Ryan Goldhahn

Waveguide invariant broadband target detection and reverberation estimation.

Reverberation often limits the performance of active sonar systems. In particular, backscatter off of a rough ocean floor can obscure target returns and/or large bottom scatterers can be easily confused with water column targets of interest. Conventional active sonar detection involves constant false alarm rate (CFAR) normalization of the reverberation return which does not account for the frequency-selective fading caused by multipath propagation. This paper presents an alternative to conventional reverberation estimation motivated by striations observed in time-frequency analysis of active sonar data. A mathematical model for these reverberation striations is derived using waveguide invariant theory. This model is then used to motivate waveguide invariant reverberation estimation which involves averaging the time-frequency spectrum along these striations. An evaluation of this reverberation estimate using real Mediterranean data is given and its use in a generalized likelihood ratio test based CFAR detector is demonstrated. CFAR detection using waveguide invariant reverberation estimates is shown to outperform conventional cell-averaged and frequency-invariant CFAR detection methods in shallow water environments producing strong reverberation returns which exhibit the described striations.

Waveguide Invariant Reverberation Mitigation for Active Sonar

Reverberation often limits the performance of active sonar systems. A method of target detection and bottom-feature suppression has been developed exploiting waveguide-invariant phenomena and the frequency-selective fading properties of broadband reverberation in shallow water channels. Specifically, the mean reverberation power is estimated along the striations in the reverberation spectrogram predicted by waveguide invariant theory, where the expected power is constant. Preliminary simulations indicate that significant performance increases are possible over traditional cell-averaging constant false alarm rate (CA-CFAR) methods in the detection of weak targets in reverberation and differentiating between bottom features and water column targets.

Enhancing AUV localization using underwater acoustic sensor networks: Results in long baseline navigation from the COLLAB13 sea trial

This work proposes a novel approach to enhance Autonomous Underwater Vehicles (AUVs) navigation through the addition of localisation services to networked acoustic communication. The approach is based on the inclusion of timing information within acoustic messages through which it is possible to know the exact time of an acoustic transmission in relation to its reception. The exploitation of such information at the network level makes it possible to create an interrogation scheme similar to that of a long-baseline (LBL). The advantage is that the AUVs themselves become the transponders of the baseline, and hence there is no need for dedicated instrumentation. Results are given from the COLLAB13 experimental campaign, where a three node network was deployed off the coast of La Spezia, Italy.

A waveguide invariant adaptive matched filter for active sonar target depth classification.

This paper addresses depth discrimination of a water column target from bottom clutter discretes in wideband active sonar. To facilitate classification, the waveguide invariant property is used to derive multiple snapshots by uniformly sub-sampling the short-time Fourier transform (STFT) coefficients of a single ping of wideband active sonar data. The sub-sampled target snapshots are used to define a waveguide invariant spectral density matrix (WI-SDM), which allows the application of adaptive matched-filtering based approaches for target depth classification. Depth classification is achieved using a waveguide invariant minimum variance filter (WI-MVF) which matches the observed WI-SDM to depth-dependent signal replica vectors generated from a normal mode model. Robustness to environmental mismatch is achieved by adding environmental perturbation constraints (EPC) derived from signal covariance matrices averaged over the uncertain channel parameters. Simulation and real data results from the SCARAB98 and CLUTTER09 experiments in the Mediterranean Sea are presented to illustrate the approach. Receiver operating characteristics (ROC) for robust waveguide invariant depth classification approaches are presented which illustrate performance under uncertain environmental conditions.

Passive acoustic detection and classification of Ziphius cavirostris

Cuviers beaked whales, Ziphius cavirostris (Zc), are a species of marine mammals particularly sensitive to anthropogenic noise. Estimating their habitats and abundance is thus of particular importance when planning and conducting active sonar exercises. Since their deep-diving behavior make them difficult to observe visually, passive acoustics is frequently used for detection. A method of automatic detection and classification of Zc is presented based on the inter-click interval, click spectrum, and direction of arrival estimated on a volumetric array. Specifically, click spectra are compared against a signal subspace constructed from eigenvectors of previously identified beaked whale clicks. The direction of arrival is estimated by cross correlating the received click across a three-dimensional array and clicks are classified based on their estimated elevation angle. Additionally, since Zc are known to produce click trains rather than single clicks, detections made without neighbouring detections are di...

