title: Physically constrained maximum likelihood mode filtering.
abstract: Mode filtering is most commonly implemented using the sampled mode shapes or pseudoinverse algorithms. Buck et al. [J. Acoust. Soc. Am. 103, 1813-1824 (1998)] placed these techniques in the context of a broader maximum a posteriori (MAP) framework. However, the MAP algorithm requires that the signal and noise statistics be known a priori. Adaptive array processing algorithms are candidates for improving performance without the need for a priori signal and noise statistics. A variant of the physically constrained, maximum likelihood (PCML) algorithm [A. L. Kraay and A. B. Baggeroer, IEEE Trans. Signal Process. 55, 4048-4063 (2007)] is developed for mode filtering that achieves the same performance as the MAP mode filter yet does not need a priori knowledge of the signal and noise statistics. The central innovation of this adaptive mode filter is that the received signal's sample covariance matrix, as estimated by the algorithm, is constrained to be that which can be physically realized given a modal propagation model and an appropriate noise model. Shallow water simulation results are presented showing the benefit of using the PCML method in adaptive mode filtering.
authors.affiliation: Woods Hole Oceanographic Institution, MS 44, Woods Hole, Massachusetts 02543, USA. jcpapp@mit.edu
authors.forename: Joseph C
authors.initials: JC
authors.lastname: Papp
authors.forename: James C
authors.initials: JC
authors.lastname: Preisig
authors.forename: Andrey K
authors.initials: AK
authors.lastname: Morozov
mesh_terms: D000162:Acoustics / Q000295:instrumentation; D000465:Algorithms; D003198:Computer Simulation; D016013:Likelihood Functions; D009038:Motion; D009716:Numerical Analysis, Computer-Assisted; D011312:Pressure; D012815:Signal Processing, Computer-Assisted; D013016:Sound; D013997:Time Factors; D014159:Transducers; D014867:Water
chemical_list: D014867:Water