Beihang University

Yuanhao Tan

Xuejun Zhang

Xueyuan Li

The present invention discloses a multi-beamforming optimization method for space-based ADS-B based on a coverage matrix. The method includes: calculating signal-to-noise ratios of received signals based on an ADS-B signal model and an air-space channel model; deriving the correct reception probability of ADS-B signals by a satellite with different signal-to-noise ratios and with different numbers of aircraft on the basis of analyzing a correct decoding probability and a collision probability; and in the case that the constraint of satellite coverage metric is satisfied, aiming at minimizing the update interval of position messages at the update probability of 95%, establishing a digital multi-beamforming optimization model for space-based ADS-B.

Multi-beamforming optimization method for space-based ADS-B based on coverage matrix

The present invention discloses a coevolution-based multi-beamforming optimizing method for space-based ADS-B, which belongs to the field of aviation technologies. According to this method, after initializing population and acquiring an initial optimal solution, optimization parameters are firstly grouped randomly so that all beams form several significant uncovered regions. Then, grouping is performed by an adaptive grouping strategy based on the relative orientation between the mean center of the largest uncovered region and the direction of each beam, so that the parameters quickly converge to a full-coverage state within the half angle of the satellite. Finally, grouping is performed by a grouping strategy based on the aircraft density covered by each beam to obtain optimally the minimal update time interval under a full-coverage constraint. The method of the present invention adopts an adaptive dynamic grouping strategy based on the beam pointing, the orientation of uncovered region and the aircraft density covered by each beam, such that the algorithm performance and optimization efficiency can both be improved.

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Beijing, China

Yuanhao Tan