DOA Estimation Using Electrically Small Matched Dipole Antennas and the Associated Cramer-Rao Bound

2007 ◽  
pp. 439-462
Keyword(s):  
Doa Estimation ◽  
Dipole Antennas ◽  
Cramer Rao Bound

scholarly journals Polarization and DOA Estimation Based on Dual-Polarized Conformal Array

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Chao Liu ◽  
Shuai Xiang ◽  
Liangfeng Xu ◽  
Zhengfei Fang
Keyword(s):  
Doa Estimation ◽  
Signal Classification ◽  
Orthogonal Polarization ◽  
Coherent Signals ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
Polarization Signal ◽  
Conformal Array ◽  
Dual Polarized ◽  
Cramer Rao Bound

A dual-polarized multiple signal classification (DP-MUSIC) algorithm is presented to estimate the arrival directions and polarizations for a dual-polarized conformal array. Each polarization signal is decomposed into two orthogonal polarization components, which are considered to be a pair of coherent signals coming from the same direction but different polarization. The polarization parameters are modeled as the equivalent coherence coefficients of the orthogonal polarization components. Then, the method of decoherence can be used to decouple the information of polarization states and signal angles. After that, the direction of arrival (DOA) and polarization parameters can be estimated by the DP-MUSIC algorithm. Moreover, the angles of incident direction are re-estimated, which greatly improves the accuracy of DOA estimation. The Cramer–Rao bound (CRB) is derived and the effectiveness of the proposed algorithm is verified by Monte Carlo simulations.

scholarly journals A MIMO Radar-Based DOA Estimation Structure Using Compressive Measurements

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4706 ◽  
Author(s):  
Tao Chen ◽  
Jian Yang ◽  
Muran Guo
Keyword(s):  
Antenna Arrays ◽  
Multiple Input Multiple Output ◽  
Information Criterion ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Optimization Approach ◽  
Measurement Matrix ◽  
Estimation Performance ◽  
Input Multiple Output ◽  
Cramer Rao Bound

In this paper, we propose a novel direction-of-arrival (DOA) estimation structure based on multiple-input multiple-output (MIMO) radar with colocated antennas, referred to as compressive measurement-based MIMO (CM-MIMO) radar, where the compressive sensing (CS) is employed to reduce the number of channels. Therefore, the system complexity and the computational burden are effectively reduced. It is noted that CS is used after the matched filters and that a measurement matrix with less rows than columns is multiplied with the received signals. As a result, the configurations of the transmit and receive antenna arrays are not affected by the CS and can be determined according to the practical requirements. To study the estimation performance, the Cramér–Rao bound (CRB) with respect to the DOAs of the proposed CM-MIMO radar is analyzed in this paper. The derived CRB expression is also suitable for the conventional MIMO radar by setting the measurement matrix as an identity matrix. Moreover, the CRB expression can work in the under-determined case, since the sum-difference coarray structure is considered. However, the random measurement matrix leads to high information loss, thus compromising the estimation performance. To overcome this problem, we consider that the a prior probability distribution of the DOAs associated with the targets can be obtained in many scenarios and an optimization approach for the measurement matrix is proposed in this paper, where the maximum mutual information criterion is adopted. The superiority of the proposed structure is validated by numerical simulations.

scholarly journals Sparsity-Based DOA Estimation with Gain and Phase Error Calibration of Generalized Nested Array

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Ziang Feng ◽  
Guoping Hu ◽  
Hao Zhou
Keyword(s):  
Degrees Of Freedom ◽  
Phase Error ◽  
Doa Estimation ◽  
Model Errors ◽  
Multiple Sources ◽  
Sparse Arrays ◽  
Estimation Algorithms ◽  
Phase Errors ◽  
Representation Method ◽  
Cramer Rao Bound

Sparse arrays, which can localize multiple sources with less physical sensors, have attracted more attention since they were proposed. However, for optimal performance of sparse arrays, it is usually assumed that the circumstances are ideal. But in practice, the performance of sparse arrays will suffer from the model errors like mutual coupling, gain and phase error, and sensor’s location error, which causes severe performance degradation or even failure of the direction of arrival (DOA) estimation algorithms. In this study, we follow with interest and propose a covariance-based sparse representation method in the presence of gain and phase errors, where a generalized nested array is employed. The proposed strategy not only enhances the degrees of freedom (DOFs) to deal with more sources but also obtains more accurate DOA estimations despite gain and phase errors. The Cramer–Rao bound (CRB) derivation is analyzed to demonstrate the robustness of the method. Finally, numerical examples illustrate the effectiveness of the proposed method from DOA estimation.

