The Invader Unmanned Aerial Vehicle (UAV) Surveillance System (IUAVSS) is a cutting-edge technology, which is designed to enhance security and monitoring capabilities. It uses radar-equipped UAVs, commonly referred to as drones, to offer situational awareness in real time for intelligence gathering. For effective categorization, localisation, and tracking of prospective intruders or threats, the system uses a variety of algorithms. Large areas and hard-to-reach places can be effectively covered by the UAVs&rsquo fast deployment and agile manoeuvrability. Real-time data processing and analysis allows for quick decision-making and a pro-active approach to security issues. The IUAVSS is a flexible and efficient method for protecting vital infrastructure, boosting border security, and assisting military and law enforcement activities.
The dynamic nature of the IUAVSS presents challenges in terms of adapting to changing environments and scenarios. UAVs require to quickly respond to dynamic conditions such as unpredictable weather patterns, varying terrains, and evolving threat landscapes. Additionally, environmental factors like strong winds, extreme temperatures, and limited visibility can impact flight stability, sensor performance, and data accuracy. By looking at these issues, the research work proposes the following techniques for a better solution.
(i) Due to the adaptability and UAVs are increasingly being used for surveillance purposes. The success of surveillance activities, however, depends on the UAVs&rsquo ability to communicate effectively with one another. In this regard, a bio-inspired technique is proposed to maintain the connectivity and coordination among swarm of UAVs in the surveillance system. To improve the overall effectiveness and robustness of the UAV surveillance system, the proposed methodologies make use of the concepts of swarm intelligence, self-organization, and adaptive behaviours.
(ii) In order to determine the presence of intruder drone, it is essential for recognizing and keeping an eye on a variety of flying objects, such as drones, birds, and aeroplanes. In this regard, Convolutional Neural Network-Memetic Algorithm (CNN-MA) techniques is proposed. The proposed algorithm categorizes and analyse the flying object based on the Micro-Doppler Signature (MDS) data obtained by UAV mounted radar at different angles. Results from experiments show how well the CNN-MA based classification strategy performs in reliably identifying and classifying a variety of flying objects, offering useful information for improving UAV surveillance systems.
(iii) After identification of UAVs from other flying object, demarcation between the surveillance and invader UAV is crucial. In this work, Band Stop Filter is used to filter out the surveillance UAV data, from the intruder. Further localization of invader UAV is done in a surveillance system utilising an adaptable (reconfigurable) radar. Effective surveillance, monitoring, and situational awareness depend on accurate and trustworthy UAV localization. The proposed method makes use of reconfigurable radar technology to improve the intruder UAV localization, ranging, and detecting accuracy&mdasheven in challenging circumstances. The reconfigurable radar-based localization method achieves excellent precision and robustness, paving the door for improved UAV surveillance system performance in a variety of real-world circumstances.
(iv) Towards the continuous monitoring of the invader UAV, very tracking is important. For this, the Hybrid Unscented Kalman-Continuous Ant Colony Filter (HUK-CACF) is used to investigate the tracking of invader UAV. The proposed method applies the HUK-CACF algorithm to estimate the UAV&rsquos position, velocity, and other state variables. The HUK-CACF is highly suited for UAV tracking because to its capability to handle nonlinear dynamics.
(v) 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 pictures, sensor readings, and control orders, requires strong security against unauthorised access and alteration. Through simulations, the efficiency and performance of the cryptographic techniques are assessed, revealing their capacity to protect data integrity, secrecy, and authenticity in UAV surveillance systems. The results of this research will aid in the creation of reliable and secure data transfer methods that improve the security and privacy of UAV surveillance operations.