An Improved Partial Adaptive Narrow-Band Beamformer Using Concentric Ring Array

Vicente, Luı́s; Ho, K.C.; Kwan, Chi-Hang

doi:10.1109/icassp.2006.1661149

Cited by 6 publications

(3 citation statements)

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“…In certain scenarios the DOAs and powers of some interferences are known or can be estimated a priori. In such a case, Vicente et al [7] proposed the Modified Type I beamformer that uses the intra-ring weights to cancel the known interferences and leaves the adaptive GSC weights to remove the unknown interferences. The result is an increase in the total number of interferences that can be eliminated because it does not rely on the adaptive weights to cancel all interferences.…”

Section: Introductionmentioning

confidence: 99%

“…The fixed weights are chosen to eliminate the known interferences, but at the same time restricted to not deviate too much from the delay-and-sum weights. A fixed value penalty factor is used to control this amount of deviation [7].…”

Section: Introductionmentioning

confidence: 99%

“…The Modified Type I beamformer [7] was created to utilize the prior knowledge available for some interferences and increase the number of interferences that can be canceled with respect to the Fig. 2.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing the performance of the partial adaptive concentric ring array in the presence of prior knowledge

Vicente

2008

2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop

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The partial adaptive concentric ring array (CRA) has been successfully applied to 3-D beamforming because of its flexibility, faster tracking ability and reduced computation with respect to the fully adaptive CRA. In some cases, prior knowledge regarding some interferences is available so that better beamformers can be designed. The previous method that exploits prior knowledge by using a fixed penalty factor could not guarantee in maintaining a low residual interference and noise level. We propose in this paper an adaptive beamformer that automatically seeks out the optimum penalty factor to achieve the best performance. The proposed beamformer outperforms the previous design in maintaining a higher output signal to interference and noise ratio, even after the characteristics of the interferences have changed. The performance of the proposed beamformer is evaluated through simulations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%