Design considerations for a compact correlation velocity log

Abstract

A Correlation Velocity Log (CVL) has some advantages (lower source level and operating frequency) over a Doppler Velocity Log (DVL) as a navigational aid for an unmanned underwater vehicle (UUV). A CVL provides a bottom referenced velocity estimate by estimating displacement using the incoherently scattered field from an acoustic projector and an array of hydrophones. A small low cost UUV generally operates in shallow water and has limited space and power available for a navigational aid, creating added constraints for the design of a CVL. The important design considerations (such as size, array geometry, operating frequency, and bandwidth) will be discussed as they relate to accuracy of the velocity estimate and operating range. [The authors acknowledge the financial support for this work by Lockheed Martin Undersea Systems.]

Motion Estimation of Underwater Platforms Using Impulse Responses From the Seafloor

“…To produce the narrow beams, high frequencies are required and therefore the acoustic signals are highly attenuated, limiting its usage to shallow waters [15]. A correlation velocity log (CVL) is less constrained in this respect [16]. A CVL has a looking down single wide-beam projector and an array of hydrophones to receive the backscattered signals from the seafloor.…”

“…A CVL has a looking down single wide-beam projector and an array of hydrophones to receive the backscattered signals from the seafloor. This allows the use of lower frequencies with less signal attenuation [16]. 3) Techniques based on environment referencing: These techniques use sensors for extracting information from the platform surrounding environment to estimate the position and attitude and can be divided into visual and acoustic.…”

“…One of the main differences between the proposed technique and a CVL is that the CVL works by transmitting two short ping pulses that are reflected from the seafloor and the echoes are measured at the receivers [24], [16], while the proposed technique uses a continuous signal transmission and the receivers are continuously listening at the same time. This allows continuous measurement of the channel impulse response while systems like the CVL have a ping period for each measurement, which limits amount of information used for the motion estimation.…”

“…Another difference is that a CVL measures the correlation between responses of the two pings [24], whereas our technique exploits the complex-valued channel impulse response variation in time and uses a DL network to estimate the motion. Finally, a CVL requires an array with a large number of receivers [27], [16] while the proposed technique can achieve fairly accurate motion estimation with only two receivers and high accuracy with four receivers. Compared to the techniques based on environment referencing, such as in [20], [21], [22], [23], where sonar images represent estimates of the channel impulse response magnitudes varying in time, and thus ignoring the phase information, the proposed method is based on the whole information of the channel impulse response and thus is capable of providing a better motion estimation performance.…”

Coupling Rendering and Generative Adversarial Networks for Artificial SAS Image Generation

“…There is a growing demand for large-scale Synthetic Aperture Sonar (SAS) datasets. This demand stems from datadriven applications such as Automatic Target Recognition (ATR) [1]- [3], segmentation [4] and oceanographic research of the seafloor, simulation for sensor prototype development and calibration [5], and even potential higher level tasks such as motion estimation [6] and micronavigation [7]. Unfortunately, the acquisition of SAS data is bottlenecked by the costly deployment of SAS imaging systems, and even when data acquisition is possible, the data is often skewed towards containing barren seafloor rather than objects of interest.…”