Experimental Study on Two-Dimensional Position Estimation Method by Stepped-FM MIMO Radar using 2D-MUSIC Method

2018 ◽  
Vol 138 (2) ◽  
pp. 112-117
Author(s):  
Kenshi Ogawa ◽  
Akihiro Kajiwara
Keyword(s):  
Experimental Study ◽  
Estimation Method ◽  
Mimo Radar ◽  
Position Estimation ◽  
Two Dimensional

scholarly journals Two-Dimensional DOA Estimation for Monostatic MIMO Radar with Electromagnetic Vector Received Sensors

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Guimei Zheng ◽  
Jun Tang
Keyword(s):  
Electromagnetic Field ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Field Vector ◽  
Cross Product ◽  
Mimo Radar ◽  
Two Dimensional ◽  
Array Configuration ◽  
2D Doa Estimation

We study two-dimensional direction of arrival (2D-DOA) estimation problem of monostatic MIMO radar with the receiving array which consists of electromagnetic vector sensors (EMVSs). The proposed angle estimation algorithm can be applied to the arbitrary and unknown array configuration, which can be summarized as follows. Firstly, EMVSs in the receiver of a monostatic MIMO radar are used to measure all six electromagnetic-field components of an incident wavefield. The vector sensor array with the six unknown electromagnetic-field components is divided into six spatially identical subarrays. Secondly, ESPRIT is utilized to estimate the rotational invariant factors (RIFs). Parts of the RIFs are picked up to restore the source’s electromagnetic-field vector. Last, a vector cross product operation is performed between electric field and magnetic field to obtain the Pointing vector, which can offer the 2D-DOA estimation. Prior knowledge of array elements’ positions and angle searching procedure are not necessary for the proposed 2D-DOA estimation method. Simulation results prove the validity of the proposed method.

scholarly journals 2-D DOA Estimation Method Based on Coprime Array MIMO Radar

2021 ◽  
Author(s):  
Fei Zhang ◽  
Zijing Zhang ◽  
Aisuo Jin ◽  
Chuantang Ji ◽  
Yi Wang
Keyword(s):  
Estimation Method ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Degree Of Freedom ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Two Dimensional ◽  
Multiple Sources ◽  
Sparse Array ◽  
Coprime Array

Abstract Aiming at the problem that traditional Direction of Arrival (DOA) estimation methods cannot handle multiple sources with high accuracy while increasing the degree of freedom, a new method of 2-D DOA estimation based on coprime array MIMO Radar (SA-MIMO-CA). Frist of all, in order to ensure the accuracy of multi-source estimation when the number of elements is finite, a new coprime array model based on MIMO (MIMO-CA) is proposed. The array model uses a special irregular array as the transmitting array and a uniform linear array as the receiving array. Besides, in order to reduce complexity and improve the accuracy of two-dimensional DOA estimation, a new two-dimensional DOA estimation method based on sparse array is proposed. This method uses the sparse array topology of virtual array elements to analyze a larger number of information sources, and combines the compressed sensing method to process the sparse array, and obtains a larger array aperture with a smaller number of array elements, and improves the resolution of the azimuth angle. This method improves the DOA estimation accuracy and reduces the complexity. Finally, experiments verify the effectiveness and reliability of the SA-MIMO-CA method in improving the degree of freedom of the array, reducing the complexity, and improving the accuracy of the DOA.

scholarly journals Calibration Method of Array Errors for Wideband MIMO Imaging Radar Based on Multiple Prominent Targets

2021 ◽  
Vol 13 (15) ◽  
pp. 2997
Author(s):  
Zheng Zhao ◽  
Weiming Tian ◽  
Yunkai Deng ◽  
Cheng Hu ◽  
Tao Zeng
Keyword(s):  
Multiple Input Multiple Output ◽  
Estimation Method ◽  
Target Position ◽  
Position Estimation ◽  
Least Square ◽  
Antenna Structure ◽  
Imaging Radar ◽  
Position Errors ◽  
Calibration Methods ◽  
Array Errors

Wideband multiple-input-multiple-output (MIMO) imaging radar can achieve high-resolution imaging with a specific multi-antenna structure. However, its imaging performance is severely affected by the array errors, including the inter-channel errors and the position errors of all the transmitting and receiving elements (TEs/REs). Conventional calibration methods are suitable for the narrow-band signal model, and cannot separate the element position errors from the array errors. This paper proposes a method for estimating and compensating the array errors of wideband MIMO imaging radar based on multiple prominent targets. Firstly, a high-precision target position estimation method is proposed to acquire the prominent targets’ positions without other equipment. Secondly, the inter-channel amplitude and delay errors are estimated by solving an equation-constrained least square problem. After this, the element position errors are estimated with the genetic algorithm to eliminate the spatial-variant error phase. Finally, the feasibility and correctness of this method are validated with both simulated and experimental datasets.

scholarly journals A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter Criteria

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 700
Author(s):  
Belén Pérez-Sánchez ◽  
Martín González ◽  
Carmen Perea ◽  
Jose J. López-Espín
Keyword(s):  
Experimental Study ◽  
Computational Study ◽  
Estimation Method ◽  
Information Criteria ◽  
Simultaneous Equations ◽  
Estimation Methods ◽  
Entropy Measure ◽  
Economic Science ◽  
Akaike Information Criteria ◽  
Simultaneous Equations Models

Simultaneous Equations Models (SEM) is a statistical technique widely used in economic science to model the simultaneity relationship between variables. In the past years, this technique has also been used in other fields such as psychology or medicine. Thus, the development of new estimating methods is an important line of research. In fact, if we want to apply the SEM to medical problems with the main goal being to obtain the best approximation between the parameters of model and their estimations. This paper shows a computational study between different methods for estimating simultaneous equations models as well as a new method which allows the estimation of those parameters based on the optimization of the Bayesian Method of Moments and minimizing the Akaike Information Criteria. In addition, an entropy measure has been calculated as a parameter criteria to compare the estimation methods studied. The comparison between those methods is performed through an experimental study using randomly generated models. The experimental study compares the estimations obtained by the different methods as well as the efficiency when comparing solutions by Akaike Information Criteria and Entropy Measure. The study shows that the proposed estimation method offered better approximations and the entropy measured results more efficiently than the rest.

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