Single-snapshot two-dimensional DOA estimation method in large-scale uniform cross array

Abstract

The invention discloses a single-snapshot two-dimensional DOA estimation method in a large-scale uniform cross array. The method specifically comprises the following steps of: firstly, setting a large-scale cross antenna array, and performing single-snapshot sampling on a received signal; performing DFT on received data of an X-axis direction array element and a Y-axis direction array element respectively, obtaining a phase shift matrix through a DFT spectrum search method, and then performing angle matching by adopting a maximum likelihood estimation method to obtain a DOA initial estimationvalue; and then obtaining estimation of the offset through a Taylor expansion method to obtain a DOA fine estimation value. According to the method, the space smoothing process in a traditional subspace method and the phase search process in a traditional DFT method can be effectively avoided, and the calculation cost is reduced.

Description

technical field [0001] The invention belongs to the technical field of array signal processing. Background technique [0002] Array signal processing technology obtains information by receiving data from sensor arrays. It has the advantages of flexible beam control, high signal gain, strong anti-interference ability, and high spatial resolution. It has been widely used in many fields such as radar, wireless communication, satellite navigation, and voice signal processing. Applications. Direction of Arrival estimation (Direction of Arrival, DOA) is an important direction of array signal processing. It is a technology to estimate the position of the source through the signal received by the sensor array. Applying DOA technology to large-scale antenna arrays can quickly and accurately Estimating the source location will play an important role in sonar, radar and 5G communication systems. Traditional subspace DOA estimation methods, such as MUSIC (Multiple Signal Classificatio...

AI Technical Summary

Problems solved by technology

Traditional subspace DOA estimation methods, such as MUSIC (Multiple Signal Classification) multiple signal classification algorithm and ESPRIT (Estimating Signal Parameters via Rotational Invariance Techniques, ESPRIT) algorithm, rely on rotation invariance to estimate signal parameters and require many snapshots of received data estimation Signal subspace, resulting in slow algorithm estimation speed, and this type of algorithm requires eigenvalue decomposition, the complexity is very high when directly applied to large-scale arrays

Although the computational complexity of the traditional DFT algorithm is much lower than that of the subspace algorithm, the complexity of the phase search fine estimation process is still relatively high, and there is a precision threshold. When the array size is large, the performance improvement is not much compared with the initial estimation.

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