Radar Angle and Distance Estimation Method Based on Tensor Higher Order Singular Value Decomposition

Abstract

The present invention provides a radar angle and distance estimation method based on tensor high-order singular value decomposition, which includes the following steps: constructing a MIMO radar receiving array including k sub-arrays, and obtaining the receiving data of the target echo through the MIMO radar receiving array , and construct a third-order tensor signal model; adopt a high-order singular value decomposition third-order tensor signal model to obtain a tensor-based signal subspace; extract the receiving matrix of the signal subspace to realize MIMO radar target DOA parameter estimation; Extract the signal subspace and emission matrix corresponding to each sub-array, and eliminate the phase ambiguity problem caused by the target DOD parameter and distance coupling, and realize the automatic pairing of the target DOD and distance with the DOA parameter, and finally realize the MIMO radar target DOD parameter and distance Estimation of parameters.

Description

technical field [0001] The invention relates to the technical field of bistatic MIMO radar systems, in particular to a radar angle and distance estimation method based on tensor high-order singular value decomposition. Background technique [0002] Frequency controlled array MIMO radar is a new system radar that has been continuously developed in recent years. On the premise of retaining the advantages of phased array, it has been better improved. It has high resolution and excellent parameter estimation performance. It is the current signal processing field. Research hotspots. Frequency-controlled array MIMO radars can be divided into two types based on the relative positions of the receiving array and the transmitting array, namely stand-alone ground frequency-controlled array MIMO radar and bistatic frequency-controlled array MIMO radar. In recent years, the research on frequency-steered array MIMO radar has mostly focused on the field of single base, and some algorithms...

AI Technical Summary

Problems solved by technology

However, the methods mentioned above are all based on subspace algorithms of matrix decomposition, which simply store the received echo data as a matrix, but this method will lose the inherent multi-dimensional structure of the received data

Secondly, this kind of algorithm has poor performance in the case of low snapshot and low signal-to-noise ratio.

In addition, parameter matching errors are prone to occur in the case of multiple targets. In practical applications, multiple targets are more common, and in harsh electromagnetic environments, the application performance of the above method will be seriously affected.

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