In this work, the radar processing technique of Range-Doppler processing is investigated for its potential to enhance performance of the radar-encoded laser system in rangefinding applications. One of the challenges experienced by this system is in discriminating between the returns from underwater objects and environmental clutter in highly-scattering and/or low signal-to-noise ratio conditions. The intention of this work is to investigate whether the addition of a new dimension, velocity, will improve ranging performance. This work presents the application of the Range-Doppler processing technique to transform data collected in a laboratory water tank. Results and performance improvements using the radar-encoded laser system are compared against those obtained with a conventional, short-pulse laser.
In this work, we investigate the use of a radar processing technique to enhance detection performance of a short pulse laser system. One of the challenges experienced by this system is discriminating between the return from an underwater object and the clutter return caused by backscatter in highly-scattering and/or low signal-to-noise ratio (SNR) conditions. Taking inspiration from the radar processing community, we apply the Range-Doppler processing transform to data we collect in our laboratory water tank. This work will present the modified Range-Doppler technique, provide laboratory test tank results, and demonstrate performance improvements achieved using the modified Range-Doppler technique.
In this work we investigate the use of pattern classification algorithms to enhance detection performance of the underwater radar-encoded laser system. A challenge encountered with this system is the automatic detection of the return from an underwater object in highly-scattering and/or low signal-to-noise ratio (SNR) conditions. Previous efforts were largely based on threshold detection and result in detection errors in such challenging conditions. Other efforts attempt to use signal processing to remove scatter returns, but this does not address low SNR cases. We take a different approach here, investigating the use of machine learning to develop classifiers which combine various shape and statistical features to discriminate between object and non-object returns. Such pattern classifiers are commonly used in a variety of applications; the novelty in this work is applying such techniques to the problem of automatic object detection in a degraded visual environment, namely turbid water. We describe our framework and features, then demonstrate the performance of three pattern classification detectors using a series of test data collected in a variety of water conditions in a laboratory test tank. All three pattern classification detectors outperform a standard detection method. There are subtle performance differences between the classifiers that may result in application-specific tradeoff considerations.
Communication in maritime environments presents unique challenges often requiring the secure transfer of information over long distances in a complex dynamic environment. Here a system for generating orbital angular momentum (OAM) beams, multiplexing, transmitting, and demultiplexing using a convolutional neural network (CNN) is presented. A single input from a 1550 nm seed laser is amplified, split into four separate beams that are directed and modulated by four switches, and the resulting beams directed into phase plates to create beams carrying OAM. These four beams constitute the individual channels. The beams are passed through several optical elements, coherently combined, and transmitted to a receiver at a range of 12 m. The resulting OAM beam spatial patterns are captured using a high speed short-wave infrared detector, concurrently transmitted to a workstation for storage, and processed in real-time using a trained CNN. Results from short range and quiescent environmental state show a pattern detection accuracy of <99%.
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