Platform motion estimation in multi-band synthetic aperture sonar with coupled variational autoencoders.

Coherent processing in synthetic aperture sonar (SAS) requires platform motion estimation and compensation with sub-wavelength accuracy for high-resolution imaging. Micronavigation, i.e., through-the-sensor platform motion estimation, is essential when positioning information from navigational instruments is absent or inadequately accurate. A machine learning method based on variational Bayesian inference has been proposed for unsupervised data-driven micronavigation. Herein, the multiple-input multiple-output arrangement of a multi-band SAS system is exploited and combined with a hierarchical variational inference scheme, which self-supervises the learning of platform motion and results in improved micronavigation accuracy.

Far-field analytical solutions of the non-homogeneous Helmholtz and wave equations for spatially non-localized sources.

Non-localized impulsive sources are ubiquitous in underwater acoustic applications. However, analytical expressions of their acoustic field are usually not available. In this work, far-field analytical solutions of the non-homogeneous scalar Helmholtz and wave equations are developed for a class of spatially extended impulsive sources. The derived expressions can serve as benchmarks to verify the accuracy of numerical solvers.

Unsupervised learning of platform motion in synthetic aperture sonar.

Synthetic aperture sonar (SAS) provides high-resolution acoustic imaging by processing coherently the backscattered signal recorded over consecutive pings as the bearing platform moves along a predefined path. Coherent processing requires accurate estimation and compensation of the platform's motion for high quality imaging. The motion of the platform carrying the SAS system can be estimated by cross-correlating redundant recordings at successive pings due to the spatiotemporal coherence of statistically homogeneous backscatter. This data-driven approach for estimating the motion of the SAS platform is essential when positioning information from navigational instruments is absent or inadequately accurate. Herein, the problem of platform motion estimation from coherence measurements of diffuse backscatter is formulated in a probabilistic framework. A variational autoencoder is designed to disentangle the ping-to-ping platform displacement from three-dimensional (3D) spatiotemporal coherence measurements. Unsupervised representation learning from unlabeled data offers robust 3D platform motion estimation. Including a small amount of labeled data during training improves further the platform motion estimation accuracy.

Compressive synthetic aperture sonar imaging with distributed optimization.

Synthetic aperture sonar (SAS) provides high-resolution acoustic imaging by processing coherently the backscattered acoustic signal recorded over consecutive pings. Traditionally, object detection and classification tasks rely on high-resolution seafloor mapping achieved with widebeam, broadband SAS systems. However, aspect- or frequency-specific information is crucial for improving the performance of automatic target recognition algorithms. For example, low frequencies can be partly transmitted through objects or penetrate the seafloor providing information about internal structure and buried objects, while multiple views provide information about the object's shape and dimensions. Sub-band and limited-view processing, though, degrades the SAS resolution. In this paper, SAS imaging is formulated as an ℓ1-norm regularized least-squares optimization problem which improves the resolution by promoting a parsimonious representation of the data. The optimization problem is solved in a distributed and computationally efficient way with an algorithm based on the alternating direction method of multipliers. The resulting SAS image is the consensus outcome of collaborative filtering of the data from each ping. The potential of the proposed method for high-resolution, narrowband, and limited-aspect SAS imaging is demonstrated with simulated and experimental data.

Impact of temporal Doppler on synthetic aperture sonar imagery.

Synthetic Aperture Sonar (SAS) coherently processes the acoustic data acquired along a linear trajectory. The imaging process is in essence an inverse problem where the reflectivity of the seafloor has to be estimated. Several imaging algorithms have been proposed over the years including back-projection algorithms. One commonly assumed hypothesis, however, is that the antenna is motionless during transmission and reception. This hypothesis is known as the start-stop assumption. This paper questions the validity of this assumption, and proposes a full derivation of the SAS processing taking into account the vehicle motion by using the Lorentz transformation. The cell migration for the SAS system is computed and the validity limit of the stop-start assumption depending on the SAS system parameters is derived. Numerical examples of start-stop assumption violations are given and the Doppler cell migration correction on real SAS data are presented and discussed.

Increasing circular synthetic aperture sonar resolution via adapted wave atoms deconvolution.

Circular Synthetic Aperture Sonar (CSAS) processing computes coherently Synthetic Aperture Sonar (SAS) data acquired along a circular trajectory. This approach has a number of advantages, in particular it maximises the aperture length of a SAS system, producing very high resolution sonar images. CSAS image reconstruction using back-projection algorithms, however, introduces a dissymmetry in the impulse response, as the imaged point moves away from the centre of the acquisition circle. This paper proposes a sampling scheme for the CSAS image reconstruction which allows every point, within the full field of view of the system, to be considered as the centre of a virtual CSAS acquisition scheme. As a direct consequence of using the proposed resampling scheme, the point spread function (PSF) is uniform for the full CSAS image. Closed form solutions for the CSAS PSF are derived analytically, both in the image and the Fourier domain. The thorough knowledge of the PSF leads naturally to the proposed adapted atom waves basis for CSAS image decomposition. The atom wave deconvolution is successfully applied to simulated data, increasing the image resolution by reducing the PSF energy leakage.

Analysis and classification of broadband echoes using bio-inspired dolphin pulses.

To date most sonars use narrow band pulses and often only the echo envelope is used for object detection and classification. This paper considers the advantages afforded by bio-inspired sonar for object identification and classification through the analysis and the understanding of the broadband echo structure. Using the biomimetic dolphin based sonar system in conjunction with bio-inspired pulses developed from observations of bottlenose dolphins performing object identification tasks, results are presented from experiments carried out in a wave tank and harbor. In these experiments responses of various targets to two different bio-inspired signals are measured and analyzed. The differences in response demonstrate the strong dependency between signal design and echo interpretation. In the simulations and empirical data, the resonance phenomena of these targets cause strong notches and peaks in the echo spectra. With precision in the localization of these peaks and dips of around 1 kHz, the locations are very stable for broadside insonification of the targets and they can be used as features for classification. This leads to the proposal of a broadband classifier which operates by extracting the notch positions in the target echo spectra.

Bio-inspired wideband sonar signals based on observations of the bottlenose dolphin (Tursiops truncatus).

This paper uses advanced time-frequency signal analysis techniques to generate new models for bio-inspired sonar signals. The inspiration comes from the analysis of bottlenose dolphin clicks. These pulses are very short duration, between 50 and 80 micros, but for certain examples we can delineate a double down-chirp structure using fractional Fourier methods. The majority of clicks have energy distributed between two main frequency bands with the higher frequencies delayed in time by 5-20 micros. Signal syntheses using a multiple chirp model based on these observations are able to reproduce much of the spectral variation seen in earlier studies on natural dolphin echolocation pulses. Six synthetic signals are generated and used to drive the dolphin based sonar (DBS) developed through the Biosonar Program office at the SPAWAR Systems Center, San Diego, CA. Analyses of the detailed echo structure for these pulses ensonifying two solid copper spherical targets indicate differences in discriminatory potential between the signals. It is suggested that target discrimination could be improved through the transmission of a signal packet in which the chirp structure is varied between pulses. Evidence that dolphins may use such a strategy themselves comes from observations of variations in the transmissions of dolphins carrying out target detection and identification tasks.