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An airborne microwave wide-band radiometer (500–2000 MHz) was operated for the first time in Antarctica to better understand the emission properties of sea ice, outlet glaciers and the interior ice sheet from Terra Nova Bay to Dome C. The different glaciological regimes were revealed to exhibit unique spectral signatures in this portion of the microwave spectrum. Generally, the brightness temperatures over a vertically homogeneous ice sheet are warmest at the lowest frequencies, consistent with models that predict that those channels sensed the deeper, warmer parts of the ice sheet. Vertical heterogeneities in the ice property profiles can alter this basic interpretation of the signal. Spectra along the lengths of outlet glaciers were modulated by the deposition and erosion of snow, driven by strong katabatic winds. Similar to previous experiments in Greenland, the brightness temperatures across the frequency band were low in crevasse areas. Variations in brightness temperature were consistent with spatial changes in sea ice type identified in satellite imagery and in situ ground-penetrating radar data. The results contribute to a better understanding of the utility of microwave wide-band radiometry for cryospheric studies and also advance knowledge of the important physics underlying existing L-band radiometers operating in space. [ABSTRACT FROM AUTHOR]

Ice internal temperature and basal geothermal heat flux (GHF) are analyzed along a study line in northwestern Greenland. The temperatures were obtained from a previously reported inversion of airborne microwave brightness-temperature spectra. The temperatures vary slowly through the upper ice sheet and more rapidly near the base increasing from ~259 K near Camp Century to values near the melting point near NorthGRIP. The flow-law rate factor is computed from temperature data and analytic expressions. The rate factor increases from ~1 × 10−8 to 8 × 10−8 kPa−3 a−1 along the line. A laminar flow model combined with the depth-dependent rate factor is used to estimate horizontal velocity. The modeled surface velocities are about a factor of 10 less than interferometric synthetic aperture radar (InSAR) surface velocities. The laminar velocities are fitted to the InSAR velocities through a factor of 8 enhancement of the rate factor for the lower 25% of the column. GHF values retrieved from the brightness temperature spectra increase from ~55 to 84 mW m−2 from Camp Century to NorthGRIP. A strain heating correction improves agreement with other geophysical datasets near Camp Century and NEEM but differ by ~15 mW m−2 in the central portion of the profile. [ABSTRACT FROM AUTHOR]

In this paper, the performance of the Coupled Ocean‐Atmosphere Mesoscale Prediction System (COAMPS®) electromagnetic (EM) propagation forecast is assessed using numerous metrics such as forecast lengths, frequencies, ranges, and spatial resolutions. The modeled PL (Propagation Loss) for 2–40 GHz based on the COAMPS ®predicted refractivity profiles is evaluated by 1 month of range‐dependent measurements, which were collected during Coupled Air‐Sea Processes and Electromagnetic ducting Research (CASPER) East Campaign. PL data were collected using a shore‐to‐ship radio link, near the coast of Duck, NC. Mesoscale atmospheric refractivity predictions for the CASPER East operational area are provided by COAMPS®. Propagation loss predictions are computed from these refractivity profiles via a parabolic wave equation propagation code. The results show a good agreement between the COAMPS®‐predicted and the measured propagation loss for the northwest Atlantic region. The analyses show that the prediction error varies from 3 to 8 dB as the frequency increases from 2 to 40 GHz. In addition, COAMPS® successfully tracked the changes in the refractivity field even when the forecast length increased from 0 to 48 hr, with only a marginal degradation in EM prediction accuracy within the first 12 hr. Higher horizontal range resolution does not result in significant improvement in EM predictions as long as there are at least 2 M‐profiles that allows the EM propagation code to capture the first order horizontal variation. Low‐resolution, high order nested COAMPS® fields still produce good EM forecasts for this particular geometry. Key Points: COAMPS® is a registered trademark of the Naval Research Laboratory electromagnetic propagation forecast is evaluated with one month long 2–40 GHz RF measurement data during ducting conditionsNumerous metrics such as forecast lengths, frequencies, ranges, and spatial resolutions are used to assess COAMPS® predictionCOAMPS® predictions on stable and unstable atmospheric conditions are compared [ABSTRACT FROM AUTHOR]

