A SIMPLE POWER – BASED DIRECTION OF ARRIVAL SENSOR

2021 ◽  
pp. 48-54
Author(s):  
Minh Thuy Le
Keyword(s):  
Simple Formula ◽  
Direction Of Arrival ◽  
Direction Finding ◽  
Power Dividers ◽  
Simple Power ◽  
Sensor Structure

In this paper, a simple power - based Direction of Arrival (DoA) sensor is investigated. This sensor is applied in direction finding systems working at 2.6 GHz. The proposed sensor can determine the DoA via a simple formula compared to the algorithms – based methods. The sensor structure consists of simple components: two receiving antennas and a power dividers which have a good isolation between output ports based on  hybrid couplers (HCs). With the range of simulation DoA from 0o to 35o, the proposed sensor yields the error of less than 5o.

scholarly journals Direction of Arrival Based on the Multioutput Least Squares Support Vector Regression Model

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Kai Huang ◽  
Ming-Yi You ◽  
Yun-Xia Ye ◽  
Bin Jiang ◽  
An-Nan Lu
Keyword(s):  
Least Squares ◽  
Support Vector Regression ◽  
Direction Of Arrival ◽  
Direction Finding ◽  
Training Data ◽  
Data Driven ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Model Training ◽  
Finding Method

The interferometer is a widely used direction-finding system with high precision. When there are comprehensive disturbances in the direction-finding system, some scholars have proposed corresponding correction algorithms, but most of them require hypothesis based on the geometric position of the array. The method of using machine learning that has attracted much attention recently is data driven, which can be independent of these assumptions. We propose a direction-finding method for the interferometer by using multioutput least squares support vector regression (MLSSVR) model. The application of this method includes the following: the construction of MLSSVR model training data, training and construction of the MLSSVR model, and the estimation of direction of arrival. Finally, the method is verified through numerical simulation. When there are comprehensive deviations in the system, the direction-finding accuracy can be effectively improved.

scholarly journals Cramér-Rao Bound of Direction Finding Using a Uniform Hexagonal Array

2019 ◽  
pp. 1-14
Author(s):  
Grace Wakarima Ndiritu ◽  
Dominic Makaa Kitavi ◽  
Cyrus Gitonga Ngari
Keyword(s):  
Sensor Array ◽  
Graphical Representation ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Direction Finding ◽  
Direction Of Arrival Estimation ◽  
Circular Array ◽  
Hexagonal Array ◽  
Sensor Array Processing ◽  
Cramer Rao Bound

Direction-of-arrival (DOA) estimation is a key area of sensor array processing which is encountered  in many important engineering applications. Although various studies have focused on the uniform hexagonal array for direction finding, there is a scanty use of the uniform hexagonal array in conjunction with Cramer-Rao bound for direction finding estimation. The advantage of Cramér- Rao bound based on the uniform hexagonal array: overcome the problem of unwanted radiation in undesired directions. In this paper, the direction-of-arrival estimation of Cramér-Rao bound based on the uniform hexagonal array was studied. The proposed approach concentrated on deriving the array manifold vector for the uniform hexagonal array and Cramer-Rao bound of the uniform hexagonal array. The Cramér-Rao bound based on the uniform hexagonal array was compared with Cramer-Rao bound based on the uniform circular array. The conclusions are as follows. The Cramer-Rao bound of uniform hexagonal array decreases with an increase in the number of sensors. The comparison between the uniform hexagonal array and uniform circular array shows that the Cramér-Rao bound of the uniform hexagonal array was slightly higher as compared to the Cramér-Rao bound of the uniform circular array. The analytical results are supported by graphical representation.

scholarly journals A Switched-Element System Based Direction of Arrival (DOA) Estimation Method for Un-Cooperative Wideband Orthogonal Frequency Division Multi Linear Frequency Modulation (OFDM-LFM) Radar Signals

