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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

Digitally controlled sonars

Sonars are usually designed and constructed as stand alone instruments. That is, all elements or subsystems of the sonar are provided: power conditioning, displays, intercommunications, control, receiver, transmitter, and transducer. The sonars which are a part of the Advanced Ocean Test Development Platform (AOTDP) represent a departure from this manner of implementation and are configured more like an instrumentation system. Only the transducer, transmitter, and receiver which are unique to a particular sonar function; Up, Down, Side Scan, exist as separable subsystems. The remaining functions are reserved to the AOTDP and serve all sonars and other instrumentation in a shared manner. The organization and functions of the common AOTDP elements were described and then the interface with the sonars discussed. The techniques for software control of the sonar parameters were explained followed by the details of the realization of the sonar functions and some discussion of the performance of the side scan sonars.

Hansen, G. R.↗
DOE OSTI2025

Towards automated and real-time multi-object detection of anguilliform fishes from sonar data using YOLOv8 deep learning algorithm

Eels (Anguilla spp.), including American eels (Anguilla rostrata), European eels (Anguilla anguilla), and Japanese eels (Anguilla japonica), are species of critical management and regulatory concern due to their vulnerability to various stressors during downstream migrations. Accurate and efficient detection of migrating eels can improve our understanding of fish behaviors and fish-hydraulic structure interactions, thereby facilitating the design, operation, and optimization of more effective downstream passage facilities from both biological and economic perspectives. However, a real-time, automated framework for detecting migrating eels in real-world applications is currently lacking. Leveraging imaging sonar as a reliable technology for fish passage monitoring, field data are acquired using imaging sonar and then converted to single sonar frames/images for subsequent analysis. In this study, a framework based on the You Only Look Once Version 8 (YOLOv8)-based convolutional neural network is proposed for multi-object detection of eels and non-eel fish using the sonar images after image subtraction and additional wavelet denoising. The results from both training and testing phases demonstrate that the framework's ability can successfully detect both eels and non-eel fish in preprocessed sonar images, achieving F1-scores and mAP@0.50 exceeding 0.84. Additionally, the incorporation of wavelet denoising during preprocessing slightly improve detection performance. Furthermore, the transferability of this framework from eel to lamprey detection is demonstrated to be feasible given the similar morphological characteristics of these two species. Overall, the proposed framework achieves accurate and efficient detection of migrating eels, providing reliable and real-time information that can help conserve vulnerable eel and eel-like populations.

Deep learning↗
NASA NTRS2011

Testing of a Composite Wavelet Filter to Enhance Automated Target Recognition in SONAR

Automated Target Recognition (ATR) systems aim to automate target detection, recognition, and tracking. The current project applies a JPL ATR system to low resolution SONAR and camera videos taken from Unmanned Underwater Vehicles (UUVs). These SONAR images are inherently noisy and difficult to interpret, and pictures taken underwater are unreliable due to murkiness and inconsistent lighting. The ATR system breaks target recognition into three stages: 1) Videos of both SONAR and camera footage are broken into frames and preprocessed to enhance images and detect Regions of Interest (ROIs). 2) Features are extracted from these ROIs in preparation for classification. 3) ROIs are classified as true or false positives using a standard Neural Network based on the extracted features. Several preprocessing, feature extraction, and training methods are tested and discussed in this report.

image processing↗
DOE OSTI2024

Deep Learning for Fish Identification from Sonar Data (CRADA 481 Final Report)

In eastern regions of the United States, the American eel is a species of management and regulatory concern because of significant population declines, despite the species’ previous abundance in all tributaries of rivers flowing into the Atlantic Ocean. The American eel is also a candidate for listing under the U.S. Endangered Species Act. While hydropower construction and operation are only one of several factors contributing to this population decline, such a listing could impose additional regulatory challenges for a large number of hydropower projects. In this CRADA project, we improved technologies for identifying migrating eels with the goal of reducing the cost and time required for future American eel hydropower impact assessment and mitigation studies, while maintaining accuracy. We built on results from a previous FOA project (FOA# DE-FOA-0001662), led by the Electric Power Research Institute (EPRI), which developed a highly accurate, deep-learning method for identifying migrating eels from imaging sonar data. The current study aimed to further optimize this deep-learning model, originally designed for image classification, and to develop an object detection software capable of identifying fish from sonar videos in real time, enabling the detection of events like fish migrations and specific species, such as the American eel, at hydropower dams. The data conversion algorithms were packaged as software with a graphical user interface, and the software is evaluated by external collaborators. We focused on the American eel in this project and explored the transferability of the developed deep learning models to the sea lamprey, given the similar body shape and swimming behavior between the two species.

