1. What are the contributions mentioned in the paper "A temporal algorithm for satellite subset selection in multi-constellation gnss" ?
And since the typical performance criterion ( e. g. Geometric Dilution of Precision ) is nonlinear and non-separable in the satellites ’ locations in the sky, finding the best subset is a brute force procedure ; hence, a number of authors have described sub-optimal algorithms for choosing satellites.. This paper revisits this problem, especially in the context of multiple GNSS constellations.. The paper begins with a review of the existing subset selection algorithms.. Through an example with the GPS constellation, the authors examine the time-sequential, or temporal, characteristics of the best subset selection noting: • That the best subset at a particular point ( snapshot ) in time is also the best subset for a significant time interval around that point ( typically measured in minutes ).. Based upon these observations this paper develops several time-sequential, or temporal, algorithms that attempt to track the optimum subset of satellites over time at low computational cost.. That the receiver is using corrections from some augmentation system and that the bandwidth of the correction channel is insufficient to provide information for all of the visible satellites ( see, for example, [ 4 ] ).. This question of selecting a subset of the possible satellites is not new in the navigation literature ; multiple authors have described sub-optimal algorithms for choosing the satellite subset.. ( Note, however, that this question is still timely ; all three of the papers referenced above, [ 2–4 ], are from 2016. ). This paper revisits this problem, especially in the context of multiple GNSS constellations.. This review is followed by a motivational example with the GPS constellation, examining the time-sequential, or temporal, characteristics of the best subset.. Based upon these observations, this paper develops several time-sequential, or temporal, algorithms that attempt to track the optimum subset of satellites over time at low computational cost.. For example, to generate a subset of size m the authors of [ 10 ] suggest starting with the full set of n satellites and iterating the following steps:. An algorithm could, then, choose high elevation satellites to match the number desired to be at zenith and then attempt to place the remaining satellites near the horizon following “ balance ” [ 15,16 ].. To motivate the time sequential, or temporal, algorithms developed in this paper, consider the case of selecting a subset of satellites from the GPS constellation.. For example, the issue might be: • That the receiver physically can not track all of the potential signals – this might be a hardware limitation ( due to a fixed number of channels ) or the desire to minimize power usage [ 2, 3 ].. Further all of these algorithms are what might be called “ snapshot ” in nature, selecting a subset for a single, fixed skyview of satellites.. Another sub-optimum algorithm suggests starting with a subset of size m ( and one could discuss how to select this initial subset ! ) and then iterate in a greedy fashion – growing the subset to m + 1 satellites by adding the most helpful ( with respect to GDOP ) of the unused satellites and then shrinking back down to m by removing the least helpful one, denoted a “ revolving door ” method [ 12 ] – until a equilibrium is reached.
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2. What are the future works in "A temporal algorithm for satellite subset selection in multi-constellation gnss" ?
Their future work is to combine the understanding of optimum constellations to improve suboptimum subset selection algorithms for multiple constellations.. Clearly the authors could improve on this by identifying selected candidate satellites.. One goal would be to exploit these observations to further reduce the computational load of the subset selection algorithms.
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