Synthetic Robust Adaptive Beamforming for Interference Suppression
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Solution Overview
Problem
Adaptive beamforming in non-stationary environments requires numerous snapshots to estimate the data covariance matrix, making it challenging to effectively suppress interference signals, especially when the environment is dynamic and the covariance changes over time.
Innovation Solution
The method involves estimating an autoregressive model of power over angles of arrival using a single snapshot or fewer, calculating roots associated with interference signals, constructing a synthetic covariance matrix with weights to suppress these signals, and determining the number of nulls needed to place at the angles of arrival of interference, allowing for adaptive beamforming without relying on the sample covariance matrix.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the sample covariance matrix is used to estimate the data covariance matrix for adaptive beamforming, then the beamforming can effectively suppress interference signals, but a large number of snapshots (at least 2*Ne where Ne is the number of array elements) are required, making it unsuitable for non-stationary environments
Solution Approach 1:
The patent creates a synthetic covariance matrix that copies the essential statistical properties of the true data covariance matrix without requiring extensive snapshot collection. By synthesizing a covariance matrix with desired null placements based on interference signal directions, the method replicates the interference suppression capability of traditional adaptive beamforming while requiring only a single snapshot or fewer snapshots.
Solution Approach 2:
The patent changes the fundamental parameter from requiring multiple snapshots (Ns ≥ 2*Ne) to requiring minimal snapshots (Ns = 1 or fewer). This is achieved by transitioning from empirical covariance estimation through snapshot averaging to a synthetic covariance construction that directly incorporates interference direction information and desired null placements, fundamentally altering the data requirements while maintaining beamforming effectiveness.
2Measurement precision
If multiple snapshots are collected to improve the accuracy of covariance matrix estimation, then the beamforming performance improves, but in non-stationary environments the covariance changes over time making the collected snapshots obsolete
Solution Approach 1:
The patent performs preliminary action by directly constructing the synthetic covariance matrix with the desired properties (nulls at interference directions) without waiting for multiple snapshots to be collected. This preliminary construction based on available interference direction information allows the beamformer to be configured immediately for non-stationary conditions, eliminating the delay and obsolescence issues associated with collecting and processing multiple snapshots in changing environments.
Solution Approach 2:
The patent introduces dynamics by enabling the synthetic covariance matrix to be rapidly reconstructed as environmental conditions change. Since the method requires minimal snapshots and directly incorporates current interference direction information, the beamforming weights can be dynamically updated to track changing interference patterns in non-stationary environments, providing adaptability that static snapshot-based methods cannot achieve.
3Object-generated harmful factors
If traditional adaptive beamforming methods are used, then interference signals can be suppressed, but the method lacks control over null placement and requires numerous snapshots
Solution Approach 1:
The patent applies local quality by placing nulls at specific local directions (angles of arrival of interference signals) rather than relying on global covariance estimation. The synthetic covariance matrix is constructed to have localized nulls precisely where interference is present, providing targeted suppression without requiring global statistical convergence that would demand numerous snapshots. This localized approach simplifies the overall method while maintaining effective interference suppression.
Data Source
AI summary
A method for beamforming digitized signals from a sensor array to enhance signals originating from a preselected angle of interest and a preselected range bin while suppressing interference signals from other angles of arrival. Snapshots of sensor data are acquired and used to estimate an autocorrelation function. An autoregressive model of power is determined over angles of arrival as polynomial coefficients and roots are calculated as associated with the interference signals. Roots are selected for suppression by providing nulls. Exponentials are constructed from the roots. The exponentials are used with variances from the autoregressive model to provide a synthetic covariance matrix having weights that suppress the interference signals.