A High-Efficiency 3D-DOA Estimation Algorithm for Unknown Sources Number

Signal estimation is the basic function, applications and services of Internet of Things. Due to the problems of spike non-normal noise, multiple signal classification (MSC) algorithm will lose the tenacity for the 3D-DOA(3D-direction of arrival) estimation of multiple-sources and obtain inaccurate results. In this paper, therefore, we propose a new 3D-DOA estimation algorithm for amorphic multiple-sources based on nested array and FLOC-MSC method. This new scheme firstly quantifies to covariance matrix to extend the array aperture and it generates new covariance matrix. In addition, the new method utilizes row vector of covariance matrix to construct Toeplitz matrix and it uses the joint diagonal structure of Toeplitz matrix to create cost function. Afterwards, array output matrix is extended from second geometric moment to lower order moment. We can get spatial spectrum based on covariation matrix through analyzing covariation matrix. Eventually, after gradient operation for spatial spectrum, we conduct extremum searching through one-dimensional spectrum peak search and find DOA of each source. Our algorithm is compared to covariance fitting optimization technique and the fast approximated power iteration-total least square-estimation of signal parameters via rotational invariance technique (FAPI-TLS-ESPRIT) algorithm using the TLS-ESPRIT method and the subspace updating via FAPI-algorithm. At the end, experiments show that this new algorithm can accurately estimate 3D-DOA of mutiple-sources under the spike non-normal noise condition, especially at low signal-to-noise ratio (SNR) values with impulse. The new method has high estimation precision and resolution with unknown and underdetermined number of sources.