2D-DOA Estimation for MIMO Radar on Monostatic L-Shaped Minimum-Redundancy Linear Arrays

2014 ◽  
Vol 926-930 ◽  
pp. 2871-2875
Author(s):  
Ying Li ◽  
Gong Zhang
Keyword(s):  
High Efficiency ◽  
Linear Array ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Radar System ◽  
Signal Classification ◽  
Multiple Signal Classification ◽  
Linear Arrays ◽  
2D Doa Estimation ◽  
Better Than

This paper discussed the problem of two dimensional (2D) direction of arrival (DOA) estimation for multi-input multi-output (MIMO) radar. The minimum-redundancy linear array (MLRA) is introduced into the transmitting array and receiving array, which enables the high efficiency of the radar system. By utilizing the algorithm of multiple signal classification (MUSIC), we illustrate that the proposed scheme performs better than the uniform linear arrays (ULA) configuration under the same conditions. Simulation results verify the effectiveness of our scheme.

scholarly journals The PARAFAC-MUSIC Algorithm for DOA Estimation with Doppler Frequency in a MIMO Radar System

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Nan Wang ◽  
Wenguang Wang ◽  
Fan Zhang ◽  
Yunneng Yuan
Keyword(s):  
Doppler Frequency ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Radar System ◽  
Signal Classification ◽  
Moving Targets ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
Parallel Factor ◽  
Simulation Results

The PARAFAC-MUSIC algorithm is proposed to estimate the direction-of-arrival (DOA) of the targets with Doppler frequency in a monostatic MIMO radar system in this paper. To estimate the Doppler frequency, the PARAFAC (parallel factor) algorithm is firstly utilized in the proposed algorithm, and after the compensation of Doppler frequency, MUSIC (multiple signal classification) algorithm is applied to estimate the DOA. By these two steps, the DOA of moving targets can be estimated successfully. Simulation results show that the proposed PARAFAC-MUSIC algorithm has a higher accuracy than the PARAFAC algorithm and the MUSIC algorithm in DOA estimation.

scholarly journals A Novel Algorithm to Estimate Closely Spaced Source DOA

2017 ◽  
Vol 7 (4) ◽  
pp. 2109 ◽  
Author(s):  
Sidi Mohamed Hadj Irid ◽  
Samir Kameche ◽  
Said Assous
Keyword(s):  
Classical Music ◽  
Linear Array ◽  
Doa Estimation ◽  
Spatial Sampling ◽  
Signal Classification ◽  
Far Field ◽  
Uniform Linear Array ◽  
Resolution Method ◽  
Multiple Signal Classification ◽  
High Resolution Method

<p>In order to improve resolution and direction of arrival (DOA) estimation of two closely spaced sources, in context of array processing, a new algorithm is presented. However, the proposed algorithm combines both spatial sampling technic to widen the resolution and a high resolution method which is the Multiple Signal Classification (MUSIC) to estimate the DOA of two closely spaced sources impinging on the far-field of Uniform Linear Array (ULA). Simulations examples are discussed to demonstrate the performance and the effectiveness of the proposed approach (referred as Spatial sampling MUSIC SS-MUSIC) compared to the classical MUSIC method when it’s used alone in this context.</p>

The application of high-resolution methods for DOA estimation using a linear antenna array

2014 ◽  
Vol 7 (1) ◽  
pp. 87-94 ◽  
Author(s):  
Lotfi Osman ◽  
Imen Sfar ◽  
Ali Gharsallah
Keyword(s):  
Antenna Array ◽  
Linear Array ◽  
Doa Estimation ◽  
Signal Classification ◽  
Multiple Signal Classification ◽  
Signal Parameters ◽  
Radio Frequency Signals ◽  
Observation Space ◽  
Signal Covariance Matrix

This paper presents results of direction of arrival (DOA) estimation using multiple signal classification (MUSIC), Root-MUSIC, and estimation of signal parameters via rotational invariance technique algorithms. As is well known, these algorithms are mainly based on the specific properties of the signal covariance matrix as well as the decomposition of the observation space into two subspaces, one for the signal and the other for the noise. Here, we are particularly interested in the quality of sources localization considering only the case of uncorrelated radio frequency signals impinging on an antenna array. A measurement system consisting of a linear array antenna and a five-port network applicable to a demodulator such as a receiver is used for the DOA estimation process. Co-simulations performed with the Advanced Device System and Matlab yielded interesting results not only on their performance but also on their limitation.

