scholarly journals Direction of Arrival Estimation Accuracy Enhancement via Using Displacement Invariance Technique

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Youssef Fayad ◽  
Caiyun Wang ◽  
Qunsheng Cao ◽  
Alaa El-Din Sayed Hafez
Keyword(s):  
Performance Enhancement ◽  
Doppler Frequency ◽  
Direction Of Arrival ◽  
Estimation Accuracy ◽  
Direction Of Arrival Estimation ◽  
Target Movement ◽  
Nonlinearity Effect ◽  
Esprit Algorithm ◽  
Cramer Rao Bound ◽  
Better Than

A new algorithm for improving Direction of Arrival Estimation (DOAE) accuracy has been carried out. Two contributions are introduced. First, Doppler frequency shift that resulted from the target movement is estimated using the displacement invariance technique (DIT). Second, the effect of Doppler frequency is modeled and incorporated into ESPRIT algorithm in order to increase the estimation accuracy. It is worth mentioning that the subspace approach has been employed into ESPRIT and DIT methods to reduce the computational complexity and the model’s nonlinearity effect. The DOAE accuracy has been verified by closed-form Cramér-Rao bound (CRB). The simulation results of the proposed algorithm are better than those of the previous estimation techniques leading to the estimator performance enhancement.

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 Cramér-Rao Bound of Direction Finding Using Uniform Arc Arrays

2019 ◽  
pp. 1-15
Author(s):  
Veronicah Nyokabi ◽  
Dominic Makaa Kitavi ◽  
Cyrus Gitonga Ngari
Keyword(s):  
Direction Of Arrival ◽  
Direction Finding ◽  
Azimuth Angle ◽  
Estimation Accuracy ◽  
Direction Of Arrival Estimation ◽  
Circular Array ◽  
Cramer Rao Bound ◽  
Array Geometry

Direction-of-Arrival estimation accuracy using arc array geometry is considered in this paper.There is a scanty use of Uniform Arc Array (UAA) in conjunction with Cramer-Rao bound (CRB)for Direction-of-Arrival estimation. This paper proposed to use Uniform Arc Array formed from a considered Uniform Circular Array (UCA) in conjunction with CRB for Direction-of-Arrival estimation. This Uniform Arc Array is obtained by squeezing all sensors on the Uniform Circular Array circumference uniformly onto the Arc Array. Cramer-Rao bounds for the Uniform Arc Array and that of the Uniform Circular Array are derived. Comparison of performance of the Uniform Circular Array and Uniform Arc Array is done. It was observed that Uniform Arc Array has better estimation accuracy as compared to Uniform Circular Array when number of sensors equals four and ve and azimuth angle ranging between $$\frac{\pi}{9}~ and ~\frac{7}{18}\pi~ and~ also ~\frac{10}{9}\pi ~and ~\frac{25}{18}\pi$$. However, UCA and UAA have equal performance when the number of sensors equals three and the azimuth angle ranging between 0 and 2π. UCA has better estimation accuracy as compared to UAA when the number of sensors equals four and ve and the azimuth angle ranging between  $$\frac{\pi}{2} ~and~ \pi ~and ~also~ \frac{3}{2}\pi ~and~ 2\pi$$

scholarly journals Effect of varying Number of Spacing between Antenna Elements, Snapshots, SNR on AP ML, AP-SSF and ESPRIT Algorithm

2021 ◽  
Vol 11 ◽  
pp. 143-150
Author(s):  
Vinod Kumar ◽  
Sanjeev Kumar Dhull
Keyword(s):  
Signal Processing ◽  
Maximum Likelihood ◽  
Key Words ◽  
Array Signal Processing ◽  
Direction Of Arrival ◽  
Performance Comparison ◽  
Direction Of Arrival Estimation ◽  
Multiple Sources ◽  
Alternating Projection ◽  
Esprit Algorithm

