<ul><li>New framework PC-DeepNet uses a permutation-invariant deep neural network to minimize positioning errors in global navigation satellite systems (GNSS) in urban and sub-urban areas.</li><li>PC-DeepNet addresses challenges posed by non-line-of-sight propagation, multipath effects, and low received power levels that result in non-linear and non-Gaussian measurement error distributions.</li><li>The framework leverages NLOS and multipath indicators as features to enhance positioning accuracy in challenging environments.</li><li>PC-DeepNet achieves superior accuracy and lower computational complexity compared to existing model-based and learning-based methods.</li></ul>

PC-DeepNet: A GNSS Positioning Error Minimization Framework Using Permutation-Invariant Deep Neural Network

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