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 132 ◽  
Author(s):  
Yifei Liu ◽  
Yuan Zhao ◽  
Jun Zhu ◽  
Jun Wang ◽  
Bin Tang
Keyword(s):  
Frequency Modulation ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Direction Finding ◽  
Linear Frequency Modulation ◽  
Frequency Division ◽  
Linear Frequency ◽  
Orthogonal Frequency Division Multi ◽  
Radar Signals

This paper proposes a switched-element direction finding (SEDF) system based Direction of Arrival (DOA) estimation method for un-cooperative wideband Orthogonal Frequency Division Multi Linear Frequency Modulation (OFDM-LFM) radar signals. This method is designed to improve the problem that most DOA algorithms occupy numbers of channel and computational resources to handle the direction finding for wideband signals. Then, an iterative spatial parameter estimator is designed through deriving the analytical steering vector of the intercepted OFDM-LFM signal by the SEDF system, which can remarkably mitigate the dispersion effect that is caused by high chirp rate. Finally, the algorithm flow and numerical simulations are given to corroborate the feasibility and validity of our proposed DOA method.

scholarly journals Noise Suppression for Direction of Arrival Estimation in Co-located MIMO Sonar

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1325 ◽  
Author(s):  
Xue Cheng ◽  
Yingmin Wang
Keyword(s):  
Dimension Reduction ◽  
Covariance Matrix ◽  
Gaussian Noise ◽  
Noise Suppression ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Direction Finding ◽  
White Gaussian Noise ◽  
Multiple Targets ◽  
Noise Component

Noise suppression capacity in multiple-input multiple-output (MIMO) sonar signal processing is derived under the assumption of white Gaussian noise. However, underwater noise mainly includes white Gaussian noise and colored noise. There exists a certain correlation between the noise signals received by each MIMO sonar array element. The performance of traditional direction-of-arrival (DOA) estimation methods decreases obviously in complex marine noise. In this paper, we propose a marine environment noise suppression method for MIMO applied to multiple targets’ DOA estimation. The noise field can be decomposed into a symmetric noise component and an asymmetric noise component. We use the covariance matrix imaginary component to pre-estimate the signal sources, then use the dimension reduction transformation to reconstruct the real component of the covariance matrix. The Toeplitz technique is utilized to reduce the correlation of the reconstructed covariance matrix. Thus, the subspace decomposition-based techniques such as multiple signal classification (MUSIC) can be used for multiple targets’ DOA estimation. To reduce the computational complexity of the methods, search-free direction-finding techniques such as the estimation of signal parameters via rotational invariance techniques (ESPRIT) can be utilized. As a result, the proposed methods can achieve better direction-finding performance in the condition of limited snapshots with lower computational cost. The corresponding Cramer-Rao bound (CRB) is deduced and the signal-to-noise ratio (SNR) gain obtained by dimension reduction processing is discussed. Simulation results also show the superiority of the proposed method over the existing methods.

scholarly journals Mutual Coupling Compensation for Direction-of-Arrival Estimations Using the Receiving-Mutual-Impedance Method

2010 ◽  
Vol 2010 ◽  
pp. 1-7 ◽  
Author(s):  
Hoi Shun Lui ◽  
Hon Tat Hui
Keyword(s):  
Mutual Coupling ◽  
Linear Array ◽  
Direction Of Arrival ◽  
Short Review ◽  
Direction Finding ◽  
Impedance Method ◽  
Mutual Impedance ◽  
Determination Methods

A short review of the receiving-mutual-impedance method (RMIM) for mutual coupling compensation in direction finding applications using linear array is conducted. The differences between the conventional-mutual-impedance method (CMIM) and RMIM, as well as the three different determination methods for receiving mutual impedance (RMI), will be discussed in details. As an example, direction finding with better accuracies is used for demonstrating the superiority of mutual coupling compensation using RMIM.

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  $$\frac{\pi}{2} ~and~ \pi ~and ~also~ \frac{3}{2}\pi ~and~ 2\pi$$

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