AircraftWitness: How Often Aircraft Can Locate Recordings

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1. Apparenté Contexte plus large Pas pertinent

   A Search for Technosignatures Around 11,680 Stars with the Green Bank Telescope at 1.15-1.73 GHz

   Jean-Luc Margot, Megan G. Li, Pavlo Pinchuk et al. · 2023 · AJ 166 206 (2023) · cité 15 fois · Résumé IA disponible

   Cette recherche de technosignatures examine des signaux radio et leur détection, tandis qu’AircraftWitness exploite des signatures Doppler acoustiques pour localiser des enregistrements.

   Résumé scientifique

   We conducted a search for narrowband radio signals over four observing sessions in 2020-2023 with the L-band receiver (1.15-1.73 GHz) of the 100 m diameter Green Bank Telescope. We pointed the telescope in the directions of 62 TESS Objects of Interest, capturing radio emissions from a total of ~11,860 stars and planetary systems in the ~9 arcminute beam of the telescope. All detections were either automatically rejected or visually inspected and confirmed to be of anthropogenic nature. In this work, we also quantified the end-to-end efficiency of radio SETI pipelines with a signal injection and recovery analysis. The UCLA SETI pipeline recovers 94.0% of the injected signals over the usable f…

2. Apparenté Même méthode, autre problème Pas pertinent

   UAV's Rotor Micro-Doppler Feature Extraction Using Integrated Sensing and Communication Signal: Algorithm Design and Testbed Evaluation

   Jiachen Wei, Dingyou Ma, Feiyang He et al. · 2024 · Signal Processing · cité 24 fois · Pas encore résumé

   Cette étude extrait le micro-Doppler des rotors de drones pour les détecter par radar, tandis qu’AircraftWitness utilise des courbes Doppler acoustiques d’aéronefs.

   Résumé scientifique

   With the rapid application of unmanned aerial vehicles (UAVs) in urban areas, the identification and tracking of hovering UAVs have become critical challenges, significantly impacting the safety of aircraft take-off and landing operations. As a promising technology for 6G mobile systems, integrated sensing and communication (ISAC) can be used to detect high-mobility UAVs with a low deployment cost. The micro-Doppler signals from UAV rotors can be leveraged to address the detection of low-mobility and hovering UAVs using ISAC signals. However, determining whether the frame structure of the ISAC system can be used to identify UAVs, and how to accurately capture the weak rotor micro-Doppler sig…

3. Apparenté Contexte plus large Pas pertinent

   A deep-learning search for technosignatures of 820 nearby stars

   Peter Xiangyuan Ma, Cherry Ng, Leandro Rizk et al. · 2023 · Instrumentation and Methods for Astrophysics · cité 41 fois · Résumé IA disponible

   Cette recherche détecte des signaux radio à dérive Doppler autour d’étoiles, alors qu’AircraftWitness repère des trajectoires d’aéronefs à partir de signatures Doppler audio.

   Résumé scientifique

   The goal of the Search for Extraterrestrial Intelligence (SETI) is to quantify the prevalence of technological life beyond Earth via their "technosignatures". One theorized technosignature is narrowband Doppler drifting radio signals. The principal challenge in conducting SETI in the radio domain is developing a generalized technique to reject human radio frequency interference (RFI). Here, we present the most comprehensive deep-learning based technosignature search to date, returning 8 promising ETI signals of interest for re-observation as part of the Breakthrough Listen initiative. The search comprises 820 unique targets observed with the Robert C. Byrd Green Bank Telescope, totaling over…

4. Apparenté Autre approche du problème Pas pertinent

   Privacy-Preserving by Design: Indoor Positioning System Using Wi-Fi Passive TDOA

   Mohamed Mohsen, Hamada Rizk, Moustafa Youssef · 2023 · Artificial Intelligence · cité 10 fois · Résumé IA disponible

   PassiFi localise des appareils en intérieur par TDoA Wi-Fi passif, tandis qu’AircraftWitness localise des enregistrements extérieurs à l’aide de l’audio et d’ADS-B.

