Seminal article about model-based space-time array processing.

The Reflections series takes a look back on historical articles from The Journal of the Acoustical Society of America that have had a significant impact on the science and practice of acoustics.

Autonomous learning and interpretation of channel multipath scattering using braid manifolds in underwater acoustic communications.

In this work, we explore machine learning through a model-agnostic feature representation known as braiding, that employs braid manifolds to interpret multipath ray bundles. We generate training and testing data using the well-known BELLHOP model to simulate shallow water acoustic channels across a wide range of multipath scattering activity. We examine three different machine learning techniques-k-nearest neighbors, random forest tree ensemble, and a fully connected neural network-as well as two machine learning applications. The first application applies known physical parameters and braid information to determine the number of reflections the acoustic signal may undergo through the environment. The second application applies braid path information to determine if a braid is an important representation of the channel (i.e., evolving across bands of higher amplitude activity in the channel). Testing accuracy of the best trained machine learning algorithm in the first application was 86.70% and the testing accuracy of the second application was 99.94%. This work can be potentially beneficial in examining how the reflectors in the environment changeover time while also determining relevant braids for faster channel estimation.

Shallow water acoustic channel estimation using two-dimensional frequency characterization.

Shallow water acoustic channel estimation techniques are presented at the intersection of time, frequency, and sparsity. Specifically, a mathematical framework is introduced that translates the problem of channel estimation to non-uniform sparse channel recovery in two-dimensional frequency domain. This representation facilitates disambiguation of slowly varying channel components against high-energy transients, which occupy different frequency ranges and also exhibit significantly different sparsity along their local distribution. This useful feature is exploited to perform non-uniform sampling across different frequency ranges, with compressive sampling across higher Doppler frequencies and close to full-rate sampling at lower Doppler frequencies, to recover both slowly varying and rapidly fluctuating channel components at high precision. Extensive numerical experiments are performed to measure relative performance of the proposed channel estimation technique using non-uniform compressive sampling against traditional compressive sampling techniques as well as sparsity-constrained least squares across a range of observation window lengths, ambient noise levels, and sampling ratios. Numerical experiments are based on channel estimates from the SPACE08 experiment as well as on a recently developed channel simulator tested against several field trials.

Signal Processing Methods to Interpret Polychlorinated Biphenyls in Airborne Samples.

The main contribution of this interdisciplinary work is a robust computational framework to autonomously discover and quantify previously unknown associations between well-known (target) and potentially unknown (non-target) toxic industrial air pollutants. In this work, the variability of polychlorinated biphenyl (PCB) data is evaluated using a combination of statistical, signal processing, and graph-based informatics techniques to interpret the raw instrument signal from gas chromatography-mass spectrometry (GC/MS/MS) data sets. Specifically, minimum mean-squared techniques from the adaptive signal processing literature are extended to detect and separate coeluted (overlapped) peaks in the raw instrument signal. A graph-based visualization is provided which bridges two complementary approaches to quantitative pollution studies: (i) peak-cognizant target analysis (limits data analysis to few well-known compounds) and (ii) chemometric analysis (statistical large-scale data analysis) that is agnostic of specific compounds. Further, peak fitting techniques based on L2 error minimization are employed to autonomously calculate the amount of each PCB present with a normalized mean square error of -18.4851 dB. Graph-based visualization of associations between known and unknown compounds are developed through principal component analysis and both fuzzy c-means (FCM) and k-means clustering techniques are implemented and compared. The efficiency of these methods are compared using 150 air samples analyzed for individual PCBs with GC/MS/MS against traditional target-only techniques that perform analysis across only the known (target) PCBs. Parameter optimization techniques are employed to evaluate the relative contribution of PCB signals against ten potential source signals representing legacy signatures from historical manufacture of Aroclors and modern sources of PCBs produced as by products of pigment and polymer manufacturing. Aroclors 1232, 1254, 1016, and 1221 as well as non-Aroclor 3, 3', dichlorobiphenyl (PCB 11) were found in many of the samples as unique source signals that describe PCB mixtures in air samples collected from Chicago, IL.