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ABSTRACT The increasing use of unmanned vehicles (UVs) in defense and security operations has significantly enhanced real-time surveillance and threat detection capabilities. However, insurgency monitoring in dynamic and hostile environments presents challenges that require adaptive decision-making and real-time intelligence. This study explores the integration of Deep Reinforcement Learning (DRL) in Unmanned Vehicle Intelligence (UVI) to enable autonomous adaptive threat response in insurgency-prone areas. The proposed system leverages Deep Q-Networks (DQN) and Policy Gradient methods to train UVs in detecting, analyzing, and responding to insurgent activities based on multi-sensor data, including thermal imaging, motion tracking, and acoustic signals. By incorporating sensor fusion techniques and real-time environmental learning, the system enhances situational awareness and optimizes decision-making processes in uncertain and rapidly changing battle conditions. The DRL framework enables the UV to dynamically adjust patrol routes, evade obstacles, and differentiate between hostile and non-hostile entities while minimizing false alerts. Simulation results demonstrate improved threat identification accuracy, reduced response time, and enhanced mission success rates compared to traditional rule-based surveillance models. This research contributes to the development of autonomous and intelligent UVs capable of performing adaptive threat response in real-time, thereby strengthening counter-insurgency operations. Future work includes hardware implementation and real-world testing in complex terrains to further validate the effectiveness of the proposed model.INDEX TERMS: Unmanned Vehicle Intelligence, Deep Reinforcement Learning, Adaptive Threat Response, Insurgency Monitoring, Sensor Fusion, Deep Q-Networks (DQN) 1.INTRODUCTIONThe rapid advancement of Artificial Intelligence (AI) and autonomous systems has revolutionized the use of Unmanned Vehicles (UVs) in modern military and security operations. In regions affected by insurgency, the need for real-time threat detection, surveillance, and adaptive response mechanisms has become increasingly critical. Traditional surveillance and counter-insurgency measures often rely on manual monitoring, pre-programmed patrol routes, and static rule-based threat detection systems, which are inefficient in dynamic and unpredictable environments (Sutton & Barto, 2018). To address these limitations, Deep Reinforcement Learning (DRL) has emerged as a powerful tool for enhancing the intelligence of unmanned vehicles, enabling them to learn from environmental interactions and autonomously respond to emerging threats (Mnih et al., 2015).Deep Reinforcement Learning in Unmanned Vehicles (DRL) is a branch of machine learning where an agent learns optimal policies by interacting with its environment and receiving reward-based feedback. Unlike traditional supervised learning methods, DRL allows UVs to continuously adapt and improve their decision-making process in real-time (Arulkumaran et al., 2017). This is particularly beneficial for insurgency monitoring, where threat landscapes are constantly evolving. By integrating DRL with multi-sensor fusion techniques such as thermal imaging, motion detection, acoustic sensing, and LiDAR-based object recognition UVs can autonomously detect, track, and classify potential threats, minimizing human intervention and enhancing operational efficiency (Gu et al., 2017).Conventional UV surveillance systems often rely on static algorithms and predefined response mechanisms, which are inadequate in highly dynamic battle zones (Kendall et al., 2019). Additionally, these systems may struggle with false positive detections, leading to inefficient resource deployment and mission failures. The complexity of urban and forested terrains further complicates navigation, necessitating an intelligent system capable of autonomous path planning, obstacle avoidance, and adaptive threat engagement (Silver et al., 2016). DRL-based approaches address these challenges by allowing UVs to develop context-aware threat identification and strategic decision-making skills, improving their ability to respond to insurgent activities in real time.Recent studies have demonstrated the effectiveness of Deep Q-Networks (DQN) and Policy Gradient methods in military surveillance and autonomous decision-making (Lillicrap et al., 2016). DQNs enable UVs to evaluate multiple response strategies and select the optimal action based on a Q-value function, while policy gradient techniques refine long-term strategy planning by optimizing neural network-based policies (Schulman et al., 2017). When combined with sensor fusion technologies, DRL enhances the situational awareness of unmanned vehicles, improving their ability to differentiate between hostile and non-hostile entities and adjust patrol routes dynamically.The integration of DRL in unmanned vehicle intelligence represents a significant breakthrough in autonomous defense technologies. By enabling UVs to self-learn and adapt to insurgency threats, this research aims to enhance counter-insurgency operations, reduce risks to human personnel, and improve surveillance efficiency in high-risk areas (Haarnoja et al., 2018). Furthermore, this study provides a foundation for future developments in autonomous military robotics, with potential applications in border security, anti-terrorism efforts, and disaster response missions.

Copyright © 2025 Nwabueze Charles Nnaemeka. This is an open access article distributed under the Creative Commons Attribution License.