scholarly journals Joint High-Resolution Range and DOA Estimation via MUSIC Method Based on Virtual Two-Dimensional Spatial Smoothing for OFDM Radar

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Rui Zhang ◽  
Ying-Hui Quan ◽  
Sheng-Qi Zhu ◽  
Lei Yang ◽  
Ya-chao Li ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
High Resolution ◽  
Orthogonal Frequency Division Multiplexing ◽  
Doa Estimation ◽  
Joint Estimation ◽  
Two Dimensional ◽  
Spatial Smoothing ◽  
Multiple Signal Classification ◽  
Band Signal ◽  
Angle Parameter

For the purpose of target parameter estimation of the orthogonal frequency-division multiplexing (OFDM) radar, a high-resolution method of joint estimation on range and direction of arrival (DOA) based on OFDM array radar is proposed in this paper. It begins with the design and analysis of an echo model of OFDM array radar. Since there is no coupling between range and angle parameter estimation for a narrow-band signal, a method which exploits the data of one snapshot to estimate the range and angle of the target by means of multiple signal classification (MUSIC) based on virtual two-dimensional spatial smoothing in range and angle dimensions is devised. The proposed method is capable of joint estimating the range and DOA of the target in a high resolution under a single snapshot circumstance. Simulation experiments demonstrate the validity of the proposal.

scholarly journals Thermos Array: Two-Dimensional Sparse Array with Reduced Mutual Coupling

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Lei Sun ◽  
Minglei Yang ◽  
Baixiao Chen
Keyword(s):  
Closed Form ◽  
Degrees Of Freedom ◽  
Mutual Coupling ◽  
Doa Estimation ◽  
Two Dimensional ◽  
Spatial Smoothing ◽  
Planar Arrays ◽  
Half Wavelength ◽  
Planar Array ◽  
Small Spacing

Sparse planar arrays, such as the billboard array, the open box array, and the two-dimensional nested array, have drawn lots of interest owing to their ability of two-dimensional angle estimation. Unfortunately, these arrays often suffer from mutual-coupling problems due to the large number of sensor pairs with small spacing d (usually equal to a half wavelength), which will degrade the performance of direction of arrival (DOA) estimation. Recently, the two-dimensional half-open box array and the hourglass array are proposed to reduce the mutual coupling. But both of them still have many sensor pairs with small spacing d, which implies that the reduction of mutual coupling is still limited. In this paper, we propose a new sparse planar array which has fewer number of sensor pairs with small spacing d. It is named as the thermos array because its shape seems like a thermos. Although the resulting difference coarray (DCA) of the thermos array is not hole-free, a large filled rectangular part in the DCA can be facilitated to perform spatial-smoothing-based DOA estimation. Moreover, it enjoys closed-form expressions for the sensor locations and the number of available degrees of freedom. Simulations show that the thermos array can achieve better DOA estimation performance than the hourglass array in the presence of mutual coupling, which indicates that our thermos array is more robust to the mutual-coupling array.

scholarly journals Fast Estimation Method of Space-Time Two-Dimensional Positioning Parameters Based on Hadamard Product

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Haiwen Li ◽  
Nae Zheng ◽  
Xiyu Song ◽  
Yinghua Tian
Keyword(s):  
Frequency Domain ◽  
Orthogonal Frequency Division Multiplexing ◽  
Hadamard Product ◽  
Estimation Method ◽  
Space Time ◽  
Joint Estimation ◽  
Two Dimensional ◽  
Music Algorithm ◽  
Fast Estimation ◽  
Response Vector

The estimation speed of positioning parameters determines the effectiveness of the positioning system. The time of arrival (TOA) and direction of arrival (DOA) parameters can be estimated by the space-time two-dimensional multiple signal classification (2D-MUSIC) algorithm for array antenna. However, this algorithm needs much time to complete the two-dimensional pseudo spectral peak search, which makes it difficult to apply in practice. Aiming at solving this problem, a fast estimation method of space-time two-dimensional positioning parameters based on Hadamard product is proposed in orthogonal frequency division multiplexing (OFDM) system, and the Cramer-Rao bound (CRB) is also presented. Firstly, according to the channel frequency domain response vector of each array, the channel frequency domain estimation vector is constructed using the Hadamard product form containing location information. Then, the autocorrelation matrix of the channel response vector for the extended array element in frequency domain and the noise subspace are calculated successively. Finally, by combining the closed-form solution and parameter pairing, the fast joint estimation for time delay and arrival direction is accomplished. The theoretical analysis and simulation results show that the proposed algorithm can significantly reduce the computational complexity and guarantee that the estimation accuracy is not only better than estimating signal parameters via rotational invariance techniques (ESPRIT) algorithm and 2D matrix pencil (MP) algorithm but also close to 2D-MUSIC algorithm. Moreover, the proposed algorithm also has certain adaptability to multipath environment and effectively improves the ability of fast acquisition of location parameters.

