Tensor-sparse-representation-based MIMO radar's imaging method

Abstract

The invention belongs to the radar technology field and signal processing field and more particularly, to a tensor-sparse-representation-based MIMO radar's imaging method for a multi-input and multi-output type system. The method comprises: transmitting by M emission array elements mutually orthogonal phase coded signals; receiving by N receiving array elements the phase coded signals; using a matched filter to perform matched filtering to the received radar signals; conducting the Fourier transform to the signals after matched filtering to obtain the spatial spectral echo expression; performing mesh generation to the scene; and discretizing the radar echoes to obtain the mathematical expression for radar imaging and focusing under the compression sensing framework. The method of the invention overcomes the shortcoming of a DAS method which has intrinsically low resolutions and high side lobes. Compared with other classic imaging method featuring compression sensing, the THMP method of the invention fully utilizes the tensor characteristics of received signals to conduct sparse signal recovery, which avoids the information loss of the internal structure of the signals brought about by the vectorized operations.

Description

technical field [0001] The invention belongs to the technical field of radar and the field of signal processing, in particular to a MIMO radar imaging method based on tensor sparse representation for a multi-input multi-output type system. Background technique [0002] Multiple-input multiple-output (MIMO) radar is a new radar system emerging in the 21st century, which uses multiple transmitting and receiving antennas to observe targets simultaneously. Good array configuration design and waveform diversity technology enable MIMO radar to obtain observation channels and spatial degrees of freedom far more than the actual number of physical array elements, which can significantly improve the identifiability of parameters and achieve a more flexible emission pattern designed to improve object detection and parameter estimation performance. Compared with traditional imaging radar, MIMO radar has obvious performance advantages in imaging azimuth resolution, real-time and motion ...

AI Technical Summary

Problems solved by technology

This method has a lower computational load and higher imaging resolution, but since the OMP method can only expand the strategy of not removing the bad base signal when selecting the base signal, the OMP recovery method will have artifacts in radar imaging applications point, which is not conducive to the identification of the target

The literature (Subspace pursuit for compressive sensing signal recomstruction.IEEE Transaction on Information Theory,2009,55(5):2230-2249) proposes a compressed sensing greedy method called subspace pursuit (subspace pursuit, SP), which corrects the OMP The method has the problem of artifact points, but its resolution is lower than the OMP method in MIMO radar imaging applications

[0005] In addition, the sparse imaging method mentioned above usually processes the received signal as a stack, which will undoubtedly destroy the multi-dimensional structure of the signal, so that the multi-dimensional structure information of the signal cannot be used, resulting in a decrease in the performance of the method

Especially in the case of low signal-to-noise ratio and small samples, the performance is even worse

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