The direction of arrival estimation is the main key problem in array signal processing. In this paper, the alternating projection maximum Likelihood (AP-ML), Alternating projection sub space framework (APSSF) and ESPRIT algorithm are studied. The simulation is performed in MATLAB for single and multiple sources. The effect of the varying number of spacing between antenna elements, number of snapshots and SNR are studied. The performance comparison shows that ESPRIT algorithm performs better as compared to the AP-ML and AP-SSF. Key-Words: - AP-ML, AP-SSF, Direction of Arrival, ESPRIT, Snapshots, SNR

scholarly journals A Novel Unitary ESPRIT Algorithm for Monostatic FDA-MIMO Radar

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 827 ◽  
Author(s):  
Feilong Liu ◽  
Xianpeng Wang ◽  
Mengxing Huang ◽  
Liangtian Wan ◽  
Huafei Wang ◽  
...  
Keyword(s):  
Computational Complexity ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Estimation Accuracy ◽  
Phase Ambiguity ◽  
Steering Vector ◽  
Input Multiple Output ◽  
Joint Direction ◽  
Esprit Algorithm ◽  
Cramer Rao Bound

A novel unitary estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm, for the joint direction of arrival (DOA) and range estimation in a monostatic multiple-input multiple-output (MIMO) radar with a frequency diverse array (FDA), is proposed. Firstly, by utilizing the property of Centro-Hermitian of the received data, the extended real-valued data is constructed to improve estimation accuracy and reduce computational complexity via unitary transformation. Then, to avoid the coupling between the angle and range in the transmitting array steering vector, the DOA is estimated by using the rotation invariance of the receiving subarrays. Thereafter, an automatic pairing method is applied to estimate the range of the target. Since phase ambiguity is caused by the phase periodicity of the transmitting array steering vector, a removal method of phase ambiguity is proposed. Finally, the expression of Cramér–Rao Bound (CRB) is derived and the computational complexity of the proposed algorithm is compared with the ESPRIT algorithm. The effectiveness of the proposed algorithm is verified by simulation results.

scholarly journals AR Model-Based Direction-of-Arrival Estimation of Coherent Signals in the Presence of Unknown Mutual Coupling

2013 ◽  
Vol 2013 ◽  
pp. 1-6
Author(s):  
Zhi-Chao Sha ◽  
Zhang-Meng Liu ◽  
Zhi-Tao Huang ◽  
Yi-Yu Zhou
Keyword(s):  
Mutual Coupling ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Ar Model ◽  
Estimation Accuracy ◽  
Direction Of Arrival Estimation ◽  
Coherent Signals ◽  
Model Based ◽  
Mixing Matrix ◽  
Simulation Results

This paper addresses the problem of direction-of-arrival (DOA) estimation of coherent signals in the presence of unknown mutual coupling, and an autoregression (AR) model-based method is proposed. The effects of mutual coupling can be eliminated by the inherent mechanism of the proposed algorithm, so the DOAs can be accurately estimated without any calibration sources. After the mixing matrix is estimated by independent component analysis (ICA), several parameter equations are established upon the mixing matrix. Finally, all DOAs of coherent signals are estimated by solving these equations. Compared with traditional methods, the proposed method has higher angle resolution and estimation accuracy. Simulation results demonstrate the effectiveness of the algorithm.

scholarly journals Application of Sparse Representation to Bartlett Spectra for Improved Direction of Arrival Estimation

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 77
Author(s):  
Jacob Compaleo ◽  
Inder J. Gupta
Keyword(s):  
Sparse Representation ◽  
Regularization Parameter ◽  
Angular Separation ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Direction Of Arrival Estimation ◽  
Small Signal ◽  
Music Algorithm ◽  
A New Technique ◽  
Better Than

A new technique for high-resolution direction of arrival estimation is presented. The method utilizes the traditional Bartlett spectra and sparse representation to locate emitters in single and multiple emitter scenarios. A method for selecting the sparse representation regularization parameter is also presented. Using Monte Carlo simulations, we show that the proposed approach achieves accurate direction of arrival (DOA) estimations that are unbiased and a variance that approaches the Cramer–Rao lower bound. We show that our method outperforms the popular MUSIC algorithm, and is slightly better than the sparse representation based L1-SVD algorithm when angular separation between emitters is small, signal SNR is low, and a small number of snapshots are used in DOA estimation.

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