A beamforming method using signal non-circular characteristics

Abstract

The invention belongs to a beam forming method in the technical field of electronic information, in particular to a beam forming method utilizing the non-circular characteristic of a signal. The method of the present invention at first carries out time-domain sampling to the received non-circular signal to obtain the observation vector; then, obtains the signal model of an extended observation vector by taking the real part and the imaginary part of the observation vector; Divide the grid by angle to obtain the weight vector matrix, and do the inner product with the extended observation vector to draw the three-dimensional direction diagram of the received signal. The estimated value of the direction of arrival and the non-circular phase; finally, the estimated value of the direction of arrival is combined with the non-circular coefficient of the signal to construct a weighted vector function of the target signal, and then obtain the desired signal pattern. The beamforming method based on the non-circular characteristics of the signal uses the non-circular characteristics to make selections and filter out interference signals, thereby significantly reducing the influence of interference on the DOA estimation of the signal of interest.

Description

technical field [0001] The invention belongs to a beam forming method in the technical field of electronic information, in particular to a beam forming method utilizing the non-circular characteristic of a signal. Background technique [0002] Beamforming uses a system composed of multiple sensor arrays in space to transmit or receive space signals. Through the weighted processing of the received information of the array, the components of the expected signal in the output of the array are received and enhanced as effectively as possible, while the interference and noise are effectively suppressed. Among the existing methods based on this principle, there are typically Minimum Variance Distortionless Response (MVDR, Minimum Variance Distortionless Response) beamforming, and Linear Multi-Constraint Minimum Variance (LCMV, Linear Constraint Minimum Variance) beamforming algorithms. [0003] Traditional linear beamformers are generally based on the simple assumption that the d...

AI Technical Summary

Problems solved by technology

When beamforming technology is applied to some fields that require artificially modulated signals (non-circular signals), such as in the field of wireless communication, the traditional linear beamformer based on various criteria is no longer optimal, because these artificially modulated signals are non-stationary For stochastic processes, its statistical properties are no longer the same as the assumptions about the signal in the traditional linear beamformer. Only using the traditional optimal beamformer to beamform the non-stationary process cannot fully utilize all the information of the observation vector

[0004] For uniform linear arrays, increasing the number of array elements can improve the resolution of conventional beamforming methods, but cannot reduce the sidelobe level

In the case of not increasing the number of array elements, if the non-uniform weighting process is performed, although the resolution of the conventional beamforming method can be improved, the sidelobe level of the beam will increase, and at the same time, the signal-to-noise ratio will be lost.

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