scholarly journals Time Reversal Linearly Constrained Minimum Power Algorithm for Direction of Arrival Estimation in Diffuse Multipath Environments

2020 ◽  
Vol 12 (20) ◽  
pp. 3344
Author(s):  
Chao Xiong ◽  
Chongyi Fan ◽  
Xiaotao Huang
Keyword(s):  
Covariance Matrix ◽  
Time Reversal ◽  
Direction Of Arrival ◽  
Mimo Radar ◽  
Estimation Accuracy ◽  
Angle Estimation ◽  
Minimum Power ◽  
Sensing Applications ◽  
Linearly Constrained ◽  
Multipath Signals

Direction of arrival (DOA) estimation in diffuse multipath environments is a challenge for ground-based radar remote sensing applications, which has significant value in military fields, such as air defense surveillance. However, radar received echo usually contains various multipath signals caused by the reflection of complex ground or sea surface. With the introduction of multipath signals, traditional algorithms’ performance on angle estimation decreases severely. In response to this problem, the letter proposes a new time reversal (TR) algorithm used for multiple-input multiple-output (MIMO) radar angle estimation. First, the algorithm reconstructs a TR covariance matrix by multiplexing the data’s rows and columns, increasing the estimation accuracy of the TR covariance matrix. Besides, the letter applies a linearly constrained minimum power (LCMP) constraint to suppress diffuse multipath signals according to the prior knowledge of environments. Simulation results examine the improvement of estimation accuracy by the proposed algorithm, also verify the superiority of the proposed algorithm in different multipath scenarios. What’s more, the algorithm has broader applicability due to avoiding the difficulties of removing the coherence and estimating multipath number in practice.

scholarly journals Tensor-Based Angle Estimation Approach for Strictly Noncircular Sources with Unknown Mutual Coupling in Bistatic MIMO Radar

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2788 ◽  
Author(s):  
Yuehao Guo ◽  
Xianpeng Wang ◽  
Wensi Wang ◽  
Mengxing Huang ◽  
Chong Shen ◽  
...  
Keyword(s):  
Mutual Coupling ◽  
Multiple Input Multiple Output ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Angle Estimation ◽  
Estimation Performance ◽  
Direction Of Departure ◽  
Value Decomposition

In the paper, the estimation of joint direction-of-departure (DOD) and direction-of-arrival (DOA) for strictly noncircular targets in multiple-input multiple-output (MIMO) radar with unknown mutual coupling is considered, and a tensor-based angle estimation method is proposed. In the proposed method, making use of the banded symmetric Toeplitz structure of the mutual coupling matrix, the influence of the unknown mutual coupling is removed in the tensor domain. Then, a special enhancement tensor is formulated to capture both the noncircularity and inherent multidimensional structure of strictly noncircular signals. After that, the higher-order singular value decomposition (HOSVD) technology is applied for estimating the tensor-based signal subspace. Finally, the direction-of-departure (DOD) and direction-of-arrival (DOA) estimation is obtained by utilizing the rotational invariance technique. Due to the use of both noncircularity and multidimensional structure of the detected signal, the algorithm in this paper has better angle estimation performance than other subspace-based algorithms. The experiment results verify that the method proposed has better angle estimation performance.

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 Computationally Efficient Unitary ESPRIT Algorithm in Bistatic MIMO Radar

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Baobao Liu ◽  
Tao Xue ◽  
Cong Xu ◽  
Yongjun Liu
Keyword(s):  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Mimo Radar ◽  
Computational Effort ◽  
Estimation Accuracy ◽  
Computationally Efficient ◽  
Angle Estimation ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Esprit Algorithm

A low complexity unitary estimating signal parameter via rotational invariance techniques (ESPRIT) algorithm is presented for angle estimation in bistatic multiple-input-multiple-output (MIMO) radar. The devised algorithm only requires calculating two submatrices covariance matrix, which reduces the computation cost in comparison with subspace methods. Moreover, the signal subspace can be efficiently acquired by exploiting the NystrÖm method, which only needs O M N K 2 flops. Thus, the presented algorithm has an essentially diminished computational effort, especially useful when K ≪ M N , while it can achieve efficient angle estimation accuracy as well as the existing algorithms. Several theoretical analysis and simulation results are provided to demonstrate the usefulness of the proposed scheme.