We address the problem of tracking the spatial and temporal lower atmospheric variations in maritime environments. The evolution of the range and height-dependent index of refraction is tracked using the sea clutter return from sea-borne radars operating in the region. A split-step fast Fourier transform based parabolic equation approximation to the wave equation is used to compute the clutter return in complex environments with varying index of refraction. In addition, regional statistics are incorporated as prior densities, resulting in a highly nonlinear and non-Gaussian tracking problem. Tracking algorithms such as the extended Kalman, unscented Kalman and particle filters are used for tracking both evaporative and surface-based electromagnetic ducts frequently encountered in marine environments. The tracking performances and applicability of these techniques to different types of refractivity-from-clutter problems are studied using the posterior Cramer-Rao lower bound. Track divergence statistics are analyzed. The results show that while the tracking performance of the Kalman filters is comparable to the particle filters in evaporative duct tracking, it is limited by the high non-linearity of the parabolic equation for the surface-based duct case. Particle filters, on the other hand, prove to be very promising in tracking a wide range of environments including the abruptly changing ones. Index Terms--Atmospheric ducts, extended Kalman filter (EKF), parabolic equation, particle filter (PF), refractivity-fromclutter (RFC), spatial and temporal tracking, unscented Kalman filter (UKF).

This paper describes a Markov chain Monte Carlo (MCMC) sampling approach for the estimation of not only the radio refractivity profiles from radar clutter but also the uncertainties in these estimates. This is done by treating the refractivity from clutter (RFC) problem in a Bayesian framework. It uses unbiased MCMC sampling techniques, such as Metropolis and Gibbs sampling algorithms, to gather more accurate information about the uncertainties. Application of these sampling techniques using an electromagnetic split-step fast Fourier transform parabolic equation propagation model within a Bayesian inversion framework can provide accurate posterior probability distributions of the estimated refractivity parameters. Then these distributions can be used to estimate the uncertainties in the parameters of interest. Two different MCMC samplers (Metropolis and Gibbs) are analyzed and the results compared not only with the exhaustive search results but also with the genetic algorithm results and helicopter refractivity profile measurements. Although it is slower than global optimizers, the probability densities obtained by this method are closer to the true distributions. Index Terms--Atmospheric ducts, genetic algorithms, Markov chain Monte Carlo (MCMC) techniques, radar clutter, refractivity estimation.

An airborne microwave wide-band radiometer (500-2000 MHz) was operated for the first time in Antarctica to better understand the emission properties of sea ice, outlet glaciers and the interior ice sheet from Terra Nova Bay to Dome C. The different glaciological regimes were revealed to exhibit unique spectral signatures in this portion of the microwave spectrum. Generally, the brightness temperatures over the inland ice sheet were warmest at the lowest frequencies consistent with models that predict that those channels sensed the deeper, warmer parts of the ice sheet. Spectra along the lengths of outlet glaciers were modulated by the deposition and erosion of snow, driven by strong katabatic winds. Similar to previous experiments in Greenland, the brightness temperatures across the frequency band were low in crevasse areas. Variations in brightness temperature were consistent with spatial changes in sea ice type identified in satellite imagery and in situ ground penetrating radar data. The results contribute to a better understanding of the utility of microwave wide-band radiometry for cryospheric studies and also advance knowledge of the important physics underlying existing L-band radiometers operating in space. [ABSTRACT FROM AUTHOR]

