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Chen C.,Nanjing University of Aeronautics and Astronautics | Zhang X.,Nanjing University of Aeronautics and Astronautics | Zhang X.,Nanjing Panda Electronics Group | Zhang X.,Xiamen University
International Journal of Electronics | Year: 2013

In this article, we study the problem of four-dimensional angles estimation for bistatic multiple-input multiple-output (MIMO) radar with arbitrary arrays, and propose a joint two-dimensional direction of departure (2D-DOD) and two-dimensional direction of arrival (2D-DOA) estimation algorithm. Our algorithm is to extend the propagator method (PM) for angle estimation in MIMO radar. The proposed algorithm does not require peak searching and eigenvalue decomposition of received signal covariance matrix, because of this, it has low computational complexity. And it can achieve automatic pairing of four-dimensional angles. Furthermore, the proposed algorithm has much better angle estimation performance than interpolated estimation method of signal parameters via rotational invariance techniques (ESPRIT), and has very close angle estimation performance to ESPRIT-like algorithm which has higher computational cost than the proposed algorithm. We also analyze the complexity and angle estimation error of the algorithm, and derive the Cramer-Rao bound (CRB). The simulation results verify the effectiveness and improvement of the proposed algorithm. © 2013 Taylor and Francis Group, LLC.


Xiaofei Z.,Nanjing University of Aeronautics and Astronautics | Xiaofei Z.,Nanjing Panda Electronics Group | Xiaofei Z.,Nanjing University | Ming Z.,Nanjing University of Aeronautics and Astronautics | And 2 more authors.
Multidimensional Systems and Signal Processing | Year: 2014

This paper discusses the problem of two-dimensional (2D) direction of arrival (DOA) estimation for acoustic vector-sensor array, and derives a successive multiple signal classification (MUSIC) algorithm therein. The proposed algorithm obtains initial estimations of the azimuth and elevation angles obtained from the signal subspace, and uses successively one-dimensional local searches to achieve the joint estimation of 2D-DOA. The proposed algorithm, which requires the one-dimension local searches, can avoid the high computational cost within 2D-MUSIC algorithm. The proposed algorithm can obtain automatically-paired 2D-DOA estimation for acoustic vector-sensor array, and it has better DOA estimation performance than propagator method, estimation of signal parameters via rotational invariance technique algorithm and trilinear decomposition algorithm. Meanwhile, it has very close angle estimation to 2D-MUSIC algorithm. Furthermore, it is suitable for non-uniform linear arrays, works well for the sources with the same azimuth angle, and imposes less constraint on the sensor spacing, which does not have to be restricted within half-wavelength. We have also derived the mean-square error of DOA estimation of the proposed algorithm and the Cramer-Rao bound of DOA estimation. Simulation results verify the usefulness of the proposed algorithm. © 2013 Springer Science+Business Media New York.


Zhang X.,Nanjing University of Aeronautics and Astronautics | Zhang X.,Nanjing Panda Electronics Group | Zhang X.,Nanjing University | Zhou M.,Nanjing University of Aeronautics and Astronautics | Li J.,Nanjing University of Aeronautics and Astronautics
Sensors (Switzerland) | Year: 2013

In this paper, we combine the acoustic vector-sensor array parameter estimation problem with the parallel profiles with linear dependencies (PARALIND) model, which was originally applied to biology and chemistry. Exploiting the PARALIND decomposition approach, we propose a blind coherent two-dimensional direction of arrival (2D-DOA) estimation algorithm for arbitrarily spaced acoustic vector-sensor arrays subject to unknown locations. The proposed algorithm works well to achieve automatically paired azimuth and elevation angles for coherent and incoherent angle estimation of acoustic vector-sensor arrays, as well as the paired correlated matrix of the sources. Our algorithm, in contrast with conventional coherent angle estimation algorithms such as the forward backward spatial smoothing (FBSS) estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm, not only has much better angle estimation performance, even for closely-spaced sources, but is also available for arbitrary arrays. Simulation results verify the effectiveness of our algorithm. © 2013 by the authors; licensee MDPI, Basel, Switzerland.


Zhang X.,Nanjing University of Aeronautics and Astronautics | Zhang X.,Nanjing Panda Electronics Group | Zhang X.,Nanjing University | Cao R.,Nanjing University of Aeronautics and Astronautics | Zhou M.,Nanjing University of Aeronautics and Astronautics
Eurasip Journal on Advances in Signal Processing | Year: 2013

Abstracts: In this paper, we propose a noncircular-parallel factor (NC-PARAFAC) algorithm for two-dimensional direction of arrival (DOA) estimation of noncircular signals for acoustic vector-sensor array. The proposed algorithm enhances the angle estimation performance via utilizing the noncircularity of the signals, and it can be suitable for arbitrary array subjected to unknown locations and achieve automatically paired two-dimensional angle estimation. The proposed algorithm has better angle estimation performance than estimation of signal parameters via rotational invariance technique, PARAFAC algorithm, and propagator method. Furthermore, the proposed algorithm has a lower computational complexity than the PARAFAC algorithm. We also derive the Crámer-Rao bound of DOA estimation of noncircular signal in acoustic vector-sensor array. The simulation results verify the effectiveness of the algorithm. © 2013 Zhang et al.; licensee Springer.


