Chin-Foo See

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—The performance of traditional beamformers tends to degrade due to inaccurate estimation of covariance matrix and imprecise knowledge of array steering vector. The inaccurate estimation of covariance matrix can be attributed to limited data samples and the presence of desired signal in the training data. The mismatch between the actual and presumed(More)
—The performance degradation in traditional adaptive beamformers can be attributed to the imprecise knowledge of the array steering vector and inaccurate estimation of the covariance matrix. The inaccurate estimation of the covariance matrix is due to the limited data samples and presence of desired signal components in the training data. The mismatch(More)
—The presence of desired signal in the training data for sample covariance matrix calculation is known to lead to a substantial performance degradation, especially when the desired signal is the dominant signal in the training data. Together with the uncertainty in the look direction, most of the adaptive beamforming solutions are unable to approach the(More)
HARDWARE OVERHEAD, impact on system performance, and fault coverage are key variables a designer must consider in deciding to use BIST (built-in self-test) in a design. Once the designer completes a design with BIST capability, its hardware overhead and system performance become known deter-ministically. But the designer must rely on probabilistic results(More)
The performance degradation in traditional adaptive beamformers can be attributed to the imprecise knowledge of the array steering vector and inaccurate estimation of the covariance matrix. The inaccurate estimation of the covariance matrix is due to the limited data samples and presence of desired signal components in the training data, especially when the(More)
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