A study on the use of graph signal processing techniques for satellite-based navigation systems

This paper presents the results from a study on the use of Graph Signal Processing (GSP) techniques for satellite-based navigation systems. Graph Signal Processing, an emerging and innovative way of processing signal and information on a graph, had established some unique advantages in analyzing very large datasets, or big data that usually pose a significant challenge for conventional signal processing techniques. This paper described the generic framework of Graph Signal Processing and some commonly-used operations for processing signals on graph. As the trend of navigation system design moves from single, standalone system to networked, collaborative systems, methods and tools for Graph Signal Processing have a great potential to be used in the design of networked, collaborative navigation systems since the formation of satellite-based navigation systems can be naturally represented in the form of a graph with nodes (satellites and receivers) correlating/connecting to each other. For such reasons, the feasibility of using Graph Signal Processing for satellite-based navigation applications was investigated and discussed in this paper. An example was then given in the paper using simulated and real DGNSS pseudorange corrections generated from a set of spatially-correlated Continuously Operating Reference Station (CORS) receivers in North Carolina as signals on graph to show the possible use of Graph Signal Processing techniques in a receiver network. Both simulation and real-data results have shown the effectiveness of using such method and algorithm in the detection of anomalies in GNSS measurements for integrity monitoring within the receiver network.