Bio-neural navigation and intelligent intrusion detection for uav systems

A method for integrating bio-neural navigation with intelligent intrusion detection for UAV and nano drone systems, comprising:

• Receiving real-time visual inputs and extracting navigational features using a CNN-based feature extraction network to recognize visual cues, detect objects, and analyze optic flow patterns;
• Updating an internal orientation representation using a bio-inspired ring attractor network that maintains stable navigation based on visual and sensory cues from the environment;
• Detecting potential intrusions by analyzing multi-sensor data through a hybrid neural network combining CNNs and Recurrent Neural Networks (RNNs) for temporal analysis and threat prediction;
• Performing real-time fusion of navigation and intrusion detection data to dynamically adjust the UAV's trajectory and initiate evasive maneuvers in response to detected threats or obstacles in diverse environments, including GPS-denied regions or indoor spaces.

A UAV navigation system for autonomous operation in complex environments, comprising:

• A neural network-based visual processing unit that detects optic flow and extracts high-level navigational features from real-time camera inputs to identify objects and obstacles;
• An artificial head direction network that maintains an internal representation of the UAV's orientation relative to visual landmarks, enabling stable navigation in GPS-denied environments;
• A multi-modal intrusion detection system that integrates data from radar, LiDAR, and acoustic sensors, providing a comprehensive threat assessment based on environmental inputs;
• An adaptive response system that employs a deep reinforcement learning model to update navigation paths and execute evasion strategies based on detected intrusions or obstacles.

A method for bio-neural navigation and intelligent intrusion detection in nano drones, comprising:

• Training a CNN-based navigation model on biological optic flow datasets to replicate insect-inspired navigation mechanisms and visual processing;
• Implementing a ring attractor network for orientation stability, which updates its state based on visual and gyroscopic sensor inputs to maintain heading direction and positional awareness;
• Training a hybrid neural network model for intrusion detection using labeled datasets of known UAV or nano drone intrusions and anomalies, and employing clustering techniques to identify new or unknown threats;
• Integrating real-time intrusion detection outputs with the navigation module to autonomously adjust the UAV's flight parameters, enabling real-time collision avoidance and threat evasion in dynamic environments.