Construction and calculation method of random distributed robust beam former

Abstract

The invention discloses a construction and calculation method of a random distributed robust beam former. The method comprises the following steps: step S1, setting a sensor array; step S2, receivinga signal; step S3, estimating an interference and noise covariance matrix; step S4, acquiring an expression of the beam former in an ideal state; step S5, introducing random distribution of mismatch vectors in ideal planning of the beam former, and obtaining the random distributed robust beam former; and step S6, reconstructing the random distributed robust beam former into a cone programming expression capable of being calculated by a standard software package. The invention proposes the random distributed robust beam former which is more universal and practical and better in performance andthe calculation method thereof.

Description

technical field [0001] The invention relates to the field of beamformers, in particular to a construction and calculation method of a randomly distributed robust beamformer. Background technique [0002] The design of the existing robust beamformer is basically obtained by the worst-case optimization of the uncertain region of the steering vector, or assuming that the probability distribution of the precise steering vector is known. [0003] The first type of robust beamformer is usually called the worst case robust beamformer, such as S.Vorobyov, et al published Robust adaptive beamforming using worst-case performance op-timization: A solution to the signal mismatch problem (IEEE Trans.Signal Process.,vol.51,no.2,pp.313-324,Feb.2003.) The worst-case robust beamformer usually measures the uncertainty by the norm of the mismatch vector, because in actual use It is difficult to determine the actual physical meaning by measuring the norm of the mismatch vector, and the probabi...

AI Technical Summary

Problems solved by technology

[0003] The first type of robust beamformer is usually called the worst case robust beamformer, such as S.Vorobyov, et al published Robust adaptive beamforming using worst-case performance op-timization: A solution to the signal mismatch problem (IEEE Trans.Signal Process.,vol.51,no.2,pp.313-324,Feb.2003.) The worst-case robust beamformer usually measures the uncertainty by the norm of the mismatch vector, because in actual use It is difficult to determine the actual physical meaning by measuring the norm of the mismatch vector, and the probability of the worst case of steering vector mismatch is very low, which causes the worst-case robust beamformer in practical use. Overly conservative performance and poor practicality

[0004] The second type of robust beamformer usually assumes that the mismatch vector obeys a Gaussian distribution, so it is called a Gaussian robust beamformer, such as Robust adaptive beamforming using probability-constrained optimization (Proc.13th IEEE Workshop published by S. Vorobyov et al. StatisticalSignal Process., Bordeaux, France, July 2005pp.934-939), but in actual use, the assumption that the mismatch vector obeys the Gaussian distribution is often not established, so the practicability is poor and it is not general in application

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