This thesis presents an innovative decentralised sensor fusion framework with significant potential to improve navigation accuracy in autonomous vehicles. Its applicability is especially noteworthy in demanding scenarios, such as adverse weather conditions and intricate urban environments. In general, sensor fusion is a crucial method for integrating signals from various sources, extracting and integrating information from multiple inputs into a unified signal or data set. Frequently, sources of information are from sensors or devices designed for the perception and measurement of dynamic environmental changes. The collected data from diverse sensors undergoes processing through specialised algorithms, commonly referred to as "sensor fusion" or "data fusion" algorithms. This thesis describes sensor fusion's significance in processing data from multiple sources. It highlights the classification of fusion algorithms, demonstrating the versatility and applicability of sensor fusion across a range of redundant sensors. Moreover, various creative strategies for sensor fusion, including fault detection and isolation and methods for addressing non-Gaussian noise through smoothing filter techniques, are collectively introduced as part of a comprehensive navigation framework. The contributions of this thesis are summarized in the following. First, it introduces a decentralised two-layered fusion architecture for pose estimation, emphasising fault resilience. In a decentralised fashion, it utilises distributed nodes equipped with extended Kalman filters in the initial tier and optimal information filters in the subsequent tier to amalgamate pose data from multiple sensors. The design is named the Fault-Resilient Optimal Information Fusion (FR-OIF) architecture in this thesis, which guarantees reliable pose estimation, even in cases of sensor malfunctions. Secondly, this work proposes an Auto-encoder-based fault detection framework for a multi-sensorial distributed pose estimation. In this framework, auto-encoders are applied to detect anomalies in the raw signal measurements. At the same time, a fault-resilient optimal information filter (FROIF) approach is incorporated with the auto-encoder-based detection to improve estimation accuracy. The effectiveness of these methods is demonstrated through experimental results involving a micro aerial vehicle and is compared to a novel classical detection approach based on the Extended Kalman filter. Furthermore, it introduces an integrated multi-sensor fusion architecture enhanced by centralised Auto-encoder technology and an EKF framework. This approach effectively removes sensor data noise and anomalies, ensuring reliable data reconstruction, even when faced with time-dependent anomalies. The assessment of the framework's performance using actual sensor data collected from the onboard sensors of a micro aerial vehicle demonstrates its superiority compared to a centralised Extended Kalman filter without Auto-encoders. The next part of the thesis discusses the increasing need for resilient autonomy in complex space missions. It emphasises the challenges posed by interactions with non-cooperative objects and extreme environments, calling for advanced autonomy solutions.  Furthermore, this work introduces a decentralised multi-sensor fusion architecture for resilient satellite navigation around asteroids. It addresses challenges such as dynamic illumination, sensor drift, and momentary sensor failure. The approach includes fault detection and isolation methods, ensuring autonomous operation in adverse conditions. Finally, the last part of the thesis focuses on accurate localisation and deviation identification in multi-sensor fusion with Millimeter-Wave Radars. It presents a flexible, decentralised smoothing filter framework that effectively handles unwanted measurements and enhances Ego velocity estimation accuracy.  Overall, this thesis plays a significant role in advancing the field of decentralised sensor fusion, encompassing anomaly avoidance mechanisms, fault detection and isolation frameworks, and robust navigation algorithms applicable across a range of domains, covering everything from robotics to space exploration. In the initial section of this thesis, we delve into the backdrop, reasons behind the research, existing challenges, and the contributions made. Conversely, the subsequent section comprises the complete articles linked to the outlined contributions and a bibliography.