The Coastal Land–Air–Sea Interaction (CLASI) project aims to develop new "coast-aware" atmospheric boundary and surface layer parameterizations that represent the complex land–sea transition region through innovative observational and numerical modeling studies. The CLASI field effort involves an extensive array of more than 40 land- and ocean-based moorings and towers deployed within varying coastal domains, including sandy, rocky, urban, and mountainous shorelines. Eight Air–Sea Interaction Spar (ASIS) buoys are positioned within the coastal and nearshore zone, the largest and most concentrated deployment of this unique, established measurement platform. Additionally, an array of novel nearshore buoys and a network of land-based surface flux towers are complemented by spatial sampling from aircraft, shore-based radars, drones, and satellites. CLASI also incorporates unique electromagnetic wave (EM) propagation measurements using a coherent array, drone receiver, and a marine radar to understand evaporation duct variability in the coastal zone. The goal of CLASI is to provide a rich dataset for validation of coupled, data assimilating large-eddy simulations (LES) and the Navy's Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS). CLASI observes four distinct coastal regimes within Monterey Bay, California (MB). By coordinating observations with COAMPS and LES simulations, the CLASI efforts will result in enhanced understanding of coastal physical processes and their representation in numerical weather prediction (NWP) models tailored to the coastal transition region. CLASI will also render a rich dataset for model evaluation and testing in support of future improvements to operational forecast models. [ABSTRACT FROM AUTHOR]

This article explores the effects of system frequency on electromagnetic (EM) remote sensing of atmospheric refractivity. A frequency range of 2–40 GHz covering S- to Ka-bands is analyzed. A multifrequency inversion algorithm is developed to estimate the evaporation duct heights using a parabolic wave equation propagation model. The capabilities of a theoretical multifrequency system are explored for high and low signal-to-noise ratios for varying numbers of frequencies and bands. Experimental data were collected using the lower atmospheric propagation ultrawideband (LATPROP-UWB) system deployed during the Coupled Air-Sea Processes and EM Ducting Research (CASPER) East Campaign. Range-dependent propagation loss measurements at 53 frequencies ranging from 2 to 40 GHz were made. The evaporation duct refractivity profiles estimated from the multifrequency LATPROP-UWB data were compared to the profiles inferred from the concurrent meteorological and oceanographic measurements. Inversion results show that the optimal refractivity retrieval can be achieved using ten frequencies and results in a fourfold reduction in the estimation RMSE compared to a single-frequency inversion. [ABSTRACT FROM AUTHOR]

The Ultra-Wideband Software Defined Microwave Radiometer (UWBRAD) is a wideband radiometer operating from 0.5 to 2 GHz for remote sensing of polar ice sheet temperature profiles. Small-scale (cm to m) fluctuations in firn density in the upper portion of the ice sheet significantly impact observed brightness temperatures. Previously, a fully coherent model based on solving Maxwell’s equations for thousands of layers throughout the entire ice sheet was developed. Density profiles in the model are described as the sum of a smooth average density profile with a spatially correlated random process that represents density fluctuations. In this article, we develop a “partially coherent” implementation of the coherent model that captures the impact of variations in ice density on predicted brightness temperatures while improving computational efficiency. The partially coherent model divides the ice sheet into blocks. Within each block, the coherent model is applied to take into account coherence among the contributions of closely spaced layers. A Monte Carlo procedure is used to calculate the average block reflection and transmission parameters. Between adjacent blocks, interactions are assumed to be incoherent, and the radiative transfer theory is used to incoherently cascade block parameters. Results of the partially coherent model are in good agreement with the fully coherent model and also with Soil Moisture Ocean Salinity (SMOS) and UWBRAD brightness temperature observations. [ABSTRACT FROM AUTHOR]

Parabolic wave equation (PWE) propagation models using multiple phase screens (MPS) are widely used to predict the log-amplitude variance of the signal propagating in tropospheric turbulence. Previous literature demonstrates the limitations of the MPS technique, usable only when the signal frequency is low (typically below 5 GHz for radio-wave propagation in the troposphere). In this article, we demonstrate that the accuracy of the MPS technique depends not only on the signal frequency but also on the outer scale length (Los) of the turbulent eddy. For example, when Los is on the order of tens of meters (e.g., in most terrestrial communication links), the MPS method is indeed unusable at high frequencies, but, when Los is on the order of hundreds of meters (e.g., in air–ground and satellite links), the method can be used even at millimeter-wave frequencies. Next, a correlated phase screen (CPS) implementation within the PWE is introduces, which avoids the condition of statistical noncorrelation of the phase screens in the MPS technique, and as such, it can be used for any signal frequency. Finally, an ad hoc error metric is provided, allowing for the selection of the appropriate phase screen model based on the signal frequency, turbulence outer scale length, and the electromagnetic (EM) link distance. The simulated signal log-amplitude variances as a function of range at microwave and millimeter-wave frequencies using the CPS method are reported here for the first time and are shown to be in excellent agreement with the analytically obtained values. [ABSTRACT FROM AUTHOR]

