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Small and nanosatellites have a very limited energy budget and their precise positioning capacity is limited in time as positioning sensors are, in general, power demanding. In this context, the article aims to present a comparison between the positioning results of a power demanding Precise Point Positioning (PPP) algorithm and the ones provided as the output of a low-power consumption Artificial Neural Network (ANN). The data that is to be processed with PPP and ANN consists of GNSS measurements. In order to validate the results, we compare the outputs with a valid set of data containing the real positions and velocities, which will be referred to as control data. The novelty of this article consists of the ANN architecture, which is designed to better exploit information coming from both an Orbit Propagator (OP) and GNSS measurements. The idea behind the OP is to estimate the Earth-orbiting satellite’s position and velocity at any moment in time. Its main disadvantage comes from the finite precision of the machine that performs the computations. Thus, numerical errors accumulate in time and the estimation becomes less and less accurate. Simultaneously, the GNSS measurements alone are not sufficiently precise to allow complex orbital maneuvers such as inspection and controlled flight formation, giving only approximations of the actual satellite’s position and velocity. The ANN is trained to compensate for the errors that the OP and the GNSS receiver intrinsically have. Two main approaches are to be tested, considering that the OP gives estimated positions and velocities with a sufficiently large frequency, as follows: (i) the GNSS data is queried at the same frequency with the OP; this approach is expected to give satisfactory results, because the estimation can be improved faster by the ANN, having constant available GNSS data. (ii) the GNSS data is acquired at a much slower frequency; the challenge for the ANN is now to better improve the predictions with limited GNSS readings. Theoretical aspects regarding the development of the ANN and its training phase are, too, described in the current paper, followed by the comparison with the control data which reveals the performance of PPP algorithms and ANN.

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