread use because they are very effective at rejecting unwanted signals (i.e., controlling sidelobe levels) and in general have very good resolution (i.e., have narrow mainlobes). However, many adaptive high-resolution array processing algorithms suffer a significant degradation in performance in the presence of environmental mismatch. This sensitivity to environmental mismatch is of particular concern in problems such as long-range acoustic array processing in the ocean where the array processor's knowledge of the propagation characteristics of the ocean is imperfect. An Adaptive Minmax Matched Field Processor has been developed which combines adaptive matched field processing and minmax approximation techniques to achieve the effective interference rejection characteristic of adaptive processors while limiting the sensitivity of the processor to environmental mismatch. The derivation of the algorithm is carried out within the framework of minmax signal processing. The optimal array weights are those which minimize the maximum conditional mean squared estimation error at the output of a linear weight-and-sum beamformer. The error is conditioned on the propagation characteristics of the environment and the maximum is evaluated over the range of environmental conditions in which the processor is expected to operate. The theorems developed using this framework characterize the solutions to the minmax array weight problem, and relate the optimal minmax array weights to the solution to a particular type of Wiener filtering problem. This relationship makes possible the development of an efficient algorithm for calculating the optimal minmax array weights and the associated estimate of the signal power emitted by a source at the array focal point. An important feature of this algorithm is that it is guarenteed to converge to an exact solution for the array weights and estimated signal power in a finite number of iterations. The Adaptive Minmax Matched Field Processor can also be interpreted as a two-stage Minimum Variance Distortionless Response (MVDR) Matched Field Processor. The first stage of this processor generates an estimate of the replica vector of the signal emitted by a source at the array focal point, and the second stage is a traditional MVDR Matched Field Processor implemented using the estimate of the signal replica vector. Computer simulations using several environmental models and types of environmental uncertainty have shown that the resolution and interference rejection capability of the Adaptive Minmax Matched Field Processor is close to that of a traditional MVDR Matched Field Processor which has perfect knowledge of the characteristics of the propagation environment and far exceeds that of the Bartlett Matched Field Processor. In addition, the simulations show that the Adaptive Minmax Matched Field Processor is able to maintain it's accuracy, resolution and interference rejection capability when it's knowledge of the environment is only approximate, and is therefore much less sensitive to environmental mismatch than is the traditional MVDR Matched Field Processor.
    Description
    Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution January 1992
    Collections
    • Applied Ocean Physics and Engineering (AOP&E)
    • WHOI Theses
    Suggested Citation
    Thesis: Preisig, James C., "Adaptive matched field processing in an uncertain propagation environment", 1992-01, DOI:10.1575/1912/5493, https://hdl.handle.net/1912/5493
     

    Related items

    Showing items related by title, author, creator and subject.

    • Thumbnail

      The development and application of random matrix theory in adaptive signal processing in the sample deficient regime 

      Pajovic, Milutin (Massachusetts Institute of Technology and Woods Hole Oceanographic Institution, 2014-09)
      This thesis studies the problems associated with adaptive signal processing in the sample deficient regime using random matrix theory. The scenarios in which the sample deficient regime arises include, among others, the ...
    • Thumbnail

      Data adaptive velocity/depth spectra estimation in seismic wide angle reflection analysis 

      Leverette, Steven John (Massachusetts Institute of Technology and Woods Hole Oceanographic Institution, 1977-07)
      In studying the earth with reflection seismics, one of the major unknowns is the velocity structure of the medium. Techniques used to determine the velocity structure commonly involve multi-channel arrays which measure ...
    • Thumbnail

      Adaptive sampling in autonomous marine sensor networks 

      Eickstedt, Donald Patrick (Massachusetts Institute of Technology and Woods Hole Oceanographic Institution, 2006-06)
      In this thesis, an innovative architecture for real-time adaptive and cooperative control of autonomous sensor platforms in a marine sensor network is described in the context of the autonomous oceanographic network scenario. ...
    All Items in WHOAS are protected by original copyright, with all rights reserved, unless otherwise indicated. WHOAS also supports the use of the Creative Commons licenses for original content.
    A service of the MBLWHOI Library | About WHOAS
    Contact Us | Send Feedback | Privacy Policy
    Core Trust Logo