Refractivity-from-clutter (RFC) is a technique for remote sensing of the lowest portion of vertical atmospheric refractivity profile, particularly evaporation and surface-based ducts. It uses the sea-surface backscattered radar signal to estimate the ducting conditions. The Lower Atmospheric Propagation (LATPROP) radar is a software-defined, coherent-on-receive remote sensing system built on a low-cost, commercial off-the-shelf marine radar platform operating at 9.41 GHz. The system is designed to specifically operate as a RFC-capable platform sensitive to low clutter power returns from the sea surface. This paper examines the appropriate radar design, system modifications, and radar signal processing steps, and provides a comparison with existing RFC-capable systems. The LATPROP system was deployed during the Coupled Air-Sea Processes and electromagnetic Ducting Research West Campaign that took place off of the coast of Southern California during October 2017 and was shown to accurately capture the effects of the ducting conditions. [ABSTRACT FROM AUTHOR]

An X-band vertical array system is developed for measuring and characterizing electromagnetic (EM) propagation in the marine atmospheric boundary layer. In particular, the evaporation duct that commonly forms over water is investigated as part of the Coupled Air-Sea Processes and Electromagnetic ducting Research (CASPER) at-sea experimental campaign conducted off the coast of Duck, NC, USA, during October–November of 2015. Monte Carlo simulations are first used to develop an optimal array of four vertically spaced receiving antennas. In the experiment, the antennas are mounted on the stern A-frame of a research vessel and measure EM signals transmitted from beacons mounted on another research vessel and on the pier at the Army Field Research Facility. While the propagation loss versus range provides a data set similar to previous work, the vertical array can provide sampling of modes in the leaky waveguide formed in the duct. Combining both the range and height sampling results in a robust inversion method for evaporation duct estimation. In this paper, the efficacy of the four-element array is demonstrated by estimation of the evaporation duct height through comparison with a library of precomputed propagation curves generated using the parabolic wave equation. Low model error gives confidence in the estimates, which are consistent with the concurrent environmental measurements performed by the CASPER team. It is found that the evaporation duct can vary significantly with range and time over the duration of a data collection run. [ABSTRACT FROM AUTHOR]

Recent experimental campaigns provided the opportunity to measure radio wave propagation and atmospheric conditions with the $X$ -band beacon-receiver (XBBR) array system. The system consists of vertical arrays of transmitters and receivers for measuring the $X$ -band propagation. Measurements near the sea surface can be used to obtain information regarding the refractivity profile of the lower atmosphere. Since ducted propagation acts as a leaky waveguide, the vertical array elements in various transmit and receive height combinations effectively observe differing combinations of the modal components propagating in the duct, the use of multiple combinations improves the estimation of duct properties. The aforementioned measurement campaigns occurred near the coast of southern California; the SoCal 2013 experiment and the Scripps Pier Campaign. During both campaigns, the propagation loss recorded at each of the receivers from each of the transmitters, standardized by the total received power, was compared to variable terrain radio parabolic equation model predictions in order to estimate the evaporation duct height (EDH). Point meteorological data were recorded and used with the Navy Atmospheric Vertical Surface Layer Model to obtain in situ measurements of the EDH. Comparisons show strong correlation between EDH values inferred from XBBR measurements and meteorological information. [ABSTRACT FROM PUBLISHER]

