Comparative Analysis of Fourth Order Cumulant Based ESPRIT Algorithms

Higher order statistics based subspace methods are extensively used for Direction of Arrival (DOA) estimation. This paper compares different versions of fourth order cumulant based ESPRIT algorithms for DOA estimation in terms of accuracy, resolution and computational complexity. The popularity of cumulant based methods is because of their better resolution and their ability to perform well even in the case of high noise correlation. To further improve the accuracy and resolution, recently multiple invariance based cumulant ESPRIT methods are developed. However, they are far more computationally expensive which poses a question on their utility. A simple directive approach is proposed in order to reduce the computational complexity for the case of multiple in variances. It is observed that directive approach gives better results in terms of computational time and accuracy. Computer simulations are presented to compare all the algorithms in consideration.

[1]  K. J. Ray Liu,et al.  Handbook on Array Processing and Sensor Networks , 2010 .

[2]  Muhammad Tufail,et al.  Multiple snapshot Beamspace Matrix Pencil method for direction of arrival estimation , 2010, 2010 The 2nd International Conference on Industrial Mechatronics and Automation.

[3]  S. Mirza,et al.  Hybrid simulated annealing image reconstruction for transmission tomography , 2009 .

[4]  Ammar Ahmed,et al.  Widely linear matrix pencil based frequency estimation in power systems , 2013, 2013 IEEE 9th International Conference on Emerging Technologies (ICET).

[5]  Muhammad Tufail,et al.  Multiple invariance cumulant ESPRIT for DOA estimation , 2014, 2014 International Conference on Robotics and Emerging Allied Technologies in Engineering (iCREATE).

[6]  Andreas Spanias,et al.  Narrowband Direction of Arrival Estimation for Antenna Arrays , 2008, Narrowband Direction of Arrival Estimation for Antenna Arrays.

[7]  Zhongfu Ye,et al.  DOA estimation for non-Gaussian signals using fourth-order cumulants , 2009 .

[8]  T. Engin Tuncer,et al.  Classical and Modern Direction-of-Arrival Estimation , 2009 .

[9]  Muhammad Arif,et al.  Determination of optimal number of projections and parametric sensitivity analysis of operators for parallel‐ray transmission tomography using hybrid continuous genetic algorithm , 2007, Int. J. Imaging Syst. Technol..

[10]  Nan Hu,et al.  A sparse recovery algorithm for DOA estimation using weighted subspace fitting , 2012, Signal Process..

[11]  Muhammad Tufail,et al.  Computationally efficient 2D beamspace matrix pencil method for direction of arrival estimation , 2010, Digit. Signal Process..

[12]  Prabhjot Singh,et al.  Concentric Circular Antenna Array design using hybrid differential evolution with Biogeography Based Optimization , 2013, 2013 IEEE International Conference on Computational Intelligence and Computing Research.

[13]  Björn E. Ottersten,et al.  Multiple invariance ESPRIT , 1992, IEEE Trans. Signal Process..

[14]  Muhammad Tufail,et al.  Beamspace matrix pencil method for direction of arrival estimation , 2009, IEICE Electron. Express.

[15]  Thomas Kailath,et al.  Comparative performance of ESPRIT and MUSIC for direction-of-arrival estimation , 1987, ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[16]  P. Palanisamy,et al.  Direction of Arrival Estimation Based on Fourth-Order Cumulant Using Propagator Method , 2009 .

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

[18]  Arumugam Nallanathan,et al.  Efficient Beamforming Training for 60-GHz Millimeter-Wave Communications: A Novel Numerical Optimization Framework , 2014, IEEE Transactions on Vehicular Technology.

[19]  Christodoulos A. Floudas,et al.  A review of recent advances in global optimization , 2009, J. Glob. Optim..

[20]  C. L. Nikias,et al.  The Esprit Algorithm With Higher-order Statistics , 1989, Workshop on Higher-Order Spectral Analysis.

[21]  Muhammad Arif,et al.  Optimization of Projections for Parallel-Ray Transmission Tomography Using Genetic Algorithm , 2008, IMTIC.

[22]  Ammar Ahmed,et al.  Subspace based widely linear frequency estimation in sustainable energy systems , 2014, 2014 International Conference on Robotics and Emerging Allied Technologies in Engineering (iCREATE).

[23]  Yuan-Hwang Chen,et al.  A modified cumulant matrix for DOA estimation , 1994, IEEE Trans. Signal Process..