Study of High Resolution DOA Estimation Based on Vector-Hydrophone

2014 ◽  
Vol 926-930 ◽  
pp. 1795-1799
Author(s):  
Hao Zhou ◽  
Zhi Jie Huo
Keyword(s):  
Doa Estimation ◽  
Spatial Spectrum ◽  
Classification Algorithm ◽  
Signal Classification ◽  
Two Dimensional ◽  
Multiple Signal Classification ◽  
Variance Matrix ◽  
Multiple Signal ◽  
Vector Hydrophone ◽  
Eigen Decomposition

Vector-hydrophone can simultaneously measure acoustic pressure and orthogonal components of the particle velocity. The 180o ambiguity in DOA estimation can be eliminated using information obtained by vector hydrophone array. Multiple signal classification algorithm is a method that takes the eigen-decomposition of data co-variance matrix to obtain the estimation of signal spatial spectrum. The two-dimensional DOA of acoustic sources is estimated based on multiple signal classification algorithm using the vector-hydrophone uniform linear array. Simulation results show that better DOA resolution performance can be obtained from vector hydrophones. Furthermore, the paper takes the de-correlation of correlated sources using spatial smoothness technology to obtain perfect performance of two-dimensional DOA estimation.

scholarly journals Two-Dimensional Direction-of-Arrival Estimation for Trilinear Decomposition-Based Monostatic Cross MIMO Radar

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Fangqing Wen ◽  
Gong Zhang
Keyword(s):  
Direction Of Arrival ◽  
Doa Estimation ◽  
Low Complexity ◽  
Mimo Radar ◽  
Eigenvalue Decomposition ◽  
Two Dimensional ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
Trilinear Decomposition ◽  
Signal Covariance Matrix

A low complexity monostatic cross multiple-in multiple-out (MIMO) radar scheme is proposed in this paper. The minimum-redundancy linear array (MRLA) is introduced in the cross radar to improve the efficiency of the array elements. The two-dimensional direction-of-arrival (DOA) estimation problem links to the trilinear model, which automatically pairs the estimated two-dimensional angles, requiring neither eigenvalue decomposition of received signal covariance matrix nor spectral peak searching. The proposed scheme performs better than the uniform linear arrays (ULA) configuration under the same conditions, and the proposed algorithm has less computational complexity than that of multiple signal classification (MUSIC) algorithm. Simulation results show the effectiveness of our scheme.

scholarly journals Three-Dimensional Target Localization and Cramér-Rao Bound for Two-Dimensional OFDM-MIMO Radar

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Xingxing Li ◽  
Dangwei Wang ◽  
Xiaoyan Ma
Keyword(s):  
Orthogonal Frequency Division Multiplexing ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Three Dimensional ◽  
Mimo Radar ◽  
Joint Estimation ◽  
Target Localization ◽  
Two Dimensional ◽  
Frequency Diversity ◽  
Cramer Rao Bound

Target localization using a frequency diversity multiple-input multiple-output (MIMO) system is one of the hottest research directions in the radar society. In this paper, three-dimensional (3D) target localization is considered for two-dimensional MIMO radar with orthogonal frequency division multiplexing linear frequency modulated (OFDM-LFM) waveforms. To realize joint estimation for range and angle in azimuth and elevation, the range-angle-dependent beam pattern with high range resolution is produced by the OFDM-LFM waveform. Then, the 3D target localization proposal is presented and the corresponding closed-form expressions of Cramér-Rao bound (CRB) are derived. Furthermore, for mitigating the coupling of angle and range and further improving the estimation precision, a CRB optimization method is proposed. Different from the existing methods of FDA-based radar, the proposed method can provide higher range estimation because of multiple transmitted frequency bands. Numerical simulation results are provided to demonstrate the effectiveness of the proposed approach and its improved performance of target localization.

scholarly journals DOA Estimation of Two-Dimensional Coherently Distributed Sources Based on Spatial Smoothing of Uniform Rectangular Arrays

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Tao Wu ◽  
Yiwen Li ◽  
Xiaofeng Zhang ◽  
Yijie Huang ◽  
Qingyue Gu ◽  
...  
Keyword(s):  
Doa Estimation ◽  
Covariance Matrices ◽  
Two Dimensional ◽  
Spatial Smoothing ◽  
Rectangular Array ◽  
Signal Distribution ◽  
Sample Covariance Matrices ◽  
Sample Covariance ◽  
Propagator Method ◽  
Uniform Rectangular Array

Aiming at the direction-of-arrival (DOA) estimation of two-dimensional (2D) coherently distributed (CD) sources which are coherent with each other, we explore the propagator method based on spatial smoothing of a uniform rectangular array (URA). The rotational invariance relationships with respect to the nominal azimuth and nominal elevation are obtained under the small angular spreads assumption. A propagator operator is constructed through spatial smoothing of sample covariance matrices firstly. Then, combination of propagator and identical matrix is divided according to rotational operators, and the nominal angles can be obtained through eigendecomposition lastly. Realizing angle matching automatically, the proposed method can estimate multiple DOAs of 2D coherent CD sources without spectral peak searching and prior knowledge of deterministic angular signal distribution function. Simulations are conducted to verify the effectiveness of the proposed method.

scholarly journals Direction of Arrival Estimation Using Augmentation of Coprime Arrays

Information ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 277 ◽  
Author(s):  
Tehseen Hassan ◽  
Fei Gao ◽  
Babur Jalal ◽  
Sheeraz Arif
Keyword(s):  
Covariance Matrix ◽  
Degrees Of Freedom ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Spatial Smoothing ◽  
Multiple Signal Classification ◽  
Coprime Arrays ◽  
The Difference ◽  
Source Signals

Recently, direction of arrival (DOA) estimation premised on the sparse arrays interpolation approaches, such as co-prime arrays (CPA) and nested array, have attained extensive attention because of the effectiveness and capability of providing higher degrees of freedom (DOFs). The co-prime array interpolation approach can detect O(MN) paths with O(M + N) sensors in the array. However, the presence of missing elements (holes) in the difference coarray has limited the number of DOFs. To implement co-prime coarray on subspace based DOA estimation algorithm namely multiple signal classification (MUSIC), a reshaping operation followed by the spatial smoothing technique have been presented in the literature. In this paper, an active coarray interpolation (ACI) is proposed to efficiently recovering the covariance matrix of the augmented coarray from the original covariance matrix of source signals with no vectorizing and spatial smoothing operation; thus, the computational complexity reduces significantly. Moreover, the numerical simulations of the proposed ACI approach offers better performance compared to its counterparts.

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