An ultra-wideband radiometer has been developed to measure subsurface properties of the cryosphere including ice sheets and sea ice. The radiometer measures brightness temperature spectra from 0.5 to 2 GHz using 12 channels, each of which measures scene brightness temperatures over an 88-MHz bandwidth resolved into 0.24-MHz intervals. The instrument was flown over northwestern Greenland in September 2016 and acquired the first, wideband, low-frequency brightness temperature spectra over the ice sheet and coastal region. The results reveal strong spatial and spectral variations that correlate well with the physical properties of the surface encountered along the flight path, which started over ocean, then passed the rock near the coast, and then up onto the ablation, wet, percolation, and dry snow zones of the interior ice sheet. In particular, strong spectral responses in percolation and dry snow zones are observed and plausibly explained by varying the distribution of horizontal density layers and isolated icy bodies in the upper portion of the firn. The success of the airborne deployment of the instrument and subsequent implementation of algorithms to limit radio frequency interference in unprotected bands is motivating continued airborne investigations as well as stimulating research into the feasibility of a spaceborne instrument. [ABSTRACT FROM AUTHOR]

The refractivity from clutter (RFC) technique has been proved to be an effective way to estimate atmospheric duct structure. An important issue for RFC is how to make the estimate more robust, especially in range-dependent ducting conditions. Traditionally, statistical inversion methods need a large number of forward propagation model runs to obtain an acceptable result. Especially when the parameter search space is multidimensional, these methods are prone to being trapped into local optimal solutions. Recently published results (Zhao and Huang) indicate that the adjoint parabolic equation (PE) method holds promise for real-time estimation of one-dimensional refractive index structure from radar sea clutter returns. This paper is aimed at extending the adjoint PE method to range-dependent evaporation duct cases, with a log-linear relationship describing duct structures. Numerical simulations are used to test the performance of this method and the results are compared with that retrieved using a genetic algorithm. Both noise-free and 3-dB additive Gaussian noise clutter simulations are considered, as well as linearly and nonlinearly varying duct height with range. [ABSTRACT FROM AUTHOR]

In order to carry out geoacoustic inversion in low signal-to-noise ratio (SNR) conditions, extended duration observations coupled with source and/or receiver motion may be necessary. As a result, change in the underlying model parameters due to time or space is anticipated. In this paper, an inversion method is proposed for cases when the model parameters change abruptly or slowly. A model parameter change-point detection method is developed to detect the change in the model parameters using the importance samples and corresponding weights that are already available from the recursive Bayesian inversion. If the model parameters change abruptly, a change-point will be detected and the inversion will restart with the pulse measurement after the change-point. If the model parameters change gradually, the inversion (based on constant model parameters) may proceed until the accumulated model parameter mismatch is significant and triggers the detection of a change-point. These change-point detections form the heuristics for controlling the coherent integration time in recursive Bayesian inversion. The method is demonstrated in simulation with parameters corresponding to the low SNR, 100-900 Hz linear frequency modulation pulses observed in the Shallow Water 2006 experiment [Tan, Gerstoft, Yardim, and Hodgkiss, J. Acoust. Soc. Am. 136, 1187-1198 (2014)]. [ABSTRACT FROM AUTHOR]

Surface generated ambient noise can be used to infer sediment properties. Here, a passive geoacoustic inversion method that uses noise recorded by a drifting vertical array is adopted. The array is steered using beamforming to compute the noise arriving at the array from various directions. This information is used in two different ways: Coherently (cross-correlation of upward/downward propagating noise using a minimum variance distortionless response fathometer), and incoherently (bottom loss vs frequency and angle using a conventional beamformer) to obtain the bottom properties. Compressive sensing is used to invert for the number of sediment layer interfaces and their depths using coherent passive fathometry. Then the incoherent bottom loss estimate is used to refine the sediment thickness, sound speed, density, and attenuation values. Compressive sensing fathometry enables automatic determination of the number of interfaces. It also tightens the sediment thickness priors for the incoherent bottom loss inversion which reduces the search space. The method is demonstrated on drifting array data collected during the Boundary 2003 experiment. [ABSTRACT FROM AUTHOR]

