The development of new processing algorithms has always been at the centre of satellite navigation research, aiming to minimize complexity and maximize the quality of the results. Often, these two objectives cannot be achieved simultaneously; thus, identifying the necessary trade-offs are required. Over the last decade, the development of new GNSS constellations offering modernized signals at the cost of higher decoding complexity has led to many new algorithms proposals to decode these signals.Yet, the current research environment suffers from several flaws, namely a lack of interoperability and comparability between algorithms that do not allow a fair comparison between each proposal. Such a problem is the consequence of a lack of open-source solutions, leading to individual in-house software development and dataset acquisition. To address it, we postulate the need to develop an open-source research platform providing common software and reference datasets dedicated to specific environments and user scenarios. We present the current development status and provide a discussion on future developments.

With the miniaturization of electronics, Global Navigation Satellite Systems (GNSS) receivers are getting more and more embedded into devices with harsh energy constraints. This process has led to new signal processing challenges due to the limited processing power on battery-operated devices and to challenging wireless environments, such as deep urban canyons, tunnels and bridges, forest canopies, increased jamming and spoofing. The latter is typically tackled via new GNSS constellations and modernization of the GNSS signals. However, the increase in signal complexity leads to higher computation requirements to recover the signals; thus, the trade-off between precision and energy should be evaluated for each application. This paper dives into low-power GNSS, focusing on the energy consumption of satellite-based positioning receivers used in battery-operated consumer devices and Internet of Things (IoT) sensors. We briefly overview the GNSS basics and the differences between legacy and modernized signals. Factors dominating the energy consumption of GNSS receivers are then reviewed, with special attention given to the complexity of the processing algorithms. Onboard and offloaded (Cloud/Edge) processing strategies are explored and compared. Finally, we highlight the current challenges of today's research in low-power GNSS.

Global Navigation Satellite System (GNSS) is widely used today for both positioning and timing purposes. Many distinct receiver chips are available off-the-shelf, each tailored to match various applications' requirements. Being implemented as Application-Specific Integrated Circuits, these chips provide good performance and low energy consumption but must be treated as "black boxes" by customers. This prevents modification, research in GNSS processing chain enhancement (e.g., application of Approximate Computing techniques), and designspace exploration for finding the optimal receiver implementation per each use case. In this paper, we review the development of SyDR, an open-source Software-Defined Radio oriented towards benchmarking of GNSS algorithms. Specifically, our goal is to integrate certain components of the GNSS processing chain in a Field-Programmable Gate Array and manage their operation with a Python program using the Xilinx PYNQ flow. We present the early steps of converting parts of SyDR to C, which will be later converted to Hardware Description Language descriptions using High-Level Synthesis. We demonstrate successful conversion of the tracking process and discuss benefits and drawbacks arising thereof, before outlining next steps in preparation for hardware implementation.