Adaptive Beamforming for Sparse Heterogeneous Antenna Arrays
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Solution Overview
Problem
Existing satellite antenna systems face challenges in maintaining reliable communication, especially in contested environments with jamming signals, due to the need for complex antennas and known antenna models, which limits performance and introduces grating lobes, and requires costly calibration and characterization.
Innovation Solution
A method for adaptive digital beamforming that combines signals from heterogeneous antennas without prior knowledge of antenna models or signal directions, using iterative weight estimation to optimize signal-to-noise ratio and suppress interference, allowing for flexible antenna configurations and reduced system complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional beamforming with known antenna models is used, then signal direction control is achieved, but system complexity and calibration cost increase
Solution Approach 1:
The system performs self-calibration by automatically estimating antenna element characteristics and aperture distribution from received signals without requiring external calibration equipment or pre-stored antenna models. The calibration process is integrated into normal operation, eliminating separate calibration procedures and reducing operational complexity.
Solution Approach 2:
The patent replaces model-based beamforming (which requires mechanical calibration and stored antenna patterns) with data-driven adaptive beamforming that estimates antenna characteristics directly from signal measurements. This substitution eliminates the need for mechanical calibration procedures and complex antenna model databases.
2Adaptability or versatility
If diverse antenna elements are used to improve coverage, then antenna model inaccuracies and grating lobes increase
Solution Approach 1:
The system dynamically estimates and adapts to changing antenna element parameters (amplitude, phase, position) based on received signal characteristics. By continuously updating these parameters from measured data rather than relying on fixed models, the system maintains accuracy despite physical variations in the antenna array configuration.
3Device complexity
If analog beamforming is used, then hardware complexity is reduced, but adaptability to different users and interference rejection deteriorates
Solution Approach 1:
The patent transitions from analog beamforming (single dimension of phase/amplitude control) to digital beamforming with independent weight optimization for each antenna element and each user. This adds the dimension of per-user adaptive optimization, enabling simultaneous service to multiple users with different spatial requirements while maintaining hardware efficiency through shared digital signal processing resources.
4Measurement precision
If calibration and characterization are performed to improve accuracy, then measurement precision increases, but time and cost increase
Solution Approach 1:
The system performs preliminary estimation of antenna characteristics using readily available signal data before actual beamforming operations. By preparing the antenna model information in advance from operational signals rather than during dedicated calibration procedures, the system achieves accurate characterization without sacrificing operational time.
Data Source
AI summary
A method and apparatus in one example uses adaptive digital beamforming with a plurality of heterogeneous antennas which are more affordable and flexible and do not require the use of a nuller antenna. The method uses adaptive, multi-beam digital beamforming without knowledge of a signal direction or aperture of the antena. The method works with arbitrary antenna elements in arbitrary locations and does not require any a priori antenna model. The method also optimizes signal-to-noise ratio (SNR) of the received signal.