13 HYDRO ENERGY↗
NASA NTRS1984

Color and Grey Scale in Sonar Displays

In spite of numerous publications 1 it is still rather unclear, whether color is of any help in sonar displays. The work presented here deals with a particular type of sonar data, i.e., LOFAR-grams (low frequency analysing and recording) where acoustic sensor data are continuously written as a time-frequency plot. The question to be answered quantitatively is, whether color coding does improve target detection when compared with a grey scale code. The data show significant differences in receiver-operating characteristics performance for the selected codes. In addition it turned out, that the background noise level affects the performance dramatically for some color codes, while others remain stable or even improve. Generally valid rules are presented on how to generate useful color scales for this particular application.

Kraiss, K. F.↗
NASA NTRS2004

Controllable Sonar Lenses and Prisms Based on ERFs

Sonar-beam-steering devices of the proposed type would contain no moving parts and would be considerably smaller and less power-hungry, relative to conventional multiple-beam sonar arrays. The proposed devices are under consideration for installation on future small autonomous underwater vehicles because the sizes and power demands of conventional multiple-beam arrays are excessive, and motors used in single-beam mechanically scanned systems are also not reliable. The proposed devices would include a variety of electrically controllable acoustic prisms, lenses, and prism/lens combinations both simple and compound. These devices would contain electrorheological fluids (ERFs) between electrodes. An ERF typically consists of dielectric particles floating in a dielectric fluid. When an electric field is applied to the fluid, the particles become grouped into fibrils aligned in rows, with a consequent increase in the viscosity of the fluid and a corresponding increase in the speed of sound in the fluid. The change in the speed of sound increases with an increase in the applied electric field. By thus varying the speed of sound, one varies the acoustic index of refraction, analogously to varying the index of refraction of an optical lens or prism. In the proposed acoustic devices, this effect would be exploited to control the angles of refraction of acoustic beams, thereby steering the beams and, in the case of lenses, controlling focal lengths.

Bar-Cohen, Yoseph↗
NASA NTRS1975

Miniature sonar fish tag

Self-powered sonar device may be implanted in body of fish. It transmits signal that can be detected with portable tracking gear or by automatic detection-and-tracking system. Operating life of over 4000 hours may be expected. Device itself may be used almost indefinitely.

Lovelady, R. W.↗
NASA NTRS1975

Graphical derivations of radar, sonar, and communication signals

The designer of a communication system often has knowledge concerning the changes in distance between transmitter and receiver as a function of time. This information can be exploited to reduce multipath interference via proper signal design. A radar or sonar may also have good a priori information about possible target trajectories. Such knowledge can again be used to reduce the receiver's response to clutter (MTI), to enhance signal-to-noise ratio, or to simplify receiver design. There are also situations in which prior knowledge about trajectories is lacking. The system should then utilize a single-filter pair which is insensitive to the effects induced by relative motion between transmitter, receiver, and reflectors. For waveforms with large time-bandwidth products, such as long pulse trains, it is possible to graphically derive signal formats for both situations (trajectory known and unknown). Although the exact form of the signal is sometimes not specified by the graphical procedure, the problem in such cases is reduced to one which has already been solved, i.e., the generation of an impulse equivalent code.

Altes, R. A.↗
NASA NTRS1978

Static analysis of a sonar dome rubber window

The application of NASTRAN (level 16.0.1) to the static analysis of a sonar dome rubber window (SDRW) was demonstrated. The assessment of the conventional model (neglecting the enclosed fluid) for the stress analysis of the SDRW was made by comparing its results to those based on a sophisticated model (including the enclosed fluid). The fluid was modeled with isoparametric linear hexahedron elements with approximate material properties whose shear modulus was much smaller than its bulk modulus. The effect of the chosen material property for the fluid is discussed.

