SVD (singular value decomposition) noise reduction based Bayes compressed sensing radar data integrating method

Abstract

The invention discloses an SVD (singular value decomposition) noise reduction based Bayes compressed sensing radar data integrating method. The method includes steps of acquiring echo wave data of a radar target at different frequency points and in different azimuth angles and adding noise of different degrees into echo wave data obtained through simulation for standby use for testing; utilizing aSVD noise reduction method, performing denoising treatment on the obtained radar echo wave signals; utilizing a Bayes compressed sensing method for performing data integration on sparse frequency band radar echo wave data subjected to denoising treatment and reconstructing full frequency band echo wave data; performing reverse synthetic aperture radar imaging by utilizing the full frequency bandradar echo wave data reconstructed through data integration. According to the invention, denoising treatment is performed on input radar echo wave before multi-frequency-band radar data integration, so that relative errors of the Bayes compressed sensing method in full-frequency-band radar signal reconstruction is reduced distinctively and radar imaging resolution and accuracy are improved.

Description

technical field [0001] The invention belongs to the technical field of radar target high-resolution imaging, in particular to a Bayesian compressed sensing multi-band radar data fusion high-resolution imaging method based on singular value decomposition and noise reduction. Background technique [0002] Since the resolution of a single radar imaging system is constrained by the signal bandwidth and coherent accumulation angle, it is particularly important to seek a breakthrough in the algorithm to achieve high-resolution imaging of the target. In recent years, multi-radar data fusion technology, as an emerging radar imaging technology, has received very high attention in the military and has broad application prospects. Different from a single radar, multiple radars can form different observation networks to observe targets in all directions. Multi-radar data fusion technology integrates radar echo data from different angles and frequency bands to obtain high-precision targ...

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Problems solved by technology

[0003] However, it should be pointed out that the traditional Bayesian compressive sensing multi-band radar data fusion technology usually only considers the influence of noise when modeling according to the sparse prior information of the echo, which makes this algorithm have certain limitations in the calculation of signal reconstruction. Resistance

However, when the influence of noise is too large, the anti-noise ability of the sparse Bayesian algorithm itself is not enough to completely eliminate the noise, so that the reconstructed full-band radar signal has a large error compared with the original full-band signal, which leads to blurred radar imaging. or spurious scattering center

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