Interfaces between acoustic prediction, embedded signal processing, and behaviors at NATO Undersea Research Centre

The use of acoustic sensing systems for ASW in heterogeneous sensor networks utilizing marine robots has been a subject of research at the NATO Undersea Research Centre for the past several years. In this talk, we discuss the unique challenges of implementing ASW on autonomous, collaborative networks of AUVs, including the challenges of embedding the active sonar signal processing, implementing effective underwater messaging, and designing adaptive behaviors to optimize system performance. Theoretical studies, simulations, and results from the recent GLINT series of sea trials are shown and the way forward for autonomous sensor system studies at NURC is discussed.

Time-varying filter estimation for the deconvolution of environmental reverberation from active sonar returns

The estimation and removal of the time-varying two-way impulse response to environmental scatterers from broadband reverberation data is considered for increasing the signal-to-noise ratio of sonar returns from targets in the water column. Spectrograms of simulated and real reverberation time series data from active sonars in the mid-frequency range show strong evidence of interference patterns which give clues to the number of important paths to environmental scatterers as well as their depth in the water column. In this talk we consider the estimation of a time dependent de-convolution filter for the removal of these environmental reverberation returns from active sonar data. Issues regarding the degrees of freedom required for the efficient implementation of this filter and the stability of these estimates are considered. Simulation results are shown which demonstrate the potential gain of using this approach to partially null the impact of environmental scatterers in active sonar data.

Environmentally tolerant waveguide‐invariant target depth classification for active sonar.

Shallow‐water environments produce active sonar returns with many target‐like returns from bottom clutter. Scatterer depth classification methods which can discriminate bottom clutter from water column targets are thus critical for controlling false alarms. In recent work, the waveguide invariant (WI) property of shallow‐water channels has been used to obtain multiple snapshots of frequency‐domain target return data from a single active sonar ping. These snapshots are, in turn, used to estimate a waveguide invariant spectral density matrix (WI‐SDM), which can serve as a basis for depth classification. One method employing the WI‐SDM performs minimum variance filtering (MVF) matched to depth‐dependent signal replicas derived from a normal mode model. While MVF is capable of depth classification when the environment is known, it is sensitive to mismatch when the channel parameters are uncertain. In this paper, robustness of the WI MVF to mismatch is achieved by using environmental perturbation constraints d...

Waveguide invariant minimum variance scatterer depth classification for active sonar.

Active sonar systems are plagued by false alarms due to confusion between returns from water‐column targets and backscatter from the bottom. Both feature‐based and physics‐based classifiers are notoriously susceptible to mismatch of the environment used for training and/or modeling active sonar returns. In this paper, in order to achieve more robust classification, uniformly sub‐sampled DFT‐coefficients from a single snapshot of the wideband active sonar return are used to define a waveguide‐invariant spectral density matrix (WI‐SDM). The WI‐SDM facilitates adaptive matched‐filtering based approaches for target depth estimation, where the waveguide invariant property is exploited to obtain uncorrelated snapshots without inflating covariance matrix rank. Depth classification is then performed by designing a waveguide‐invariant minimum variance filter (WI‐MVF) with adaptive weights which minimize ambiguous depth sidelobes. Simulation and real data results in a shallow‐water Mediterranean environment are presented to illustrate the approach. [Work sponsored by ONR.]

Wavguide invariant‐based characterization of wideband active sonar clutter discretes.

In active sonar, clutter discretes can produce strong, target‐like returns which often produce false alarms of water column targets. While false alarm reduction methods based on statistical feature‐based classifiers often lack sufficient training data, matched‐field based classifiers often suffer from model mismatch. A waveguide invariant‐based approach for estimating the magnitude short‐time Fourier transform (STFT) of reverberation returns was presented [Goldhahn et al., J. Acoust. Soc. Am. 124(5), 2841–2851, (2008)]. In this paper, the waveguide invariant properties of the reverberation are used to predict the frequency selective fading of strong clutter discretes. In particular, comparison of waveguide invariant‐based magnitude STFT estimates are compared with predictions made using a model of frequency‐selective fading from a clutter discrete. The results are further compared with real sonar returns collected during the SCARAB98 experiment off a shipwreck in the Malta Plateau. The results suggest tha...