Sequential Bayesian methods such as particle filters have been used to track a moving source in an unknown and space/time-evolving ocean environment. These methods treat both the source and the ocean parameters as non-stationary unknown random variables and track them via the multivariate posterior probability density function. Particle filters are numerical methods that can operate on nonlinear systems with non-Gaussian probability density functions. Particle smoothers are a natural extension to these filters. A smoother is appropriate in applications where data before and after the time of interest are readily available. Both past and 'future' measurements are exploited in smoothers, whereas filters just use past measurements. Geoacoustic and source tracking is performed here using two smoother algorithms, the forward-backward smoother and the two-filter smoother. Smoothing is demonstrated on experimental data from both the SWellEx-96 and SW06 experiments where the parameter uncertainty is reduced relative to just filtering alone. [ABSTRACT FROM AUTHOR]

A typical geoacoustic inversion procedure involves powerful source transmissions received on a large-aperture receiver array. A more practical approach is to use a single moving source and/or receiver in a low signal to noise ratio (SNR) setting. This paper uses single-receiver, broadband, frequency coherent matched-field inversion and exploits coherently repeated transmissions to improve estimation of the geoacoustic parameters. The long observation time creates a synthetic aperture due to relative source-receiver motion. This approach is illustrated by studying the transmission of multiple linear frequency modulated (LFM) pulses which results in a multi-tonal comb spectrum that is Doppler sensitive. To correlate well with the measured field across a receiver trajectory and to incorporate transmission from a source trajectory, waveguide Doppler and normal mode theory is applied. The method is demonstrated with low SNR, 100-900 Hz LFM pulse data from the Shallow Water 2006 experiment. [ABSTRACT FROM AUTHOR]

Sequential Bayesian techniques enable tracking of evolving geophysical parameters via sequential observations. They provide a formulation in which the geophysical parameters that characterize dynamic, nonstationary processes are continuously estimated as new data become available. This is done by using prediction from previous estimates of geophysical parameters, updates stemming from physical and statistical models that relate seismic measurements to the unknown geophysical parameters. In addition, these techniques provide the evolving uncertainty in the estimates in the form of posterior probability density functions. In addition to the particle filters (PFs), extended, unscented, and ensemble Kalman filters (EnKFs) were evaluated. The filters were compared via reflector and nonvolcanic tremor tracking examples. Because there are numerous geophysical problems in which the environmental model itself is not known or evolves with time, the concept of model selection and its filtering implementation were introduced. A multiple model PF was then used to track an unknown number of reflectors from seismic interferometry data. We found that when the equations that define the geophysical problem are strongly nonlinear, a PF was needed. The PF outperformed all Kalman filter variants, especially in low signal-to-noise ratio tremor cases. However, PFs are computationally expensive. The EnKF is most appropriate when the number of parameters is large. Because each technique is ideal under different conditions, they complement each other and provide a useful set of techniques for solving sequential geophysical inversion problems. [ABSTRACT FROM AUTHOR]

Efforts to characterize environmental parameters from ambient noise must contend with uncertainty introduced by stochastic fluctuations of the noise itself. This Letter calculates the Fisher information and Cramer-Rao bound of an unbiased correlated ambient noise parameter estimate. As an illustration, lower bounds on the error covariance of medium speed and attenuation parameters are obtained for a two-dimensional isotropic ambient noise scenario. The results demonstrate that an optimal sensor separation exists for obtaining the minimum error and the predictions are validated using simulated parameter inversions. The influences of record length, bandwidth, signal-to-noise, and spatial resolution are discussed. [ABSTRACT FROM AUTHOR]

Radar clutter in a non-standard atmosphere usually is modeled based on a single grazing angle at each range. Instead, the angular distribution of incident power can be used to obtain a more accurate model of the clutter. Angular spectral estimation provides the grazing angle distribution of propagating power. However, a large gradient in the refractivity profile, e.g., an evaporation duct, distorts plane wave propagation which in turn violates assumptions of plane wave spectral estimation. Ray tracing is used in these situations, but has its own limitations (e.g., shadow zones). We suggest using curved wave spectral estimation (CWS) that yields reliable results for any refractivity profile, in contrast to plane wave spectral estimation. CWS is used to derive multiple grazing angle clutter, a model for ocean surface clutter in the microwave region that depends on all incident angles at each range and their corresponding powers. [ABSTRACT FROM PUBLISHER]

