Improving the performance of UAV navigation systems using adaptive signal processing algorithms and deep learning

UAV navigation systems in dynamic and complex environments face numerous challenges, including signal noise, multipath interference, and dependence on external infrastructures such as GPS. This paper introduces a novel hybrid framework that integrates adaptive signal processing algorithms (such as LMS and RLS) and deep learning models (including CNN and LSTM) to improve the accuracy, robustness, and efficiency of UAV navigation systems. In this research, adaptive algorithms are used to reduce noise and improve the quality of received signals in real time, while deep neural networks are responsible for integrating multi-sensor data and predicting the optimal path. Experimental results in simulated and real environments show that the proposed system reduces the positioning error by 40% compared to Kalman filter-based methods and optimizes the consumption of computational resources by 25%. This hybrid approach bridges model-based and data-driven methods and has the potential to be implemented in critical applications such as search and rescue, environmental monitoring, and urban air transportation.

Improving the performance of UAV navigation systems using adaptive signal processing algorithms and deep learning Keywords:

Adaptive signal processing , deep learning , drone navigation , sensor integration , GPS-less environments.