Sonar systems for object recognition

The deep sea exploration and exploitation is one of the biggest challenges of the next century. Military, oil & gas, o shore wind farming, underwater mining, oceanography are some of the actors interested in this eld. The engineering and technical challenges to perform any tasks underwater are great but the most crucial element in any underwater systems has to be the sensors. In air numerous sensor systems have been developed: optic cameras, laser scanner or radar systems. Unfortunately electro magnetic waves propagate poorly in water, therefore acoustic sensors are a much preferred tool then optical ones. This thesis is dedicated to the study of the present and the future of acoustic sensors for detection, identi cation or survey. We will explore several sonar con gurations and designs and their corresponding models for target scattering. We will show that object echoes can contain essential information concerning its structure and/or composition

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.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 computati..

Particle separation in surface acoustic wave microfluidic devices using reprogrammable, pseudo-standing waves

We report size and density/compressibility-based particle sorting using on-off quasi-standing waves based on the frequency difference between two ultrasonic transducers. The 13.3 MHz fundamental operating frequency of the surface acoustic wave microfluidic device allows the manipulation of particles on the micrometer scale. Experiments, validated by computational fluid dynamics, were carried out to demonstrate size-based sorting of 5–14.5 μm diameter polystyrene (PS) particles and density/compressibility-based sorting of 10 μm PS, iron-oxide, and poly(methyl methacrylate) particles, with densities ranging from 1.05 to 1.5 g/cm3. The method shows a sorting efficiency of >90% and a purity of >80% for particle separation of 10 μm and 14.5 μm, demonstrating better performance than similar sorting methods recently published (72%–83% efficiency). The sorting technique demonstrates high selectivity separation of particles, with the smallest particle ratio being 1.33, compared to 2.5 in previous work. Density/compressibility-based sorting of polystyrene and iron-oxide particles showed an efficiency of 97 ± 4% and a purity of 91 ± 5%. By varying the sign of the acoustic excitation signal, continuous batch acoustic sorting of target particles to a desired outlet was demonstrated with good sorting stability against variations of the inflow rates