Adaptive matched field processing in an uncertain propagation environment
Citable URI
https://hdl.handle.net/1912/5493Location
Arctic OceanDOI
10.1575/1912/5493Keyword
Adaptive signal processingAbstract
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 highresolution 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 longrange 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 weightandsum
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 twostage
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
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