A new model for DOA estimation and its solution by multi-target intermittent particle swarm optimization

Currently, the widely used methods for direction of arrival (DOA) estimation were constructed based on the subspace, such as Multiple Signal Classification (MUSIC) and Estimating Signal Parameter via Rotational Invariance Techniques (ESPRIT), which required that the number of sources is known beforehand. In this paper, a new method based on the Vector Error Model (VEM) for estimating the DOAs was proposed, which do not need the sources number in advance. The comparison of the performance between the VEM and the MUSIC model for DOA problem was given to demonstrate the effectiveness of our method. The algorithm of multi-target intermittent particle swarm optimization (MIPSO) was adopted to solve the VEM, and the performance of the VEM-MIPSO method was analysed through simulations for a uniform linear array and an L-shaped array respectively. The results showed that: (1) the VEM was an effective model to solve the DOA estimation without prior knowledge of the sources number; (2) the MIPSO was an efficient algorithm to solve the DOA estimation with high precision.

[1]  Junpeng Shi,et al.  Orthogonal projection method for DOA estimation in low-altitude environment based on signal subspace , 2018 .

[2]  D. Luenberger.  Optimization by Vector Space Methods , 1968 .

[3]  Josiah Poon,et al.  Generalized Gaussian reference curve measurement model for high‐performance liquid chromatography with diode array detector separation and its solution by multi‐target intermittent particle swarm optimization , 2015 .

[4]  Ying Zhang,et al.  MUSIC-Like DOA Estimation Without Estimating the Number of Sources , 2010, IEEE Transactions on Signal Processing.

[5]  Yongjun Zhao,et al.  Joint Source Number Detection and DOA Estimation via Reversible Jump MCMC , 2008, 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application.

[6]  Zhang Yong-shun,et al.  An algorithm on high resolution DOA estimation with unknown number of signal sources , 2004, ICMMT 4th International Conference on, Proceedings Microwave and Millimeter Wave Technology, 2004..

[7]  Zhao Yong-jun,et al.  Joint Source Number Detection and DOA Estimation via Reversible Jump MCMC , 2008, PACIIA 2008.

[8]  Hao Chen,et al.  A parallel model of independent component analysis constrained by a 5-parameter reference curve and its solution by multi-target particle swarm optimization , 2014 .

[9]  R. O. Schmidt,et al.  Multiple emitter location and signal Parameter estimation , 1986 .

[10]  Petar M. Djuric,et al.  A model selection rule for sinusoids in white Gaussian noise , 1996, IEEE Trans. Signal Process..

[11]  In-O Choi,et al.  Efficient sparse representation algorithm for accurate DOA estimation of multiple targets with single measurement vector , 2018 .

[12]  Linlin Chen,et al.  Prediction of nucleosome rotational positioning in yeast and human genomes based on sequence-dependent DNA anisotropy , 2014, BMC Bioinformatics.

[13]  Jian Li,et al.  Performance analysis of G-MUSIC based DOA estimator with random linear array: A single source case , 2018, Signal Process..

[14]  Junbin Gao,et al.  An improved independent component analysis model for 3D chromatogram separation and its solution by multi-areas genetic algorithm , 2014, BMC Bioinformatics.

[15]  Benjamin Friedlander,et al.  Asymptotic performance analysis of ESPRIT, higher order ESPRIT, and virtual ESPRIT algorithms , 1996, IEEE Trans. Signal Process..

[16]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[17]  Oliver Thiergart,et al.  An informed LCMV filter based on multiple instantaneous direction-of-arrival estimates , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.