Frequency agility radar target reconstruction method based on non-parameter iteration adaption

Abstract

The invention discloses a frequency agility radar target reconstruction method based on nonparametric iteration adaptation, and belongs to the technical field of radar signal processing. Specifically,the method comprises the following steps of firstly, establishing an echo signal model of a frequency agility radar, then converting the echo signals into the corresponding compressed sensing modelsof the frequency agility radar, and constructing a corresponding observation matrix; and finally, performing corresponding conversion on the compressed sensing models of the frequency agility radar and the compressed sensing models of the array signals, and performing optimization solution on a target signal of the frequency agility radar by adopting a non-parametric iteration adaptive algorithm to obtain the target signal column vectors; and rearranging the target signal column vectors to obtain a reconstructed target signal. According to the method, after the observation matrix is constructed and analogous to an array manifold matrix, the IAA is introduced into the compressed sensing solving process of the frequency agility radar signal, so that the accurate reconstruction of the targetunder the condition of not needing the extra parameters, such as sparsity, etc., is achieved.

Description

technical field [0001] The invention belongs to the technical field of radar signal processing, and in particular relates to a non-parameter iterative self-adaptive method for reconfiguring an agile frequency radar target. Background technique [0002] The carrier frequency of the frequency-agile radar changes rapidly within a certain range. For the jammer, the next pulse carrier frequency of the radar is unknown, and deceptive jamming and suppressive jamming of frequency targeting are difficult to work. Therefore, the frequency-agile radar can improve the anti- Interference performance, but also can improve the advantage of distance resolution, has broad application prospects. [0003] As a theoretical model, the most important breakthrough and innovation of compressed sensing lies in the expansion of the Nyquist sampling theory; based on the prior signal sparsity, the Under the condition of under-sampling at a low rate, the compressed sensing model is optimized and solved...

AI Technical Summary

Problems solved by technology

However, the compressed sensing algorithms currently used in the frequency-agile field, such as the Orthogonal Matching Pursuit (OMP) algorithm, often need to obtain some parameter conditions. In practical applications, the parameters often need to be tested repeatedly, resulting in a large amount of calculation, poor stability of the results, and accuracy of target positioning. lower

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