Environmental parameters can have large spatial and temporal variability in shelfbreak regions. The capability of sequential Bayesian filters in tracking this variation is investigated. Particle filtering (PF) is used to extract the environmental parameters and their uncertainties. The method tracks the environment with fewer particles relative to conventional geoacoustic inversion methods using successive independent inversions. As an example, data from the Shallow Water 2006 Experiment are processed. The PF approach first is used to track the source and the environment with little spatial variation just northwest of the shelfbreak. Then the strongly range-dependent shelfbreak region is analyzed and the PF results are compared to previous geoacoustic inversion studies from the region. [ABSTRACT FROM AUTHOR]

In the ocean, acoustic information arrives at the receiver distorted by the medium and corrupted by noise. Even when the signal is deterministic, a complete description must minimally be a statistical one. If information regarding the medium or the form of the signal is available, it too can and should be included, leading to what is known as Model-Based signal processing. In other words, any processing carried out on the received signal should contain the best characterization of the distortion by the medium and corruption by the measurement noise that is available. [ABSTRACT FROM AUTHOR]

Sequential filtering provides a suitable framework for estimating and updating the unknown parameters of a system as data become available. The foundations of sequential Bayesian filtering with emphasis on practical issues are first reviewed covering both Kalman and particle filter approaches. Filtering is demonstrated to be a powerful estimation tool, employing prediction from previous estimates and updates stemming from physical and statistical models that relate acoustic measurements to the unknown parameters. Ocean acoustic applications are then reviewed focusing on source tracking, estimation of environmental parameters evolving in time or space, and frequency tracking. Spatial arrival time tracking is illustrated with 2006 Shallow Water Experiment data. [ABSTRACT FROM PUBLISHER]

A particle filtering (PF) approach is presented for performing sequential geoacoustic inversion of a complex ocean acoustic environment using a moving acoustic source. This approach treats both the environmental parameters [e.g., water column sound speed profile (SSP), water depth, sediment and bottom parameters] at the source location and the source parameters (e.g., source depth, range and speed) as unknown random variables that evolve as the source moves. This allows real-time updating of the environment and accurate tracking of the moving source. As a sequential Monte Carlo technique that operates on nonlinear systems with non-Gaussian probability densities, the PF is an ideal algorithm to perform tracking of environmental and source parameters, and their uncertainties via the evolving posterior probability densities. The approach is demonstrated on both simulated data in a shallow water environment with a sloping bottom and experimental data collected during the SWellEx-96 experiment. [ABSTRACT FROM AUTHOR]

This paper incorporates tracking techniques such as the extended Kalman, unscented Kalman, and particle (PF) filters into geoacoustic inversion problems. This enables spatial and temporal tracking of environmental parameters and their underlying probability densities, making geoacoustic tracking a natural extension to geoacoustic inversion techniques. Water column and seabed properties are tracked in simulation for both vertical (VLA) and horizontal (HLA) line arrays using the three tracking filters. Filter performances are compared in terms of filter efficiencies using the posterior Cramér-Rao lower bound. Tracking capabilities of the geoacoustic filters under slowly and quickly changing environments are studied in terms of divergence statistics. Geoacoustic tracking can provide continuously environmental estimates and their uncertainties using only a fraction of the computational power of classical geoacoustic inversion schemes. Interfilter comparison show that while a high-particle-number PF outperforms the Kalman filters, there are many cases where all three filters perform equally well depending on the inversion configuration (such as the HLA versus VLA and frequency) and the tracked parameters. [ABSTRACT FROM AUTHOR]

Seabed interface depths and fathometer amplitudes are tracked for an unknown and changing number of sub-bottom reflectors. This is achieved by incorporating conventional and adaptive fathometer processors into sequential Monte Carlo methods for a moving vertical line array. Sediment layering information and time-varying fathometer response amplitudes are tracked by using a multiple model particle filter with an uncertain number of reflectors. Results are compared to a classical particle filter where the number of reflectors is considered to be known. Reflector tracking is demonstrated for both conventional and adaptive processing applied to the drifting array data from the Boundary 2003 experiment. The layering information is successfully tracked by the multiple model particle filter even for noisy fathometer outputs. [ABSTRACT FROM AUTHOR]

