Granger Hickman

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.

Non-recurrent wideband continuous active sonar

This paper presents a continuous active sonar (CAS) waveform which enables high revisit rates and wideband processing gains to be achieved with suppression of range-ambiguous returns. Non-recurrent wideband linear FM signals with circular Costas frequency-staggering across chirp repetition intervals is shown to provide ambiguity functions with good Doppler and range resolution, controllable Doppler and range ambiguities, and a high revisit rate. These slow-time Costas or “SLO-CO” CAS waveforms allow for the possibility of updating range-velocity estimates every pulse repetition interval while achieving the clutter suppression associated with the use of conventional wideband LFM signals.

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.

Matched-field depth estimation for active sonar.

This work concerns the problem of estimating the depth of a submerged scatterer in a shallow-water ocean by using an active sonar and a horizontal receiver array. As in passive matched-field processing (MFP) techniques, numerical modeling of multipath propagation is used to facilitate localization. However, unlike passive MFP methods where estimation of source range is critically dependent on relative modal phase modeling, in active sonar source range is approximately known from travel-time measurements. Thus the proposed matched-field depth estimation (MFDE) method does not require knowledge of the complex relative multipath amplitudes which also depend on the unknown scatterer characteristics. Depth localization is achieved by modeling depth-dependent relative delays and elevation angle spreads between multipaths. A maximum likelihood depth estimate is derived under the assumption that returns from a sequence of pings are uncorrelated and the scatterer is at constant depth. The Cramér-Rao lower bound on depth estimation mean-square-error is computed and compared with Monte Carlo simulation results for a typical range-dependent, shallow-water Mediterranean environment. Depth estimation performance to within 10% of the water column depth is predicted at signal-to-noise ratios of greater than 10 dB. Real data results are reported for depth estimation of an echo repeater to within 10-m accuracy in this same shallow water environment.

SPARSE ARRAY IMAGING OF CHANGE‐DETECTED ULTRASONIC SIGNALS BY MINIMUM VARIANCE PROCESSING

Spatially distributed arrays of permanently attached ultrasonic sensors are being considered for structural health monitoring systems. Most algorithms for analyzing the received signals are based upon change detection whereby baselines from the undamaged structure are subtracted from current signals of interest, and the residual signals are analyzed. In particular, delay‐and‐sum algorithms applied to the residual signals have been shown to be effective for imaging damage in plate‐like structures that support propagation of guided waves. Here we consider minimum variance processing of the residual signals, which is an adaptive beamforming method in common use for processing of radar signals where the weights are adjusted at each pixel location prior to summation based upon actual and expected signal amplitudes. Experimental results from a sparse sensor array show that this processing method can provide a significantly improved signal‐to‐noise ratio by suppressing unwanted sidelobes in the image.

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.

Field directionality synthesis from multiple array orientations: A least squares approach

Combining multiple beamforming outputs from different orientations of a linear array to obtain a full 2-D field directionality is investigated in this paper. A wavefield sampling method is proposed that could be effective at eliminating left-right ambiguity, reducing resolution problems at endfire directions, and suppressing background noise. Simulation results suggest that this wavefield sampling method could improve the probability of detection of weak directional sources. The method is compared with previous methods and is shown to have resolution and computation advantages.

A Graph-Theoretic Approach for Constraining Floor Plan Estimation from Radar Measurements

This paper proposes an approach to deducing the floor plan of a building using only a set of noisy room dimension measurements that have been obtained indirectly through radar probes. It is not assumed that the set of measurements is complete. The key feature of this approach is a method of relating, via a graph-theoretical construction, a given floor plan topology to a model that depends linearly on a minimal set of parameters. Given a list of floor-plan topologies and a system of realizability constraints, it is possible to formulate this problem as a constrained least-squares optimization problem. An algorithm for generating all floor plan topologies with a specified number of rooms is presented, as well as an algorithm for deciding if two floor plan representations are isomorphic. The techniques in this paper are intended to serve as an exploration into the theoretical performance bounds on the floor plan estimation problem.

Single Hydrophone Passive Localization of Transiting Acoustic Sources

This paper presents a new method to estimate J motion parameters of loud transiting acoustic sources with a single stationary hydrophone in shallow water environments. The parameters of interest include the time of Closest Point of Approach (CPA) and the ratio of velocity over the CPA range. The method involves only processing the intensity surface of the spectrogram of received broadband acoustic signals. The shapes of striation curves on such surfaces are directly related to motion parameters by the principle of the waveguide invariant theory. Since little environmental information besides the waveguide invariant parameter, beta, is used, the proposed method promises to be more robust than full matched field source localization. Successful application of the method to speed boat localization in a lake experiment is described to illustrate its effectiveness.

Co-prime comb signals for active sonar

This paper presents an active sonar waveform that achieves range-Doppler performance similar to a uniform frequency comb, but uses far fewer tones to do so. The trade-off for this reduction in occupied bandwidth is a larger bandwidth extent. Co-prime comb signals consist of tones at non-uniformly spaced frequencies according to a 2-level nested co-prime array structure. Specialized non-matched filter processing enables recovery of an ambiguity surface similar to that of a uniform comb, but using fewer tonal components. This reduction in occupied bandwidth offers potential benefits such as sharing, interference avoidance, and Signal-to-Noise Ratio (SNR) improvements in both peakand total-power-limited scenarios.