Multi-SVD based subspace estimation to improve angle estimation accuracy in bistatic MIMO radar

2013 ◽  
Vol 93 (7) ◽  
pp. 2003-2009 ◽  
Author(s):  
Yuanbing Cheng ◽  
Rusheng Yu ◽  
Hong Gu ◽  
Weimin Su
Keyword(s):  
Mimo Radar ◽  
Estimation Accuracy ◽  
Angle Estimation ◽  
Bistatic Mimo Radar ◽  
Subspace Estimation

scholarly journals DOA Estimation of Noncircular Signals Using Quaternions

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Tianzhen Meng ◽  
Minjie Wu ◽  
Naichang Yuan
Keyword(s):  
Computational Complexity ◽  
Covariance Matrix ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Estimation Problem ◽  
Estimation Accuracy ◽  
Two Dimensional ◽  
Output Vector ◽  
Noncircular Signals ◽  
Simulation Results

The two-dimensional (2D) direction-of-arrival (DOA) estimation problem for noncircular signals using quaternions is considered in this paper. In the framework of quaternions, we reconstruct the conjugate augmented output vector which reduces the dimension of covariance matrix. Compared with existing methods, the proposed one has two main advantages. Firstly, the estimation accuracy is higher since quaternions have stronger orthogonality. Secondly, the dimension of covariance matrix is reduced by half which decreases the computational complexity. Simulation results are presented verifying the efficacy of the algorithm.

Target range–angle estimation based on time reversal FDA-MIMO radar

2019 ◽  
Vol 12 (4) ◽  
pp. 267-275
Author(s):  
Tong Mu ◽  
Yaoliang Song
Keyword(s):  
Time Reversal ◽  
Mimo Radar ◽  
Target Range ◽  
Reversal Theory ◽  
Signal Model ◽  
Angle Estimation ◽  
Multiple Signal Classification ◽  
Multiple Input ◽  
Target Detecting ◽  
Joint Range

AbstractDifferent from traditional multiple-input and multiple-output (MIMO) radar, the frequency diverse array MIMO (FDA-MIMO) radar generates beampattern that is dependent on both range and angle, making it applicable for joint range–angle estimation of targets. In this paper, we propose a novel time reversal based FDA-MIMO (TR-FDA-MIMO) approach for target detection. Based on the time reversal theory, the TR-FDA-MIMO signal model is established, the TR transmitting–receiving and signal processing procedure are analyzed, and the resulting range–angle spectra for targets imaging are acquired by utilizing the multiple signal classification algorithm. Numerical simulations are carried out for both single and multiple targets cases. The imaging resolution and robustness to the noise of the proposed approach are investigated and results are compared with conventional FDA-MIMO radar. It turned out that by cooperating with TR, the performance of FDA-MIMO radar for target range–angle estimation is effectively enhanced, consequently improving its applicability in practical target-detecting cases.

scholarly journals Multipath Exploitation with Time Reversal Waveform Covariance Matrix for SNR Maximization

2020 ◽  
Vol 12 (21) ◽  
pp. 3565
Author(s):  
Chao Xiong ◽  
Chongyi Fan ◽  
Xiaotao Huang
Keyword(s):  
Semidefinite Programming ◽  
Covariance Matrix ◽  
Target Detection ◽  
Time Reversal ◽  
Design Method ◽  
Signal To Noise Ratio ◽  
Gaussian White Noise ◽  
Information Matrix ◽  
Wide Range ◽  
Multipath Signals

Radar target detection has a wide range of applications in the military and civilian remote sensing fields; in particular, the target detection in multipath environments has attracted many scholars’ attention in recent years. The abundant multipath signals severely interfere with the detection performance and accuracy of parameter estimation of traditional algorithms. Under Gaussian white noise environments, this letter proposes an adaptive time reversal (TR) waveform covariance matrix (WCM) design method with multipath exploitation to improve the maximum signal-to-noise ratio (SNR) at the receiver in multipath environments. This equivalently improves the detection probability. The proposed two-stage algorithm firstly adapts the time-reversal echo to construct a multipath information matrix with a Hermitian structure. Secondly, the letter transforms the maximized SNR problem into semidefinite programming (SDP), which is constrained by a constant total transmit power. Consequently, the waveform covariance matrix is obtained by solving semidefinite programming. Simulation experiments verify the adaptability and effectiveness of the proposed algorithm.

scholarly journals An Angle Estimation Method for Monostatic MIMO Radar Based on RCC-FLOM Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Jian Gong ◽  
Huan Wang ◽  
Yiduo Guo
Keyword(s):  
Invariant Subspace ◽  
Covariance Matrix ◽  
Lower Order ◽  
Impulse Noise ◽  
Mimo Radar ◽  
Rotation Invariant ◽  
Angle Estimation ◽  
Noise Background ◽  
Impact Noise ◽  
The Impact

The performance of the angle estimation algorithm based on the two-order or higher order cumulants in the impact noise background will decline sharply. Therefore, it is necessary to study the new algorithm to estimate target angle in the impact noise background. In order to solve the angle estimation problem of coherent sources in the impulse noise background, a conjugate rotation invariant subspace algorithm based on reduced order fractional lower order covariance matrix is proposed. Use the reduced dimension lower order fraction covariance matrix to reduce the impulse noise influence. And according to the conjugate rotation invariant subspace, the coherent source is decohered. The Monte-Carlo experiments show that the proposed algorithm has the advantages of high estimation probability and low root mean square error in the case of low signal-to-noise ratio, compared with the existing FLOM-MUSIC algorithm and FLOM-Unitary ESPRIT algorithm.

Sign in / Sign up

Export Citation Format

Share Document