Autonomous Ocean Sensing: A Practical Approach to Modeling, Adaptive Whitening, and Location Estimation

Recent and ongoing advances in ocean sensor technology have led to an increased interest in autonomous data gathering. Autonomous ocean sensing presents numerous challenges for researchers, especially given the ocean's great size and environmentally unforgiving nature. Such challenges include modeling of ambient noise spectra, adaptive whitening of background noise, and sensor location estimation.

Many natural phenomena, including ambient ocean noise, can be most accurately approximated by filters with fractional-order rolloff. Existing analog design techniques have realized and improved upon such approximations. To date, digital design techniques have been largely restricted to discretizations of existing analog solutions. A novel approach is presented for designing digital lowpass filters with fractional-order rolloff directly in the discrete domain through pole-zero placement. Filters designed using the proposed iterative technique are stable, have precisely-definable cutoff frequencies, and do not suffer from the variations that can arise from transforming an existing analog design. The proposed technique is shown to outperform existing analog and digital design methods, both subjectively and by objective measures.

Passive underwater listening devices are often deployed to listen for narrowband signals of interest in time-varying background ocean noise. Quantization of sensor data adds white noise which can overwhelm weak narrowband signals if the background noise is sufficiently colored. Whitening the background noise prior to quantization can reduce the detrimental effects, but the whitening process must preserve any tonals in the signal for maximum effectiveness. Existing adaptive whitening techniques make no effort to avoid suppressing tonals in the whitening process, while existing spectral separation methods fail to whiten background noise. Two novel methods are proposed for performing adaptive whitening of background ambient noise while preserving narrowband tones at their original signal-to-noise ratios. The proposed methods are shown to outperform combinations of existing partial solutions both subjectively and by evaluating the objective criteria introduced.

Air-deployable, autonomous ocean sensors can be equipped with GPS receivers to make their position known to a ship, a base station, or an aircraft overhead. Exact knowledge of the point at which such sensors enter the water can be an important consideration, but current methods of estimating this water-entry point are insufficient for some applications. To improve upon their performance, the novel application of multivariate fractional polynomial regression is proposed. The proposed method is shown to outperform existing approaches in a variety of scenarios when mean, median, and 95th percentile of error are used as objective metrics of comparison. Parameter sensitivity analysis suggests that this advantage is maintained across a wide range of simulation parameters for a combination of linear and nonlinear sensor drift profiles.