scholarly journals DOA Estimation for Multiple Targets in MIMO Radar with Nonorthogonal Signals

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Zhenxin Cao ◽  
Peng Chen ◽  
Zhimin Chen ◽  
Yi Jin
Keyword(s):  
Correlation Matrix ◽  
Cross Correlation ◽  
Multiple Input Multiple Output ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Signal Classification ◽  
Multiple Signal Classification ◽  
Estimation Performance ◽  
Multiple Input ◽  
Input Multiple Output

This paper addresses the direction of arrival (DOA) estimation problem in the colocated multiple-input multiple-output (MIMO) radar with nonorthogonal signals. The maximum number of targets that can be estimated is theoretically derived as rankRsN, where N denotes the number of receiving antennas and Rs is the cross-correlation matrix of the transmitted signals. Therefore, with the rank-deficient cross-correlation matrix, the maximum number that can be estimated is less than the radar with orthogonal signals. Then, a multiple signal classification- (MUSIC-) based algorithm is given for the nonorthogonal signals. Furthermore, the DOA estimation performance is also theoretically analyzed by the Carmér-Rao lower bound. Simulation results show that the nonorthogonality degrades the DOA estimation performance only in the scenario with the rank-deficient cross-correlation matrix.

scholarly journals Computationally Efficient Direction-of-Arrival Estimation Algorithms for a Cubic Coprime Array

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 136
Author(s):  
Pan Gong ◽  
Xixin Chen
Keyword(s):  
Covariance Matrix ◽  
Lower Boundary ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Signal Classification ◽  
Computationally Efficient ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
Multiple Signal

In this paper, we investigate the problem of direction-of-arrival (DOA) estimation for massive multi-input multi-output (MIMO) radar, and propose a total array-based multiple signals classification (TA-MUSIC) algorithm for two-dimensional direction-of-arrival (DOA) estimation with a coprime cubic array (CCA). Unlike the conventional multiple signal classification (MUSIC) algorithm, the TA-MUSIC algorithm employs not only the auto-covariance matrix but also the mutual covariance matrix by stacking the received signals of two sub cubic arrays so that full degrees of freedom (DOFs) can be utilized. We verified that the phase ambiguity problem can be eliminated by employing the coprime property. Moreover, to achieve lower complexity, we explored the estimation of signal parameters via the rotational invariance technique (ESPRIT)-based multiple signal classification (E-MUSIC) algorithm, which uses a successive scheme to be computationally efficient. The Cramer–Rao bound (CRB) was taken as a theoretical benchmark for the lower boundary of the unbiased estimate. Finally, numerical simulations were conducted in order to demonstrate the effectiveness and superiority of the proposed algorithms.

scholarly journals The iterative search approach DOA estimation of monostatic L-shaped array MIMO radar

2018 ◽  
Vol 12 (2) ◽  
pp. 101-109
Author(s):  
Guan Jishi ◽  
Shi Yaowu ◽  
Deng Lifei ◽  
Zhu Lanxiang ◽  
Shi Hongwei
Keyword(s):  
Doa Estimation ◽  
Mimo Radar ◽  
Classification Algorithm ◽  
Signal Classification ◽  
Two Dimensional ◽  
Iterative Search ◽  
Multiple Signal Classification ◽  
Multiple Signal ◽  
Searching Method ◽  
Search Approach

In the DOA estimation of monostatic L-shaped array MIMO radar, Multiple Signal Classification algorithm is efficient. But the peak searching process of Multiple Signal Classification algorithm needs large amount of spectrum calculation. Focusing on the spectrum peak searching process of Multiple Signal Classification, an iterative search approach to reduce the calculation amount is proposed. The first- and second-order derivatives of Multiple Signal Classification spectrum functions are achieved and the calculation amount is analyzed. Two-dimensional Newton iteration methods are applied with multisearching threads and derivation information. The searching approach can greatly reduce the computational complexity of Multiple Signal Classification spectrum peak searching. The total calculation amount of the first and second derivatives is about 15 times of the spectrum function. However, in the two-dimensional searching, especially in the high accuracy processes, the amount of searched points can be reduced by ten hundreds times, and the computation is much lower than the common spectrum peak searching method. The simulation results show that when the search thread number reaches 100, the searching process can effectively achieve the entire spectrum peak and get the correct DOA estimation.