This paper discusses the effects of frequency selection on source localization and geoacoustic inversion methods that use frequency coherent objective functions. Matched-field processors based on frequency-coherent objective functions often have rapidly fluctuating range ambiguity surfaces. Insufficient sampling in frequency domain results in range aliasing terms that affect geoacoustic inversion. Range aliasing and its effects on source localization and environmental parameter inversion are demonstrated on data collected during the MAPEX2000 experiment. Guidance for frequency selection to avoid range aliasing is provided. [ABSTRACT FROM AUTHOR]

Microwave radiometry has provided valuable spaceborne observations of Earth's geophysical properties for decades. The recent SMOS, Aquarius, and SMAP satellites have demonstrated the value of measurements at 1400 MHz for observing surface soil moisture, sea surface salinity, sea ice thickness, soil freeze/thaw state, and other geophysical variables. However, the information obtained is limited by penetration through the subsurface at 1400 MHz and by a reduced sensitivity to surface salinity in cold or wind-roughened waters. Recent airborne experiments have shown the potential of brightness temperature measurements from 500-1400 MHz to address these limitations by enabling sensing of soil moisture and sea ice thickness to greater depths, sensing of temperature deep within ice sheets, improved sensing of sea salinity in cold waters, and enhanced sensitivity to soil moisture under vegetation canopies. However, the absence of significant spectrum reserved for passive microwave measurements in the 500-1400 MHz band requires both an opportunistic sensing strategy and systems for reducing the impact of radio-frequency interference. Here, we summarize the potential advantages and applications of 500-1400 MHz microwave radiometry for Earth observation and review recent experiments and demonstrations of these concepts. We also describe the remaining questions and challenges to be addressed in advancing to future spaceborne operation of this technology along with recommendations for future research activities.

In order to carry out geoacoustic inversion in low signal-to-noise ratio (SNR) conditions, extended duration observations coupled with source and/or receiver motion may be necessary. As a result, change in the underlying model parameters due to time or space is anticipated. In this paper, an inversion method is proposed for cases when the model parameters change abruptly or slowly. A model parameter change-point detection method is developed to detect the change in the model parameters using the importance samples and corresponding weights that are already available from the recursive Bayesian inversion. If the model parameters change abruptly, a change-point will be detected and the inversion will restart with the pulse measurement after the change-point. If the model parameters change gradually, the inversion (based on constant model parameters) may proceed until the accumulated model parameter mismatch is significant and triggers the detection of a change-point. These change-point detections form the heuristics for controlling the coherent integration time in recursive Bayesian inversion. The method is demonstrated in simulation with parameters corresponding to the low SNR, 100-900 Hz linear frequency modulation pulses observed in the Shallow Water 2006 experiment [Tan, Gerstoft, Yardim, and Hodgkiss, J. Acoust. Soc. Am. 136, 1187-1198 (2014)].

A low signal to noise ratio (SNR), single source/receiver, broadband, frequency-coherent matched-field inversion procedure recently has been proposed. It exploits coherently repeated transmissions to improve estimation of the geoacoustic parameters. The long observation time improves the SNR and creates a synthetic aperture due to relative source-receiver motion. To model constant velocity source/receiver horizontal motion, waveguide Doppler theory for normal modes is necessary. However, the inversion performance degrades when source/receiver acceleration exists. Furthermore processing a train of pulses all at once does not take advantage of the natural incremental acquisition of data along with the ability to assess the temporal evolution of parameter uncertainty. Here a recursive Bayesian estimation approach is developed that coherently processes the data pulse by pulse and incrementally updates estimates of parameter uncertainty. It also approximates source/receiver acceleration by assuming piecewise constant but linearly changing source/receiver velocities. When the source/receiver acceleration exists, it is shown that modeling acceleration can reduce further the parameter estimation biases and uncertainties. The method is demonstrated in simulation and in the analysis of low SNR, 100-900 Hz linear frequency modulated (LFM) pulses from the Shallow Water 2006 experiment.