Robust and Efficient Strategies for Invader Drone Surveillance System Based on UAV Borne Radar Antenna Array

Abstract

The Invader Drone Surveillance System (IDSS) is a modern framework that enhances security and monitoring capabilities. It uses radar-equipped Unmanned Aerial Vehicles (UAVs)/drones to offer situational awareness in real-time for intelligence gathering. UAVs swift and agile nature allows for comprehensive coverage of expansive areas and remote locations. Real-time data processing and analysis enable quick decision-making and a proactive approach to security issues. The system uses various algorithms for effective categorization, localization, and tracking of prospective invaders or threats. The IDSS is a flexible and efficient system for protecting vital infrastructure, boosting border security, and assisting military and law enforcement activities. The dynamic nature of the IDSS makes it difficult to adjust with changing environments and scenarios, such as unpredictable weather patterns, varying terrains, and evolving threat landscapes. The research proposes the following techniques for a better solution by looking at these issues. •Due to the adaptability and agility, UAVs are increasingly used for surveillance. The success of surveillance activities, however, depends on the UAV’s ability to coordinate effectively with one another. In this regard, a bio-inspired technique is proposed to maintain the connectivity and coordination among a swarm of UAVs in the surveillance system. To improve the effectiveness and robustness of the UAV surveillance system, the proposed methodologies use the concepts of swarm intelligence, self-organization, and adaptive behaviours. •In order to determine the presence of an invader drone, it is essential to recognize and keep an eye on a variety of flying objects, such as drones, birds, and helicopters. In this regard, the Convolutional Neural Network-Memetic Algorithm (CNN-MA) technique is proposed. The proposed algorithm categorizes and analyses the flying object based on the Micro-Doppler Signature (MDS) data obtained by UAV-mounted radar at different angles. Experiment results show how well the CNN-MA based classification strategy performs in reliably identifying and classifying various flying objects, offering useful information for improving UAV surveillance systems.• After identifying UAVs from other flying objects, the distinction between the surveillance and invader UAVs is crucial. In this work, the bandstop filter filters out the surveillance UAV data from the invader. Further localization of invader UAVs is done in a surveillance system utilizing an adaptable (reconfigurable) radar antenna array (ARAA). Effective surveillance, monitoring, and situational awareness depend on accurate and trustworthy UAV localization. The proposed method uses reconfigurable radar technology to improve the invader UAV localization, ranging, and detecting accuracy—even in challenging circumstances. The reconfigurable radar-based localization method achieves excellent precision and robustness, paving the way for improved UAV surveillance system performance in various real-world circumstances. •Towards the continuous monitoring of the invader UAV, tracking is very important. For this, the Hybrid Unscented Kalman-Continuous Ant Colony Filter (HUK-CACF) is used to investigate the tracking of invader UAVs. The proposed method applies the HUK-CACF algorithm to estimate the UAV’s position, velocity, and other state variables. The HUK-CACF is highly suited for UAV tracking because of its capability to handle nonlinear dynamics. •Finally, the last work focuses on implementing cryptographic methods to secure data transfer in a UAV surveillance system. Sensitive data transfer in UAV surveillance activities, such as patrolling UAV locations, sensor readings, control orders, etc., requires strong security against unauthorized access and alteration. The efficiency and performance of the cryptographic techniques are assessed through simulations, revealing their capacity to protect data integrity, secrecy, and authenticity in UAV surveillance systems. The results of this research will aid in creating reliable and secure data transfer methods that improve the security and privacy of UAV surveillance operations.