Modified Propagator Method for 2-Dimensional DOA Estimation in Monostatic MIMO Radar with L-Shaped Array

2013 ◽  
Vol 846-847 ◽  
pp. 1171-1175
Author(s):  
Xin Li ◽  
Ding Jie Xu ◽  
Xiao Meng Wang
Keyword(s):  
Doa Estimation ◽  
Mimo Radar ◽  
Complex Matrix ◽  
Linear Arrays ◽  
Complex Eigenvalues ◽  
Propagator Method ◽  
The Matrix ◽  
Esprit Algorithm ◽  
Influence Of Noise ◽  
Better Than

A modified propagator method based on L-shaped array for 2-Dimensional (2-D) direction of arrival (DOA) estimation in monostatic MIMO radar is proposed. A cross-correlation matrix, which can eliminate the influence of noise, is constructed by the received data from the two orthogonal uniform linear arrays (ULAs) at x-axis and z-axis. Then the matrix can be utilized to estimate signal subspace of 2-D DOA through propagator method. At last, the elevation and azimuth angles of the 2-D DOA is automatically paired by the complex eigenvalues of a low-order complex matrix. The 2-D DOA estimation performance of the proposed method is better than conventional propagator method and ESPRIT algorithm. Simulation results verify the effectiveness of the proposed method.

A Root-MUSIC Algorithm for Angle Estimation in Bistatic MIMO Radar

2013 ◽  
Vol 756-759 ◽  
pp. 4031-4035
Author(s):  
Jia Wei Liu ◽  
Ren Zheng Cao ◽  
Xiao Fei Zhang
Keyword(s):  
Doa Estimation ◽  
Mimo Radar ◽  
Signal Classification ◽  
Music Algorithm ◽  
Angle Estimation ◽  
Multiple Signal Classification ◽  
Direction Of Departure ◽  
Multiple Input ◽  
Roots Of Polynomials ◽  
Load Simulation

This paper discusses the problem of direction of departure (DOD) and direction of arrival (DOA) estimation using the root multiple signal classification (MUSIC) algorithm in a bistatic multiple input and multiple output (MIMO) radar. The proposed algorithm gets the estimation of DOA and DOD via computing the roots of polynomials and it avoids the spectral peak searching in the conventional MUSIC algorithm. Thus the Root-MUSIC algorithm has much lower computational load. Simulation results illustrate our proposed algorithm has better angle estimation performance than the conventional algorithms.

Direction of Arrival Estimation with Compact Array Antennas

2009 ◽  
pp. 201-216
Author(s):  
Eddy Taillefer ◽  
Jun Cheng ◽  
Takashi Ohira
Keyword(s):  
Single Port ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Signal Classification ◽  
Direction Of Arrival Estimation ◽  
Multiple Signal Classification ◽  
Array Antennas ◽  
Power Pattern ◽  
Parasitic Array ◽  
Compact Array

This chapter presents direction of arrival (DoA) estimation with a compact array antenna using methods based on reactance switching. The compact array is the single-port electronically steerable parasitic array radiator (Espar) antenna. The antenna beam pattern is controlled though parasitic elements loaded with reactances. DoA estimation using an Espar antenna is proposed with the power pattern cross correlation (PPCC), reactance-domain (RD) multiple signal classification (MUSIC), and, RD estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithms. The three methods exploit the reactance diversity provided by an Espar antenna to correlate different antenna output signals measured at different times and for different reactance values. The authors hope that this chapter allows the researchers to appreciate the issues that may be encountered in the implementation of direction-finding application with a single-port compact array like the Espar antenna.

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