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Fifth-generation (5G) networks are expected to provide high-precisionpositioning estimation utilizing mmWave signals in urban and downtown areas. Insuch areas, 5G base stations (BSs) will be densely deployed, allowing forline-of-sight (LOS) communications between the user equipment (UE) and multipleBSs at the same time. Having access to a plethora of measurement sources grantsthe need for optimal integration between the BSs to have an accurate andprecise positioning solution. Traditionally, 5G multi-BS fusion is conductedvia an extended Kalman filter (EKF), that directly utilizes range and anglemeasurements in a centralized integration scheme. Such measurements have anon-linear relationship with the positioning states of the filter, giving riseto linearization errors. Counter to the common belief, an unscented Kalmanfilter (UKF) will fail to totally eradicate such linearization errors. In thispaper, we argue that a decentralized integration between 5G BSs would fullyavoid linearization errors within the filter and significantly enhance thepositioning performance. This is done by fusing position measurements insteadof directly fusing range and angle measurements, which inherently leads to alinear measurement model by design. The proposed decentralized KF method wasevaluated in a quasi-real simulation setup provided by Siradel using a realtrajectory in Downtown Toronto. The experiments compared the performance ofdecentralized KF integration to that of centralized EKF and UKF integrationschemes. It was shown that the proposed method outperformed both UKF and EKFimplementations in multiple scenarios as it significantly decreased the RMS andmaximum 2D positioning errors, achieving decimeter-level accuracy for 93.9% ofthe time.

5G Multi-BS Positioning: A Decentralized Fusion Scheme