Compressed sensing theory based broadband DOA estimation algorithm of RSS algorithm

Abstract

The invention relates to a compressed sensing theory based broadband DOA estimation algorithm of an RSS algorithm, and belongs to the fields relates to array signal processing and radar detection. Under the condition that there is no information loss, signals are sampled at the speed much lower than that is required by a Nyquist sampling theorem, and the signals can be recovered completely. Data in different frequency points and reference frequency points can be shared, pre-estimation of orientation is avoided, and the estimation precision is improved. The compressed sensing theory is combinedwith DOA estimation, a focusing ideal is used to divide a broadband signal equally to narrowband signals, a narrowband ideal is used for solving, and thus, the large computational complexity of original broadband estimation is greatly reduced. The estimation precision is improved on the premise of same sampling.

Description

technical field [0001] The invention is a direction of arrival (DOA) estimation algorithm, which is applied to wireless communication, medical imaging, electronic countermeasures and other tasks, and can automatically complete the estimation of one-dimensional direction of arrival angle. The invention belongs to related fields such as array signal processing and radar detection. Background technique [0002] Array signal processing, also known as airspace signal processing, is an important branch of modern signal processing. It is widely used in the fields of mobile satellite communication, earthquake monitoring, radio astronomy, and national economy, and has been extensively researched and developed. Array signal processing is mainly to place a group of sensors in different positions in space in a fixed way, perform corresponding data processing and theoretical analysis on the signals received by these sensors, extract useful signals from electromagnetic signals and suppres...

AI Technical Summary

Problems solved by technology

At present, high-resolution DOA estimation algorithms are mainly divided into three types: the first is a linear prediction algorithm represented by the Capon adaptive beamforming method, which obtains the spatial spectrum of the signal by improving the utilization information of the signal to form the desired beam shape; The second is a subspace decomposition algorithm represented by multiple signal classification (MUSIC), which constructs high-resolution spatial spectrum peaks according to subspace decomposition, and crosses into a new field of DOA estimation; the third is based on The maximum likelihood algorithm (maximum likelihood, ML) is a representative subspace fitting algorithm. This algorithm needs to search for multidimensional spectral peaks, and has excellent statistical performance, but the calculation is complex and requires an appropriate initial value.

These classic DOA estimation algorithms have certain limitations in application, and cannot break through the limitations of subspace algorithms. They usually cannot accurately estimate the DOA when the signal-to-noise ratio is low, the snapshot is small, and the spatial distance between sources is small. direction, practicality is limited

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