3D Beamformer Design Using Constrained Convex Optimization
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Solution Overview
Problem
Conventional beamformers designed for two-dimensional spaces perform poorly in three-dimensional environments, leading to inadequate noise suppression and interference from unwanted directions, resulting in a low directivity index.
Innovation Solution
The use of constrained convex optimization to determine weighting coefficients for a three-dimensional beampattern, with constraints including side lobe suppression and white noise gain thresholds, to enhance the beamformer's performance in approximating the desired spatial filtering properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If beamformers are designed using two-dimensional beampattern optimization, then the design process is simpler and computationally less intensive, but the noise suppression capability and directivity index deteriorate in three-dimensional environments
Solution Approach 1:
The patent transitions from two-dimensional beampattern optimization to three-dimensional beampattern optimization by incorporating elevation angles in addition to azimuth angles. This dimensionality change enables the beamformer to properly suppress noise and interference from all directions in three-dimensional space, thereby improving noise suppression capability and directivity index while maintaining computational feasibility through efficient optimization algorithms.
2Power
If beamformers are designed using two-dimensional beampattern optimization, then the computational requirements are reduced, but the directivity index and spatial selectivity deteriorate
Solution Approach 1:
The patent enhances spatial selectivity by optimizing beampatterns in three-dimensional space rather than two dimensions. By incorporating elevation angle optimization alongside azimuth angle optimization, the beamformer achieves superior spatial selectivity and directivity index while managing computational resources through efficient convex optimization techniques.
3Ease of manufacture
If conventional two-dimensional optimization is used, then the design process is more straightforward, but the beampattern approximation accuracy deteriorates in three-dimensional spaces
Solution Approach 1:
The patent improves beampattern approximation accuracy by extending the optimization from two-dimensional to three-dimensional space. The method formulates convex optimization problems that simultaneously optimize azimuth and elevation angles, subject to constraints on beampattern approximation accuracy, side lobe suppression, and white noise gain, thereby achieving high-fidelity three-dimensional beampattern synthesis.
Data Source
AI summary
Embodiments of systems and methods are described for determining weighting coefficients based at least in part on using convex optimization subject to one or more constraints to approximate a three-dimensional beampattern. In some implementations, the approximated three-dimensional beampattern comprises a main lobe that includes a look direction for which waveforms detected by a sensor array are not suppressed and a side lobe that includes other directions for which waveforms detected by the microphone array are suppressed. The one or more constraints can include a constraint that suppression of waveforms received by the sensor array from the side lobe are greater than a threshold. In some implementations, the threshold can be dependent on at least one of an angular direction of the waveform and a frequency of the waveform.