We present a novel approach to underwater fleet communication using single-photon avalanche diode (SPAD) arrays on the receiver. Using a large array and a focusing lens, the array can be spatially allocated to different sectors within the lens’s field of view. This eliminates the need for precise pointing of the receiver towards the transmitter and enables simultaneous reception of multiple transmitters across the entire array. We demonstrate the feasibility of our approach through a prototype receiver that utilizes a 7x7 SPAD array, a wide field-of-view lens and FPGA processing. We were able to achieve a raw data rate of 6 Mbps across a folded 25m clear water channel. Through the use of artificial attenuation with ND filters, we estimate that the achievable distance for a raw data rate of 6 Mbps in clear water with a BER of 10-3 is approximately 400m. In over-the-air testing, we were able to achieve simultaneous communication over the entire 7x7 array at 6 Mbps.
Researching gun muzzle flash detection can be costly and time-consuming, as data collection requires specialized equipment to be set up at various ranges and angles. This process is further complicated by the need to hire licensed weapon handlers for each weapon class, and by the scarcity of shooting ranges. To address this, we propose a novel approach that uses Generative Adversarial Networks (GANs) to speed up the research process. Specifically, we train a deep convolutional GAN (DCGAN) to generate synthetic muzzle flash waveforms, which can then be used to augment limited training data for deep-learning classifier models. We evaluate the performance of the DCGAN using a lightweight deep-learning model based on ResNet and explore the possibility of re-purposing the trained discriminator as a classifier.
KEYWORDS: Single photon avalanche diodes, Field programmable gate arrays, Detection and tracking algorithms, Data modeling, Statistical modeling, Deep learning, Data processing, Computing systems
Gun muzzle flash produces characteristic signatures on the 766nm and 769nm wavelengths that can be passively picked up from a distance using ultra-sensitive SPAD arrays for immediate localization. Sifting through the massive number of pulses generated by the arrays in real-time however poses a challenge, especially when deep-learning models are used for classification. We present a novel FPGA-based expandable system consisting of a two-tier detection architecture that decouples the computationally-intensive deep-learning model from the data rate intensive SPAD arrays. Our slope-based first tier algorithm provides an FPGA-efficient first-look filter and our ResNet-based deep-learning model provides high sensitivity across different lighting conditions while maintaining high specificity in the face of potential false positives in an urban environment. The deep-learning model was trained with synthetic datasets generated from small samples of gun muzzle flashes from various weapons and ammunition types available to us, and sources of likely false positives in an urban environment. In testing, our system achieves a detection rate of 99.8%, 99.9% specificity and 99.6% sensitivity for shots fired from distances between 50 to 450m.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.