Radar angle and distance estimation method based on tensor high-order singular value decomposition

Abstract

The invention provides a radar angle and distance estimation method based on tensor high-order singular value decomposition, and the method comprises the following steps: constructing an MIMO radar receiving array comprising k subarrays, obtaining the receiving data of a target echo through the MIMO radar receiving array, and constructing a third-order tensor signal model; decomposing the three-order tensor signal model by adopting a high-order singular value to obtain tensor-based signal subspaces; extracting a receiving matrix of the signal subspace to realize MIMO radar target DOA parameter estimation; and extracting a signal subspace and an emission matrix corresponding to each subarray, eliminating the phase ambiguity problem caused by the coupling of the target DOD parameter and the distance, achieving the automatic pairing of the target DOD and the distance with the DOA parameter, and finally achieving the estimation of the target DOD parameter and the distance parameter of the MIMO radar.

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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