Navigation without Global Navigation Satellite Systems (GNSS) poses a significant challenge in aerospace engineering, particularly in the environments where satellite signals are obstructed or unavailable. This paper offers an in-depth review of various methods, sensors, and algorithms for Unmanned Aerial Vehicle (UAV) localization in outdoor environments where GNSS signals are unavailable or denied. A key contribution of this study is the establishment of a critical classification system that divides GNSS-denied navigation techniques into two primary categories: absolute and relative localization. This classification enhances the understanding of the strengths and weaknesses of different strategies in various operational contexts. Vision-based localization is identified as the most effective approach in GNSS-denied environments. Nonetheless, it’s clear that no single-sensor-based localization algorithm can fulfill all the needs of a comprehensive navigation system in outdoor environments. Therefore, it’s vital to implement a hybrid strategy that merges various algorithms and sensors for effective outcomes. This detailed analysis emphasizes the challenges and possible solutions for achieving reliable and effective outdoor UAV localization in environments where GNSS is unreliable or unavailable. This multi-faceted analysis, highlights the complexities and potential pathways for achieving efficient and dependable outdoor UAV localization in GNSS-denied environments.

SUMMARY: In mobile robot localization with multiple sensors, myriad problems arise as a result of inadequacies associated with each of the individual sensors. In such cases, methodologies built upon the concept of multisensor fusion are well-known to provide optimal solutions and overcome issues such as sensor nonlinearities and uncertainties. Artificial neural networks and fuzzy logic (FL) approaches can effectively model sensors with unknown nonlinearities and uncertainties. In this article, a robust approach for localization (positioning) of a mobile robot in indoor as well as outdoor environments is proposed. The neural network is utilized as a pseudo-sensor that models the global positioning system (GPS) and is used to predict the robot’s position in case of GPS signal loss in indoor environments. The data from proprioceptive sensors such as inertial sensors and GPS are fused using the Kalman and the complementary filter-based fusion schemes in the outdoor case. To eliminate the position inaccuracies due to wheel slippage, an expert FL system (FLS) is implemented and cascaded with the sensor fusion module. The proposed technique is tested both in simulation and in real scenarios of robot movements. The simulations and results from the experimental platform validate the efficacy of the proposed algorithm.

Quadrotor is a MIMO non-linear system with complex dynamics. It is an open loop unstable system and it requires an efficient control scheme for a stable flight. This paper presents a discrete PID control scheme, which is able to move quadrotor to any point in 3-dimensional space. This paper also presents the implementation of this control scheme on programmable system-on-chip (PSOC) device and tests this controller hardware on quadrotor mathematical model with hardware-in-loop simulation. Simulation results have shown the effectiveness of control scheme and successful implementation on PSOC device.

Terrain Aided Navigation (TAN) technology has become increasingly important due to its effectiveness in environments where Global Positioning System (GPS) is unavailable. In recent years, TAN systems have been extensively researched for both aerial and underwater navigation applications. However, many TAN systems that rely on recursive Unmanned Aerial Vehicle (UAV) position estimation methods, such as Extended Kalman Filters (EKF), often face challenges with divergence and instability, particularly in highly non-linear systems. To address these issues, this paper proposes and investigates a hybrid two-stage TAN positioning system for UAVs that utilizes Particle Filter. To enhance the system’s robustness against uncertainties caused by noise and to estimate additional system states, a Fuzzy Particle Filter (FPF) is employed in the first stage. This approach introduces a novel terrain composite feature that enables a fuzzy expert system to analyze terrain non-linearities and dynamically adjust the number of particles in real-time. This design allows the UAV to be efficiently localized in GPS-denied environments while also reducing the computational complexity of the particle filter in real-time applications. In the second stage, an Error State Kalman Filter (ESKF) is implemented to estimate the UAV’s altitude. The ESKF is chosen over the conventional EKF method because it is more suitable for non-linear systems. Simulation results demonstrate that the proposed fuzzy-based terrain composite method achieves high positional accuracy while reducing computational time and memory usage.