INS/GNSS brain-inspired positioning based on three-dimensional periodic grid cell information fusion model for UAVs

Purpose First, the head-direction cell model, using a continuous attractor neural network, integrates self-motion cues [e.g. information from inertial navigation systems (INS)] and external perception information [e.g. information from global navigation satellite systems (GNSS)] to generate precise firing rates, which determine the yaw angle. Second, a three-dimensional periodic grid cell information fusion model is proposed, designed to efficiently integrate the decoded yaw angle, self-motion cues and external perception information. The external perception information is encoded using a Gaussian function and subsequently integrated into the original grid cell model as an additional activity. Finally, a method for decoding the periodic firing rates of grid cells is introduced, enabling the precise determination of the specific positions of quadrotor aircrafts. Design/methodology/approach Most existing brain-inspired navigation models rely on vision as the primary source of information; however, other sensors can also provide spatial position perception. This study aims to enhance the compatibility of brain-inspired navigation models with unmanned aerial vehicles equipped with universal sensors, such as INS and GNSS, by proposing a novel INS/GNSS brain-inspired positioning model. Findings The INS/GNSS brain-inspired positioning model uses navigation information from INS and GNSS to determine positions and enhance the navigation system’s positioning performance. Originality/value The proposed model serves as a valuable reference for the development of brain-inspired navigation works and expands the ideas for novel unmanned aerial vehicle navigation methods.