   Résumé scientifique

   Indoor localization systems have become increasingly important in a wide range of applications, including industry, security, logistics, and emergency services. However, the growing demand for accurate localization has heightened concerns over privacy, as many localization systems rely on active signals that can be misused by an adversary to track users' movements or manipulate their measurements. This paper presents PassiFi, a novel passive Wi-Fi time-based indoor localization system that effectively balances accuracy and privacy. PassiFi uses a passive WiFi Time Difference of Arrival (TDoA) approach that ensures users' privacy and safeguards the integrity of their measurement data while st…

5. Apparenté Contexte plus large Pas pertinent

   Retrieval in Long Surveillance Videos using User Described Motion and Object Attributes

   Greg Castanon, Mohamed Elgharib, Venkatesh Saligrama et al. · 2014 · Computer Vision and Pattern Recognition · cité 24 fois · Pas encore résumé

   Cette méthode retrouve des événements dans des vidéos de surveillance à partir du mouvement et des attributs d’objets, plutôt que de localiser des enregistrements audio avec ADS-B.

   Résumé scientifique

   We present a content-based retrieval method for long surveillance videos both for wide-area (Airborne) as well as near-field imagery (CCTV). Our goal is to retrieve video segments, with a focus on detecting objects moving on routes, that match user-defined events of interest. The sheer size and remote locations where surveillance videos are acquired, necessitates highly compressed representations that are also meaningful for supporting user-defined queries. To address these challenges we archive long-surveillance video through lightweight processing based on low-level local spatio-temporal extraction of motion and object features. These are then hashed into an inverted index using locality-s…

6. Apparenté Contexte plus large Pas pertinent

   Drone navigation and license place detection for vehicle location in indoor spaces

   Moa Arvidsson, Sithichot Sawirot, Cristofer Englund et al. · 2023 · Computer Vision and Pattern Recognition · cité 2 fois · Résumé IA disponible

   Cette étude cartographie des véhicules en intérieur grâce à un drone et à la détection de plaques, tandis qu’AircraftWitness localise des lieux d’enregistrements extérieurs.

   Résumé scientifique

   Millions of vehicles are transported every year, tightly parked in vessels or boats. To reduce the risks of associated safety issues like fires, knowing the location of vehicles is essential, since different vehicles may need different mitigation measures, e.g. electric cars. This work is aimed at creating a solution based on a nano-drone that navigates across rows of parked vehicles and detects their license plates. We do so via a wall-following algorithm, and a CNN trained to detect license plates. All computations are done in real-time on the drone, which just sends position and detected images that allow the creation of a 2D map with the position of the plates. Our solution is capable of…

7. Apparenté Contexte plus large Pas pertinent

   GPS-Spoofing Attack Detection Mechanism for UAV Swarms

   Pavlo Mykytyn, Marcin Brzozowski, Zoya Dyka et al. · 2023 · Cryptography and Security · cité 28 fois · Résumé IA disponible

   Cette étude détecte l’usurpation GPS visant des essaims de drones, un problème de navigation aérienne distinct de la localisation audio par ADS-B.

   Résumé scientifique

   Recently autonomous and semi-autonomous Unmanned Aerial Vehicle (UAV) swarms started to receive a lot of research interest and demand from various civil application fields. However, for successful mission execution, UAV swarms require Global navigation satellite system signals and in particular, Global Positioning System (GPS) signals for navigation. Unfortunately, civil GPS signals are unencrypted and unauthenticated, which facilitates the execution of GPS spoofing attacks. During these attacks, adversaries mimic the authentic GPS signal and broadcast it to the targeted UAV in order to change its course, and force it to land or crash. In this study, we propose a GPS spoofing detection mecha…

8. Apparenté Contexte plus large Pas pertinent

   Fast Vehicle Detection in Aerial Imagery

   Jennifer Carlet, Bernard Abayowa · 2017 · Computer Vision and Pattern Recognition · preprint, non relu par les pairs · cité 19 fois · Résumé IA disponible

   Cette étude détecte des véhicules dans des images aériennes, tandis qu’AircraftWitness recherche les lieux d’enregistrements audio au moyen de trajectoires d’aéronefs.

   Résumé scientifique

   In recent years, several real-time or near real-time object detectors have been developed. However these object detectors are typically designed for first-person view images where the subject is large in the image and do not directly apply well to detecting vehicles in aerial imagery. Though some detectors have been developed for aerial imagery, these are either slow or do not handle multi-scale imagery very well. Here the popular YOLOv2 detector is modified to vastly improve it's performance on aerial data. The modified detector is compared to Faster RCNN on several aerial imagery datasets. The proposed detector gives near state of the art performance at more than 4x the speed.