Lai, J. L.↗
NASA NTRS1982

Echo tracker/range finder for radars and sonars

An echo tracker/range finder or altimeter is described. The pulse repetition frequency (PFR) of a predetermined plurality of transmitted pulses is adjusted so that echo pulses received from a reflecting object are positioned between transmitted pulses and divided their interpulse time interval into two time intervals having a predetermined ratio with respect to each other. The invention described provides a means whereby the arrival time of a plurality of echo pulses is defined as the time at which a composite echo pulse formed of a sum of the individual echo pulses has the highest amplitude. The invention is applicable to radar systems, sonar systems, or any other kind of system in which pulses are transmitted and echoes received therefrom.

Constantinides, N. J.↗
NASA NTRS2011

Optimization of Adaboost Algorithm for Sonar Target Detection in a Multi-Stage ATR System

JPL has developed a multi-stage Automated Target Recognition (ATR) system to locate objects in images. First, input images are preprocessed and sent to a Grayscale Optical Correlator (GOC) filter to identify possible regions-of-interest (ROIs). Second, feature extraction operations are performed using Texton filters and Principal Component Analysis (PCA). Finally, the features are fed to a classifier, to identify ROIs that contain the targets. Previous work used the Feed-forward Back-propagation Neural Network for classification. In this project we investigate a version of Adaboost as a classifier for comparison. The version we used is known as GentleBoost. We used the boosted decision tree as the weak classifier. We have tested our ATR system against real-world sonar images using the Adaboost approach. Results indicate an improvement in performance over a single Neural Network design.

Adaboost↗
NASA NTRS1985

Planning for VRM: Radar and sonar studies of volcanic terrains on Earth, Venus and Mars

Venera 15 and 16 radar images of Venus, together with Earth based data from the Arecibo Observatory, indicate that volcanism has played an important role in the evolution of the Venusian landscape. At the end of this decade, NASA's Venus Radar Mapper (VRM) spacecraft will return near global information that will further constrain the planet's geologic history. Due to the diversity of volcano/tectonic features that have already been identified on Venus, and the intrinsic differences between radar images and conventional photography, additional expertise is being developed with which to interpret the VRM images of this unusual environment. Several attempts to better understand the physical characteristics of volcanic terrains are described here. Pioneer Venus radar altimeter measurements of topographic variability and surface roughness are compared with Goldstone radar measurements of volcanic terrains on Mars. Synthetic aperture radar images obtained by the SIR-B Space Shuttle experiment over Kilauea Volcano, Hawaii, are employed to investigate the differences in radar returns from pahoehoe, aa and sheet lava flows. Four polarization, multiple incidence angle, aircraft radar images of the Medicine Lake area of N. California are used to address the unusually high cross-polarization ratio of lobate flows around Beta Regio on Venus, as measured by the Arecibo radar.

Mouginis-Mark, P. J.↗
NASA NTRS1994

Mobile robot exploration and navigation of indoor spaces using sonar and vision

Integration of skills into an autonomous robot that performs a complex task is described. Time constraints prevented complete integration of all the described skills. The biggest problem was tuning the sensor-based region-finding algorithm to the environment involved. Since localization depended on matching regions found with the a priori map, the robot became lost very quickly. If the low level sensing of the world is not working, then high level reasoning or map making will be unsuccessful.

Kortenkamp, David↗
NASA NTRS1985

Sonar Locator Systems

An underwater locator device called a Pinger is attached to an airplane's flight recorder for recovery in case of a crash. Burnett Electronics Pinger Model 512 resulted from a Burnett Electronics Laboratory, Inc./Langley Research Center contract for development of a search system for underwater mines. The Pinger's battery-powered transmitter is activated when immersed in water, and sends multidirectional signals for up to 500 hours. When a surface receiver picks up the signal, a diver can retrieve the pinger and the attached airplane flight recorder. Other pingers are used to track whales, mark underwater discoveries and assist oil drilling vessels.

Source record↗