A Developed ESPRIT Algorithm for DOA Estimation

Frequenz ◽  
2015 ◽  
Vol 69 (5-6) ◽  
Author(s):  
Youssef Fayad ◽  
Caiyun Wang ◽  
Qunsheng Cao ◽  
Alaa El-Din Sayed Hafez
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Performance Enhancement ◽  
Doa Estimation ◽  
Signal Parameter ◽  
Nonlinearity Effect ◽  
Esprit Algorithm ◽  
Cramer Rao Bound ◽  
Novel Algorithm ◽  
Better Than

AbstractA novel algorithm for estimating direction of arrival (DOAE) for target, which aspires to contribute to increase the estimation process accuracy and decrease the calculation costs, has been carried out. It has introduced time and space multiresolution in Estimation of Signal Parameter via Rotation Invariance Techniques (ESPRIT) method (TS-ESPRIT) to realize subspace approach that decreases errors caused by the model’s nonlinearity effect. The efficacy of the proposed algorithm is verified by using Monte Carlo simulation, the DOAE accuracy has evaluated by closed-form Cramér–Rao bound (CRB) which reveals that the proposed algorithm’s estimated results are better than those of the normal ESPRIT methods leading to the estimator performance enhancement.

scholarly journals DOA Estimation for Sources with Large Power Differences

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Qingyuan Fang ◽  
Mengzhe Jin ◽  
Weidong Liu ◽  
Yong Han
Keyword(s):  
Computational Complexity ◽  
Doa Estimation ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Invariant Property ◽  
Large Power ◽  
Diagonal Loading ◽  
Weak Source ◽  
Strong Source ◽  
Cramer Rao Bound

Sources with large power differences are very common, especially in complex electromagnetic environments. Classical DOA estimation methods suffer from performance degradation in terms of resolution when dealing with sources that have large power differences. In this paper, we propose an improved DOA algorithm to increase the resolution performance in resolving such sources. The proposed method takes advantage of diagonal loading and demonstrates that the invariant property of noise subspace still holds after diagonal loading is performed. We also find that the Cramer–Rao bound of the weak source can be affected by the power of the strong source, and this has not been noted before. The Cramer–Rao bound of the weak source deteriorates as the power of the strong source increases. Numerical results indicate that the improved algorithm increases the probability of resolution while maintaining the estimation accuracy and computational complexity.

scholarly journals The Upper Bound of Multi-source DOA Information in Sensor Array and Its Application in Performance Evaluation

2020 ◽  
Author(s):  
Weilin Tu ◽  
Dazhuan Xu ◽  
Ying Zhou ◽  
Chao Shi
Keyword(s):  
Performance Evaluation ◽  
Upper Bound ◽  
Array Signal Processing ◽  
Additive White Gaussian Noise ◽  
Doa Estimation ◽  
Theoretical Expression ◽  
Multiple Sources ◽  
Entropy Error ◽  
Performance Evaluation Index ◽  
Cramer Rao Bound

Abstract Direction of arrival (DOA) estimation has been discussed extensively in the array signal processing field. In this paper, we focus on the DOA information which is defined as the mutual information between the DOA and the received signal with complex additive white Gaussian noise. A theoretical expression of DOA information with multiple sources is presented in the uniform linear array. Specially, the upper bound of DOA information for sparse sources with high SNR is derived and compared with the information of single source. Moreover, the relationship between Cramer-Rao bound and the upper bound of DOA information is given. Finally, the paper investigate the performance evaluation of estimation based on the DOA information. We define the entropy error (EE) as a new performance evaluation index and find that EE is better than mean square error. Moreover, the lower bound of the EE can be regarded as the Generalized Cramer-Rao bound considering the sources' order in multi-source scenario.

scholarly journals DOA Estimation for Highly Correlated and Coherent Multipath Signals with Ultralow SNRs

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Li Cheng ◽  
Yang Li ◽  
Lianying Zou ◽  
Yong Qin
Keyword(s):  
Multipath Propagation ◽  
Signal To Noise Ratio ◽  
Main Idea ◽  
Doa Estimation ◽  
Time Shift ◽  
Direct Path ◽  
Multipath Signal ◽  
Highly Correlated ◽  
Multipath Signals ◽  
Cramer Rao Bound

In a typical multipath propagation environment, there exists a strong direct path signal accompanying with several weak multipath signals. Due to the strong direct path interference and other masking effects, the Direction-of-Arrival (DOA) of a weak multipath signal is hard to be estimated. In this paper, a novel method is proposed to estimate the DOA of multipath signals with ultralow signal-to-noise ratio (SNR). The main idea is to increase the SNR and signal-to-interference ratio (SIR) of the desired multipath signal in time-delay domain before DOA estimation processing. Firstly, the cross-correlation functions of the direct path signal and the received array signal are calculated. Then, they are combined and constructed to an enhanced array signal. Under certain conditions, the SNR and SIR of the desired signal can be significantly increased. Finally, the DOAs of multipath signals can be estimated by conventional technologies, and the associated time delays can be measured on the DOA-time-shift map. The SNR and SIR gains of the desired signal are analyzed theoretically, and theoretical analysis also indicates that the Cramer–Rao bound can be reduced. Simulation examples are presented to verify the advantages of the proposed method.

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