Direction-of-arrival angle detection method

Abstract

The invention discloses a direction-of-arrival angle detection method which includes the steps: S1, receiving data signals s (t); S2, converting the data signals s (t) received by a vector array for the t time into a vector form by a vibration velocity sensor and outputting the data signals s (t); S3, projecting output of the vibration velocity sensor in a formula of the step S2 to a certain observation direction to obtain a synthetic vibration velocity expression of the received signals; S4, multiplying a sound pressure matrix with a synthetic vibration velocity matrix to obtain a sound pressure and vibration velocity cross covariance matrix; S5, performing Unitary transformation for the cross covariance matrix to generate a matrix R'=PHRfbP; S6, performing eigenvalue decomposition for the matrix R' after Unitary transformation to obtain an equivalent noise subspace Un'=PUn; S7, generating a polynomial F (z)=zM-1QT (1/z) (Un') (Un') HQ (z) by the equivalent noise subspace, extracting the root of the F (z) and identifying the generated root to obtain a direction-of-arrival angle. By the aid of the technical scheme, detection complexity is reduced, and detection precision is improved.

Description

technical field [0001] The invention relates to the field of array signal processing, in particular to a method for detecting the azimuth of arrival. Background technique [0002] Direction-of-Arrival (DOA) estimation has always been an important research content in array signal processing, and it is widely used in various systems such as communication, navigation, and detection; its essence is to use multiple Channel reception and spatially dispersed sensor arrays are used to obtain time domain and space domain information of the signal, and to obtain the orientation or certain parameters of the signal from such information. As the application environment becomes more severe, the accuracy and parameter requirements that need to be achieved become higher; at the same time, we also hope that under the premise of meeting a certain accuracy, we can effectively reduce the calculation complexity and save calculation time. Improve the real-time performance of AOA estimation. At ...

AI Technical Summary

Problems solved by technology

[0009] Among the classic subspace algorithms, the multiple signal classification (MUSIC) algorithm can approach the Cramer-Rao bound, but it needs to search for spectral peaks, and the detection accuracy is directly linked to the search step size, which will increase the computational complexity while pursuing accuracy

Another commonly used classical method in subspace—rotation-invariant subspace algorithm, although it can directly solve the DOA information through calculation to reduce the computational complexity, but its detection performance is worse than that of the MUSIC algorithm.

The linear prediction algorithm is greatly affected by the interference of side lobes when the distance between multiple signal sources is small, and it is only suitable for situations with low detection accuracy requirements

The performance of the maximum likelihood and subspace fitting algorithm is better than that of the MUSIC algorithm, but its calculation process requires repeated iterations, and the convergence speed is greatly reduced under the condition of multiple signal sources, which will significantly increase the computational complexity

[0010] In addition, most of the existing algorithms are proposed based on the use of sound pressure sensors; with the application of vector sensors, although many algorithms have been effectively transplanted to the sound vector sensor, usually only the vibration velocity of the vector sensor The information is only processed as an independent array element, and the coherence of sound pressure and vibration velocity in the vector sensor and the resulting ability to resist isotropic noise are not fully utilized

Therefore, the existing methods cannot bring out the advantages and characteristics of the vector sensor; the method needs to be improved and perfected, so as to achieve high-precision detection results with a small computational complexity

Images