Gaussian entropy criterion-based self-adaptive reduced-rank beamforming method

An adaptive and rank-reducing technology, which is applied in space transmit diversity, radio transmission systems, electrical components, etc., can solve the problems of increasing algorithm convergence speed, reducing algorithm complexity, and high performance complexity, achieving reduced complexity and reduced The effect of increasing the amount of calculation and output SINR

Pending Publication Date: 2020-04-03
成都电科慧安科技有限公司
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Problems solved by technology

[0005] The purpose of the present invention is to solve the problem of performance degradation and the high complexity and slow convergence of the wide linear LSE algorithm when the signal is a non-circular signal in the existing algorithm, and to provide an adaptive reduced-rank beamforming method based on the Gaussian entropy criterion , the present invention effectively reduces the complexity of the algorithm and increases the convergence speed of the algorithm by introducing the rank reduction theory into the wide linear LSE algorithm, especially for larger systems, its superiority is more obvious

Method used

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  • Gaussian entropy criterion-based self-adaptive reduced-rank beamforming method

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Embodiment 1

[0073] Embodiment 1: The incident direction of the desired signal is 40°, and the incident directions of the two disturbances are 20° and 65° respectively. Their center frequencies are 15.48MHz, 12.58MHz and 17.48MHz respectively. The values ​​of SNR and SIR are both -20dB. Performing 500 independent experiments, we get as figure 2 The change curve of the output SINR with the number of snapshots shown and image 3 The change curve of MSD with the number of snapshots shown;

[0074] As can be seen from the simulation results, the curve corresponding to the method (Augmented Reduced-rank Least Stochastic Entropy, ARLSE method) proposed by the present invention is compared with the curve corresponding to the WL-LSE algorithm, and its convergence speed has been significantly improved; From the curves corresponding to the two algorithms LSE and WL-LMS, it is not difficult to see that the output SINR of the algorithm based on the Gaussian entropy criterion is significantly great...

Embodiment 2

[0075] Embodiment 2: The incident direction of the desired signal is 40°, and the incident directions of the two disturbances are 20° and 65° respectively. Their center frequencies are 15.48MHz, 12.58MHz and 17.48MHz respectively. The value of SIR is -20dB. Performing 500 independent experiments, we get as Figure 4 The output SINR vs. SNR curve is shown.

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Abstract

The invention discloses a Gaussian entropy criterion-based self-adaptive reduced-rank beamforming method, which comprises the following steps: 1, initializing a weight vector and a reduced-rank matrixof a wide linear reduced-rank beamformer, and giving a value of a step length factor; 2, acquiring an array receiving signal; 3, performing wide linearity and rank reduction processing on the array receiving signal, and then obtaining an output signal through a wide linearity rank reduction beamformer; and 4, substituting the initial value of the rank reduction matrix, the initial value of the weight vector and the output signal into an iterative formula of the reduced-rank matrix and the weight vector for iterative solution to obtain an optimal weight vector for beamforming, and then performing beamforming according to the optimal weight vector. According to the method, the rank reduction theory is introduced into the wide linear LSE algorithm, so that the algorithm complexity is effectively reduced, and the convergence rate of the algorithm is increased. Meanwhile, the wide linear processing and the Gaussian entropy criterion are adopted, and the second-order statistical characteristics of the received signal and the error signal are fully utilized, so that the method can be applied to processing non-circular signals.

Description

technical field [0001] The invention belongs to the technical field of beamforming, and in particular relates to an adaptive rank-reducing beamforming method based on Gaussian entropy criterion. Background technique [0002] Today, adaptive beamforming technology is widely used in communication, radar, sonar and satellite navigation systems. Compared with the pure spatial adaptive processing algorithm, the space-time adaptive processing (STAP) algorithm uses the joint optimization of the spatial domain and the time domain to significantly improve the output signal-to-interference-plus -noise-ratio,SINR). However, the space-time domain adaptive algorithm requires a lot of calculations when the rank is full, and the computational complexity of the full-rank space-time domain adaptive algorithm that requires matrix inversion is as high as O(L 3 ) order, where L=M×P is the dimension of the filter, when the number of array elements M or the number of time-domain taps P of each ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04B7/06H04B7/08
CPCH04B7/0617H04B7/086
Inventor 殷光强夏威方惠
Owner 成都电科慧安科技有限公司
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