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The constant modulus algorithm (CMA) has been known as blind adaptive beamforming because it requires no knowledge about the signal except that the transmitted signal waveform has a constant envelope. But in practical applications, the performance of the linearly constrained CMA is known to degrade severely in the presence of even slight signal steering vector mismatches. To account for the mismatches,...
A novel robust least squares constant modulus algorithm (LSCMA) is proposed for blind adaptive beamforming, which is based on explicit modeling of uncertainty in the desired signal array. To improve robustness, the weight vector is optimized to involve minimization of cost function, while imposing the oblique projection constraint on the weight vector and maintaining a distortionless response for...
In wireless communication environment, the time-varying channel and angular spread caused by multipath fading and the mobility of mobile degrade the performance of conventional direction of arrival (DOA) tracing algorithm. In this paper, we present a DOA tracking scheme for coherently distributed source based on support vector regression method. The mapping among the outputs of the array and the DOAs...
The conventional constrained least squares constant modulus algorithm (LSCMA) can suffer significant performance degradation in the presence of the slight mismatches between the actual and assumed signal steering vectors. In this paper, to combat the mismatches, a novel robust constrained LSCMA is proposed for implementing double constraints with Taylor-series expansion and Lagrange multipliers method,...
Adaptive beamforming is known to have resolution and interference rejection capability when the array steering vector is precisely known. However, the performance of adaptive beamforming techniques may degrade severely in the presence of mismatches between the assumed array response and the true array response. In this paper, we propose a novel neural network approach to robust adaptive beamforming...
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