Drones, with their ability to vertically take off and land with their stable hovering performance, are becoming favorable in both civilian and military domains. However, this introduces risks of its misuse, which may include security threats to airports, institutes of national importance, VIP security, drug trafficking, privacy breaches, etc. To address these issues, automated drone detection systems are essential for preventing unauthorized drone activities. Real-time detection requires high-performance devices such as GPUs. For our experiments, we utilized the NVIDIA Jetson Nano to support YOLOv9-based drone detection. The performance evaluation of YOLOv9 to detect drones is based on metrics like mean average precision (mAP), frames per second (FPS), precision, recall, and F1-score. Experimental data revealed significant improvements over previous models, with a mAP of 95.7%, a precision of 0.946, a recall of 0.864, and an F1-score of 0.903, marking a 4.6% enhancement over YOLOv8. This paper utilizes YOLOv9, optimized with pre-trained weights and transfer learning, achieving significant accuracy in real-time drone detection. Integrated with the NVIDIA Jetson Nano, the system effectively identifies drones at altitudes ranging from 15 feet to 110 feet while adapting to various environmental conditions. The model’s precision and adaptability make it particularly suitable for deployment in security-sensitive areas, where quick and accurate detection is crucial. This research establishes a solid foundation for future counter-drone applications and shows great promise for enhancing situational awareness in critical, high-risk environments.

Uncrewed aerial vehicles (UAVs) are increasingly deployed across critical applications, including disaster response, surveillance, and logistics. However, drone crashes caused by battery failures and undetected prefailure anomalies remain a major concern, particularly in endurance-demanding missions. This article presents a diagnostic approach for identifying battery-related anomalies by analyzing sensor and telemetry data from hexacopter flight logs. A detailed mathematical model of the UAV’s motion dynamics is developed to understand the system’s behavior under stable and stressful conditions. The study highlights the role of key onboard sensors, such as current sensors, thermistors, barometric altimeters, and inertial measurement units, in power monitoring and precrash diagnostics. Three flight scenarios are analyzed: normal operation, operation under wind disturbance, and battery-induced crashes. Results demonstrate that subtle irregularities in current draw, voltage fluctuations, and sensor anomalies can serve as early indicators of system instability. By benchmarking nominal behavior and correlating it with real-time flight data, the proposed method enables proactive detection of failure signatures, improving UAV reliability and safety. This work contributes to the development of sensor-integrated health monitoring systems for autonomous drones.

This paper presents an experimental investigation into the vulnerabilities of autonomous drone navigation systems under GPS spoofing attacks using Software Defined Radio (SDR). A low-cost spoofing framework was implemented using HackRF One and GPS-SDR-SIM to manipulate the navigation signals of an F450 quadcopter equipped with a Pixhawk 4 flight controller and a Ublox NEO-M8N GPS module. Both static and dynamic spoofing trials were conducted in controlled outdoor environments. During normal autonomous waypoint missions, the drone demonstrated stable sensor outputs and accurate trajectory tracking. However, under spoofed GPS conditions, the drone deviated from its intended path, breached geofenced boundaries, and exhibited unstable behaviour leading to an uncontrolled descent and crash landing instead of executing the intended Return-to-Launch (RTL) response. Detailed analysis of barometric altitude, yaw angle, magnetometer, vibration data, as well as GPS indicators such as satellite count and HDOP, revealed significant anomalies during spoofed flights. To enhance interpretability, a telemetry-based detection indicator was introduced by computing deviation thresholds between GPS, IMU, and barometric readings. These statistical correlations enabled the detection of spoofing-induced anomalies with over 90% reliability, offering a low-cost, platform-independent method for UAV spoofing awareness. These deviations underscore the susceptibility of GPS-dependent UAV systems to spoofing attacks. The study further demonstrates that cross-sensor anomaly detection without requiring additional hardware can serve as an effective strategy for real-time spoofing detection. This work contributes to a lightweight, platform-independent methodology for enhancing drone resilience against GPS spoofing threats through telemetry-based sensor fusion analytics.

This paper focuses on the implementation of Meshtastic firmware, showcasing its effectiveness in establishing a decentralized mesh network for communication and location tracking in areas where internet connectivity is unreliable. A thorough analysis of the system's features, including real-time device tracking and secure messaging, highlights its significant potential for military and surveillance applications in remote or off-grid settings. The decentralized mesh framework enhances resilience in dynamic environments, providing a versatile communication solution where conventional infrastructure is lacking or compromised. The experiments successfully demonstrate the real-world application of LoRa communication technology in urban settings, achieving dependable communication between sender and receiver boards over distances of up to 2 kilometers, even in the presence of potential urban interference. Additionally, this research highlights security protocols and ethical considerations relevant to the deployment of Meshtastic firmware in military and surveillance scenarios, offering insightful guidance for future studies and practical implementations.

This paper presents the first in-flight evaluation of the Indian Regional Navigation Satellite System (IRNSS), known as NavIC, integrated on an Unmanned Aerial Vehicle (UAV), commonly referred to as a drone. A comparative analysis was conducted between a NavIC-based and a conventional GPS-based navigation setup under identical environmental and flight conditions. The experimental platform utilised a Pixhawk flight controller with autonomous waypoints configured via Mission Planner. Due to hardware incompatibilities, an Arduino Uno was employed to convert NavIC outputs into standard NMEA format compatible with the UAV's flight system. Flight trials were conducted in open-sky conditions to assess parameters such as satellite count, fix status, time to first fix (TTFF), horizontal dilution of precision (HDOP), flight speed, and trajectory accuracy. Results indicate that while GPS offered quicker fix times and higher satellite visibility, NavIC exhibited better trajectory adherence, lower mean and maximum positional errors, and more consistent flight paths. These findings confirm the viability of NavIC as a promising alternative for drone-based real-time navigation in regional applications.