Full text loading...
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
Results of an independent analysis of the inverse beamformer for use in the advanced processor build process
1.R. E. Zarnich, “A fresh look at broadband passive sonar processing,” Proceedings of the Adaptive Sensor Array (ASAP) Workshop, 10–11 March 1999, 99–103 (1999).
2.N. H. Guertin and R. W. Miller, “A-RCI-The right way to submarine superiority,” Nav. Eng. J. 110(2) (1998).
3.M. I. Fages, “Submarine programs: A resource sponsor’s perspective,” Sub. Rev. 53–59 (1998).
4.A. H. Nuttall, G. C. Carter, and E. M. Montavon, “Estimation of the Two-Dimensional Spectrum of the Space-Time Noise Field for a Sparse Line Array,” J. Acoust. Soc. Am. 55, 1034–1041 (1974).
5.There were actually several demonstrations of the fact that legacy sonar systems could be improved by use of various acoustic signal-processing algorithms.
6.Dr. Wilson, a Naval Academy graduate and submarine-qualified officer, was also a Captain in the U.S. Naval Reserve and adjunct faculty member at the Naval Postgraduate School in Monterey, CA.
7.J. Donald, A. H. Nuttall, and J. H. Wilson, “Inverse beamforming sonar system and method,” United States Patent 5,216,640, 1 June 1993.
8.J. H. Wilson, “Applications of inverse beamforming theory,” J. Acoust. Soc. Am. 98, 3250–3261 (1995).
9.J. P. Fabre and J. H. Wilson, “Minimum detectable level evaluation of inverse beamforming using outpost SUNRISE data,” J. Acoust. Soc. Am. 98, 3262–3278 (1995).
10.Other evaluators included R. Gramann, F. Rule, H. Cox, C. Penrod, and R. Zarnich.
11.G. C. Carter, internal—private communication, providing an initial technical evaluation of IBF to U.S. Navy decision makers (1995).
12.I. S. D. Solomon, A. J. Knight, and M. V. Greening, “Sonar array signal processing for sparse linear arrays,” Fifth International Symposium on Signal Processing and its Applications (ISSPA), Brisbane, Australia, 1999, Vol. 2, pp. 527–530.
13.I. M. G. Lourtie and G. C. Carter, “Signal detectors for random ocean media,” J. Acoust. Soc. Am. 92, 1420–1427 (1992).
14.A. B. Baggeroer, W. A. Kuperman, and H. Schmidt, “Matched-field processing: Source localization in correlated noise as an optimum parameter estimation problem,” J. Acoust. Soc. Am. 83, 571–587 (1988).
15.For purposes of comparison with IBF, we can think of APB-98 as traditional signal processing (energy detection and cross correlation) with: (a) improved filter parameter settings for bandwidth, center frequency, and integration time, and (b) adaptive beamforming.
16.These test results are consistent with the recent Australian testing that showed MVDR adaptive beamforming is better than FIM under certain conditions. In particular, in the important case where the gain and phases of the sensor array elements were well known and well corrected for (that is, properly calibrated), the Australian researchers found that MVDR adaptive beamforming performed better than IBF with FIM. Also, they introduced an extension to the FIM component of IBF and call it WFIM, which their initial results find to be superior to FIM. The results of Solomon et al. are consistent with our test results that show adaptive beamforming outperforms IBF with FIM.
17.R. Gramann and F. Rule, Executive Summary, private communication (1998).
18.In comparing WFIM to other methods, one must use caution to do the comparison on an equal footing, in particular, since weighting in the covariance domain is a convolution in the frequency domain and effectively changes the shape of the frequency bandwidth (resolution) of the sonar system and vice versa. That is, averaging or convolution in the covariance domain is equivalent to frequency weighting.
19.Chief of Naval Operations, Washington, DC, private communication (14 December 1998).
Article metrics loading...