Abstract
A beamspace root modification of pseudorandom joint estimation strategy (PR-JES) is developed. The essence of PR-JES is to generate the eigenstructure-based estimator bank for given sample covariance or data matrix. Combining the results of parallel underlying estimators, PR-JES removes the outliers and improves the threshold performance. In the case of a non-uniform array, the interpolated array approach is used to enable the application of root underlying estimators. Simulations and results of real ultrasonic data processing show that the proposed beamspace root implementation significantly outperforms spectral elementspace PR-JES and achieves a performance similar or better than that of the stochastic ML method.
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