Space-Based ADS-B Multi-Beamforming Optimization via Coverage Matrix
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Solution Overview
Problem
Existing multi-beamforming optimization models for space-based Automatic Dependent Surveillance-Broadcast (ADS-B) do not consider the coverage of all beams of a satellite and the effects of different correct decoding probabilities of signals with varying signal-to-noise ratios (SNRs).
Innovation Solution
A multi-beamforming optimization method based on a coverage matrix is developed, which includes steps such as establishing a digital multibeam reception scenario, calculating signal-to-noise ratios, analyzing correct decoding and collision probabilities, and determining an optimization objective function that satisfies amplitude, phase, and full-coverage constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If phased-array antennas are used to spatially separate signals through multi-beam reception, then the collision probability is reduced, but the existing optimization model does not consider the coverage of all beams and different correct decoding probabilities of signals with different SNRs
Solution Approach 1:
The patent changes the optimization parameters from simple beam coverage to a comprehensive model that includes SNR-weighted correct decoding probabilities. The objective function incorporates signal-to-noise ratio as a parameter to evaluate and optimize the quality of signal reception across all beams, thereby improving surveillance performance while accounting for realistic signal conditions.
Solution Approach 2:
The patent segments the coverage area into multiple beams and evaluates each beam's contribution separately based on its SNR and correct decoding probability. This segmentation allows the optimization model to consider individual beam performance and combine them to achieve overall system optimization, addressing both coverage and signal quality requirements.
2Area of stationary object
If orbital altitude and coverage range are increased, then global coverage capability is improved, but signal collision and co-channel interference increase
Solution Approach 1:
The patent transitions from two-dimensional spatial coverage optimization to a three-dimensional optimization that incorporates SNR as an additional dimension. By adding the SNR dimension to the optimization problem, the system can evaluate beam quality alongside coverage, enabling better separation of signals in the vertical quality dimension while maintaining horizontal coverage.
Solution Approach 2:
The patent incorporates feedback mechanisms by using calculated correct decoding probabilities based on SNR to guide the optimization process. The optimization model uses this feedback information to adjust beam configurations, thereby reducing signal collisions and interference while maintaining coverage range.
3Reliability
If deinterleaving algorithm is used for space-based ADS-B signals, then signal processing capability is improved, but the algorithm has high complexity and high SNR requirements
Solution Approach 1:
The patent applies preliminary action by optimizing the beamforming configuration before signal reception occurs. By pre-configuring the beams to maximize correct decoding probabilities based on predicted SNR conditions, the system reduces the need for complex post-reception processing algorithms like deinterleaving, thereby lowering overall system complexity while maintaining signal processing capability.
Data Source
AI summary
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.