Estimation of Parabolic Microwave Antenna Movements Using Sensor Fusion

Abstract: Wireless communication has seamlessly integrated itself into our daily lives, with Ericsson pioneering the development of comprehensive mobile ecosystems. As the demand for data traffic continues to surge, the enhancement of both cellular radio systems and backhaul infrastructure remains paramount. Within this context, Ericsson leverages microwave transport network solutions to address backhaul needs. However, the implementation of microwave radio solutions introduces its own set of challenges, particularly concerning physical movements and meteorological fluctuations. The thesis focuses on analyzing a typical link using microwave radio solutions, which rely on a Line of Sight (LoS) path and narrow beam width for high gain. However, the very attribute that renders the link efficient—its narrow beam width—also renders it vulnerable to potential deterioration or malfunction due to factors such as antenna and mast movements. In response to these challenges, the thesis puts forth a comprehensive solution involving the deployment of sensors for movement detection. This approach encompasses the development of sensor fusion models and innovative signal processing methodologies. The primary objective revolves around accurately estimating the deviation of the antenna’s orientation from the optimal LoS alignment. Existing scholarly works have harnessed magnetometer data from an Inertial Measurement Unit (IMU) sensor to refine estimation precision. However, the radio system under consideration features an embedded sensor located directly on its circuitry. This circuitry is ensconced within a casing, potentially offering shielding against external magnetic fields. To bolster the accuracy of the sensor model, the proposed strategy is to incorporate Received Signal Strength Indicator (RSSI) measurements. The research outlined within the thesis encompasses two areas. First and foremost, it seeks to establish a correlation between the movements of the antenna and the corresponding variations in the received signal strength. By probing this relationship, the aim is to arrive at an understanding of the underlying causes for the observed link degradation. The second facet of this research delves into the domain of estimation using a variant of Kalman Filter. Specifically, the thesis endeavors to estimate the deviation in the antenna’s orientation from its optimal alignment. This estimation process is fortified by leveraging properties from historical data to estimate current deviations and possibly forecast future deviations. Moreover, the thesis recommends certain corrective measures that hold the potential to mitigate the impacts of link degradation. However, the implementation or validation of these corrective actions is not explicitly undertaken within the scope of this study. Instead, this constitutes a fertile ground for subsequent research investigations to explore and validate further.