Chen H.,Nanjing University of Aeronautics and Astronautics | Zhang X.,Nanjing University of Aeronautics and Astronautics | Zhang X.,Nanjing Panda Electronics Group | Zhang X.,Nanjing University
Wireless Personal Communications | Year: 2013

This paper discusses the problem of two-dimensional direction of arrival estimation of coherent sources for acoustic vector sensors array. Compared with subspace-based methods, such as root multiple signal classification and estimation of signal parameters via rotational invariance technique, the propagator method (PM) has lower computational complexity. However, only in high-snapshots situation, can the PM algorithm enjoy a better estimation performance. Besides, all of these algorithms mentioned above cannot work for coherent sources. In this paper, we combine PM algorithm with Toeplitz Hermitian matrix reconstruction, and propose an improved algorithm, which works well in the case of coherent signals and a single snapshot. Furthermore, the proposed method can achieve automatically paired two-dimensional angle estimation. Simulation results verify that the proposed method has the better angle performance and less computational complexity than spatial smoothing methods. © Springer Science+Business Media New York 2013.


Zhang X.F.,Nanjing University of Aeronautics and Astronautics | Zhang X.F.,Nanjing Panda Electronics Group | Yu H.X.,Nanjing University of Aeronautics and Astronautics | Li J.F.,Nanjing University of Aeronautics and Astronautics | Ben D.,Nanjing University of Aeronautics and Astronautics
Advanced Materials Research | Year: 2013

This paper discusses the signal detection problem with rectangular array, and links the detection problem to the compressed sensing trilinear model. Exploiting this link, we derive a compressed sensing trilinear decomposition-based signal detection algorithm, which can obtain the estimation of the signals from different directions. The proposed algorithm requires no spectral peak searching, and it has lower complexity than conventional trilinear decomposition-based method. Simulation results illustrate the performance of the algorithm. © (2013) Trans Tech Publications, Switzerland.


Zhang X.F.,Nanjing University of Aeronautics and Astronautics | Zhang X.F.,Nanjing Panda Electronics Group | Li J.F.,Nanjing University of Aeronautics and Astronautics | Zhou M.,Nanjing University of Aeronautics and Astronautics | Ben D.,Nanjing University of Aeronautics and Astronautics
Applied Mechanics and Materials | Year: 2013

In this paper, we address the transmit angle and receive angle estimation problem for a bistatic multiple-input multiple-output (MIMO) radar. This paper links MIMO radar angle estimation problem to the compressed sensing trilinear model. Exploiting this link, it derives a compressed sensing trilinear model-based angle estimation algorithm, which can obtain automatically paired two-dimensional angle estimation. The proposed algorithm requires no spectral peak searching or pair matching, and it has better angle estimation performance than conventional algorithms including estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. Simulation results illustrate performance of the algorithm. © 2013 Trans Tech Publications Ltd, Switzerland.


Zhou M.,Nanjing University of Aeronautics and Astronautics | Zhang X.,Nanjing University of Aeronautics and Astronautics | Zhang X.,Nanjing Panda Electronics Group
Wireless Personal Communications | Year: 2015

In this paper, we propose a new blind direction-of-departure (DOD), direction-of-arrival (DOA) and polarization estimation algorithm for bistatic polarimetric multiple-input multiple-output radar using dimension reduction multiple signal classification. The proposed algorithm obtains the initial estimation of DOD via the signal subspace, then utilizes one-dimension (1-D) local searching to estimate more accurate DOD according to the initial estimation of DOD, and finally joint estimates DOA and polarization by means of the receive polarization steering vector. The proposed algorithm, which convert multiple-dimension peak searching to 1-D local searching, can avoid the high computation cost. Simulation results show that the proposed algorithm has better angle and polarization parameter estimation performance than both estimation of signal parameters via rotational invariance technique algorithm and trilinear decomposition algorithm. Furthermore, the proposed algorithm can obtain automatically paired multi-dimensional parameter estimation. © 2014, Springer Science+Business Media New York.


Li J.,Nanjing University of Aeronautics and Astronautics | Zhang X.,Nanjing Panda Electronics Group
Multidimensional Systems and Signal Processing | Year: 2013

An algorithm based on sparse representation for joint angle and Doppler frequency estimation in multiple-input multiple-output radar is proposed. Through the data reconstruction, the algorithm only requires the dictionary for one-dimensional angle [e.g. direction of departure (DOD)], which reduces the computational complexity compared to conventional method using dictionary for two-dimensional angle. The DOD can be estimated by finding the non-zero rows in the recovered matrix, which also contains the information of the direction of arrival (DOA) and the Doppler frequency, and they can be achieved via singular value decomposition and least squares (LS) principle. The estimated DOD, DOA and Doppler frequency can be automatically paired and the parameter estimation performance of the proposed algorithm is better than that of estimation of signal parameters via rotational invariance techniques (ESPRIT)-based algorithm and parallel factor (PARAFAC) method. Furthermore, the proposed algorithm requires no knowledge of the number of targets and works well for coherent targets. Simulation results verify the effectiveness of the algorithm. © 2013, Springer Science+Business Media New York.


Chen C.,Nanjing University of Aeronautics and Astronautics | Zhang X.,Nanjing University of Aeronautics and Astronautics | Zhang X.,Nanjing Panda Electronics Group
Circuits, Systems, and Signal Processing | Year: 2013

This paper discusses the problem of the direction of departure (DOD) and the direction of arrival (DOA) estimation for multi-input multi-output (MIMO) radar with array gain-phase errors. In this paper, we propose a propagator method (PM)-like algorithm for joint angle and array gain-phase errors estimation in MIMO radar. The proposed method not only yields automatically paired estimates of the angles and gain-phase errors but also has much better gain-phase errors estimation performance than the estimation of signal parameters via rotational invariance techniques (ESPRIT)-like algorithm; this has higher computational cost than the proposed algorithm. Furthermore, the proposed algorithm has angle estimation performance very close to ESPRIT-like algorithm. We also derive the Cramér-Rao bound (CRB) for MIMO radar with array gain-phase errors. Simulation results present the usefulness of our approach. © 2012 Springer Science+Business Media New York.

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