An off-grid target localization method for frequency-controlled array radar based on sparse Bayesian learning

Abstract

The invention discloses a frequency-controlled array radar off-grid target location method based on sparse Bayesian learning. Firstly, according to the constructed monostatic frequency-controlled array radar transceiver model, the frequency offset of the transmitting array element is set to zero, and the target angle is detected under the traditional phased-array radar transceiver model. Then, the frequency offset of the transmitting array element is set to a suitable non-zero frequency offset, and the target distance is estimated under the condition of the obtained target angle information. By transmitting two pulses with zero frequency offset and non-zero frequency offset, the sparse Bayesian learning algorithm is used to complete the location of off-grid targets. Computer simulation experiments show that the method has better target positioning effect, effectively improves the convergence of the algorithm, and proves the effectiveness and reliability of the present invention.

Description

technical field [0001] The invention relates to the field of array signal processing, in particular to a frequency-controlled array radar off-grid target location method based on sparse Bayesian learning. Background technique [0002] The biggest difference between the frequency-controlled array radar and the traditional phased-array radar is that there is a frequency offset between two adjacent transmitting array elements that is much smaller than the reference carrier frequency, so that the array factor is related to distance, angle and time. . This feature makes it have a huge advantage in the field of radar target positioning. In recent years, one of the hottest problems in the field of target localization is: using Sparse Bayesian Learning (SBL: Sparse Bayesian Learning) to solve sparse models with off-grid parameters, and it has been obtained in the field of one-dimensional angle estimation of targets. good research results. However, when the sparse Bayesian model i...

AI Technical Summary

Problems solved by technology

However, when the sparse Bayesian model is extended from one-dimensional to two-dimensional, due to the sharp increase in the number of sampling grids, it will lead to unaffordable algorithmic complexity

And in the two-dimensional estimation of the target angular distance, because some grid points have the same angle or distance, this part of the grid points has correlation in the sparse source matrix, resulting in poor target positioning results

Images