Browsing University of Alaska Fairbanks by Subject "Acoustics"
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Modeling Of The Fisheries Acoustics ProblemThis dissertation presents a mathematical model of the overall fisheries acoustics problem posed by enumeration of fish populations using sonar. Emphasis is placed on three key components: a new geometric model for the target strength (TS) of Pacific salmon, a fish distribution for sockeye salmon, and generation of artificial sonar data. Results of the TS and fish distribution models show TS varies on height and breadth of fish as much as on fish length and TS from the airfilled swimbladder is the major contributor as reported by Foote [1985]. A fish roll factor within 45° leads to TS variations within 7 dB for normal incidence, side aspect and 2 dB for dorsal aspect. Also second order effects of ray propagation through fish flesh on TS from the swimbladder provide TS results up to 20 dB lower at high aspect angles. The geometric model predicts TS values that match extremely well with TS data collected on Pacific salmon and other species in river and ocean environments. By varying fish size and swimbladder parameters and considering the effect of fish flesh, the model covers the range of TS values that occur in the field, thus identifying and quantifying the uncertainty in the experimental data. The overall approach in this work is to construct a direct model providing artificial sonar data, then use an inverse model (echo integration algorithm) with that data or with experimental data to compare results. The echo integration results are not reliable at any fish rate for a fixed river crosssection. Estimated fish counts of 07 are obtained from 100 simulations for a known fish distribution of 3 fish (0.1 fish/sec). Similarly, at 0.5 fish/sec with 15 known fish, estimates of 030 were obtained; at 1 fish/sec with 30 known fish, estimates of 050; and at 5 fish/sec with 150 known fish, estimates of 0100 fish. Fish counts ranged from 019 for 3 known fish when ping rate changed from 110 pings/sec and when pulse width varied from 0.11.0 ms.

Neural Network Approach To Classification Of Infrasound SignalsAs part of the International Monitoring Systems of the Preparatory Commissions for the Comprehensive Nuclear TestBan Treaty Organization, the Infrasound Group at the University of Alaska Fairbanks maintains and operates two infrasound stations to monitor global nuclear activity. In addition, the group specializes in detecting and classifying the manmade and naturally produced signals recorded at both stations by computing various characterization parameters (e.g. mean of the cross correlation maxima, trace velocity, direction of arrival, and planarity values) using the inhouse developed weighted leastsquares algorithm. Classifying commonly observed lowfrequency (0.0150.1 Hz) signals at out stations, namely mountain associated waves and high tracevelocity signals, using traditional approach (e.g. analysis of power spectral density) presents a problem. Such signals can be separated statistically by setting a window to the tracevelocity estimate for each signal types, and the feasibility of such technique is demonstrated by displaying and comparing various summary plots (e.g. universal, seasonal and azimuthal variations) produced by analyzing infrasound data (20042007) from the Fairbanks and Antarctic arrays. Such plots with the availability of magnetic activity information (from the College International Geophysical Observatory located at Fairbanks, Alaska) leads to possible physical sources of the two signal types. Throughout this thesis a newly developed robust algorithm (sum of squares of variance ratios) with improved detection quality (under low signal to noise ratios) over two wellknown detection algorithms (mean of the cross correlation maxima and Fisher Statistics) are investigated for its efficacy as a new detector. A neural network is examined for its ability to automatically classify the two signals described above against clutter (spurious signals with common characteristics). Four identical perceptron networks are trained and validated (with >92% classification rates) using eight independent datasets; each dataset consists of threeelement (each element being a characterization parameter) feature vectors. The validated networks are tested against an expert, Prof. Charles R. Wilson, who has been studying those signals for decades. From the graphical comparisons, we conclude that such networks are excellent candidate for substituting the expert. Advantages to such networks include robustness and resistance to errors and the bias of a human operator.

The Characterization Of The Infrasonic Noise Field And Its Effects On Least Squares EstimationLocalization of the source of an acoustic wave propagating through the atmosphere is not a new problem. Location methods date back to World War I, when sound location was used to determine enemy artillery positions. Since the drafting of the Comprehensive NuclearTestBan Treaty in 1996 there has been increased interest in the accurate location of distant sources using infrasound. A standard method of acoustic source location is triangulation of the source from multiarray back azimuth estimates. For waves traveling long distances through the atmosphere, the most appropriate method of estimating the back azimuth is the least squares estimate (LSE). Under the assumption of an acoustic signal corrupted with additive Gaussian, white, uncorrelated noise the LSE is theoretically the minimum variance, unbiased estimate of the slowness vector. The infrasonic noise field present at most arrays is known to violate the assumption of white, uncorrelated noise. The following work characterizes the noise field at two infrasound arrays operated by the University of Alaska Fairbanks, The power distribution and coherence of the noise fields was determined from atmospheric pressure measurements collected from 20032006. The estimated power distribution and coherence of the noise field were not the white, uncorrelated noise field assumed in the analytic derivation of the LSE of the slowness vector. The performance of the LSE of azimuth and trace velocity with the empirically derived noise field was numerically compared to its performance under the standard noise assumptions. The effect of violating the correlation assumption was also investigated. The inclusion of clutter in the noise field introduced a dependence to the performance of the LSE on the relative signal amplitude. If the signaltoclutter ratio was above 10 dB, the parameter estimates made with the correlated noise field were comparable to the estimates made with uncorrelated noise. From the results of these numerical studies, it was determined that the assumption of Gaussian, white, uncorrelated noise had little effect on the performance of the LSE at signaltonoise ratios greater than 10 dB, but tended to over estimate the performance of the LSE at lower signaltonoise ratios.