Association of DOA Estimation From Two ULAs

In this paper, we consider the problem of associating estimated direction-of-arrival (DOA) angles of multiple targets using two uniform linear arrays (ULAs) of sensors. It is categorized as a multisource information aggregation problem, in which we devise our algorithm in the following two steps: (1) Estimate two sets of DOA angles from two ULAs, respectively, and (2) associate estimated DOA angles with the targets. Note, however, that the primary focus of this paper is the second step, i.e., association of DOA angles with the corresponding targets, whereas for the first step, we use the well-known improved polynomial rooting method. Generally speaking, if there are q targets, we have q! possible association pairs, among which, there is only one set corresponding to the correct association. Since target signals must be correlated in the observations of two ULAs, we can determine how to associate the DOA angles computed from step 1 by evaluating the covariance matrix of the data received at the two ULAs. The algorithm is effective, robust, and accurate in classifying the DOA angles into the correct association.

[1]  Michael D. Zoltowski,et al.  Eigenstructure techniques for 2-D angle estimation with uniform circular arrays , 1994, IEEE Trans. Signal Process..

[2]  Michael Georgiopoulos,et al.  A neural network-based smart antenna for multiple source tracking , 2000 .

[3]  Keith Q. T. Zhang,et al.  Information theoretic criteria for the determination of the number of signals in spatially correlated noise , 1993, IEEE Trans. Signal Process..

[4]  Constantine A. Balanis,et al.  Uniform circular arrays for smart antennas , 2005 .

[5]  Arthur Jay Barabell,et al.  Improving the resolution performance of eigenstructure-based direction-finding algorithms , 1983, ICASSP.

[6]  Etienne Barnard,et al.  Two-dimensional superresolution radar imaging using the MUSIC algorithm , 1994 .

[7]  Z. Bai,et al.  On detection of the number of signals in presence of white noise , 1985 .

[8]  Li Bai,et al.  Efficient DOA estimation method employing unitary improved polynomial rooting , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

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

[10]  M. Bartlett Periodogram analysis and continuous spectra. , 1950, Biometrika.

[11]  Thomas Kailath,et al.  ESPRIT-A subspace rotation approach to estimation of parameters of cisoids in noise , 1986, IEEE Trans. Acoust. Speech Signal Process..

[12]  M. Pastorino,et al.  A smart antenna system for direction of arrival estimation based on a support vector regression , 2005, IEEE Transactions on Antennas and Propagation.

[13]  Eric M. Dowling,et al.  Efficient direction-finding methods employing forward/backward averaging , 1994, IEEE Trans. Signal Process..

[14]  Martin Haardt,et al.  Unitary root-MUSIC with a real-valued eigendecomposition: a theoretical and experimental performance study , 2000, IEEE Trans. Signal Process..

[15]  Zhi-Dong Bai,et al.  On rates of convergence of efficient detection criteria in signal processing with white noise , 1989, IEEE Trans. Inf. Theory.

[16]  R. Kumaresan,et al.  Estimating the Angles of Arrival of Multiple Plane Waves , 1983, IEEE Transactions on Aerospace and Electronic Systems.

[17]  T.K. Sarkar,et al.  Direction of arrival estimation based on temporal and spatial processing using a direct data domain (D/sup 3/) approach , 2004, IEEE Transactions on Antennas and Propagation.

[18]  Simon Haykin,et al.  Advances in spectrum analysis and array processing , 1991 .

[19]  C. R. Rao,et al.  Multitarget angle tracking an algorithm for data association , 1994, IEEE Trans. Signal Process..

[20]  Yuehua Wu,et al.  On determination of the number of signals in spatially correlated noise , 1998, IEEE Trans. Signal Process..

[21]  Julian Besag,et al.  Spatial Statistics and Digital Image Analysis. , 1994 .

[22]  J. Capon High-resolution frequency-wavenumber spectrum analysis , 1969 .