Spatial Filtering Based on Differential Spectrum for Improving ML DOA Estimation Performance

2016 ◽  
Vol 23 (12) ◽  
pp. 1811-1815 ◽  
Author(s):  
Rodrigo Pinto Lemos ◽  
Hugo Vinicius Leao e Silva ◽  
Edna Lucia Flores ◽  
Jonas Augusto Kunzler ◽  
Diego Fernando Burgos
Keyword(s):  
Spatial Filtering ◽  
Doa Estimation ◽  
Differential Spectrum ◽  
Estimation Performance

scholarly journals Efficient Two-Dimensional Direction Finding Algorithm for Rectilinear Sources Under Unknown Mutual Coupling

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1914
Author(s):  
Jian Xie ◽  
Qiuping Wang ◽  
Yuexian Wang ◽  
Xin Yang
Keyword(s):  
Degrees Of Freedom ◽  
Mutual Coupling ◽  
Array Signal Processing ◽  
Elevation Angle ◽  
Doa Estimation ◽  
Two Dimensional ◽  
Rank Reduction ◽  
Communication Signals ◽  
Estimation Performance ◽  
Direction Cosines

Digital communication signals in wireless systems may possess noncircularity, which can be used to enhance the degrees of freedom for direction-of-arrival (DOA) estimation in sensor array signal processing. On the other hand, the electromagnetic characteristics between sensors in uniform rectangular arrays (URAs), such as mutual coupling, may significantly deteriorate the estimation performance. To deal with this problem, a robust real-valued estimator for rectilinear sources was developed to alleviate unknown mutual coupling in URAs. An augmented covariance matrix was built up by extracting the real and imaginary parts of observations containing the circularity and noncircularity of signals. Then, the actual steering vector considering mutual coupling was reparameterized to make the rank reduction (RARE) property available. To reduce the computational complexity of two-dimensional (2D) spectral search, we individually estimated y-axis and x-axis direction-cosines in two stages following the principle of RARE. Finally, azimuth and elevation angle estimates were determined from the corresponding direction-cosines respectively. Compared with existing solutions, the proposed method is more computationally efficient, involving real-valued operations and decoupled 2D spectral searches into twice those of one-dimensional searches. Simulation results verified that the proposed method provides satisfactory estimation performance that is robust to unknown mutual coupling and close to the counterparts based on 2D spectral searches, but at the cost of much fewer calculations.

scholarly journals A Robust Spatial Filtering Method for DOA Estimation of Coherent Sources in Impulsive Interference

1999 ◽  
pp. 46-56
Author(s):  
Marilli Rupi ◽  
Panagiotis Tsakalides ◽  
Enrico Del Re ◽  
Chrysostomos L. Nikias
Keyword(s):  
Spatial Filtering ◽  
Doa Estimation ◽  
Filtering Method ◽  
Impulsive Interference ◽  
Coherent Sources

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 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 Atomic Norm-Based DOA Estimation with Dual-Polarized Radar

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1056 ◽  
Author(s):  
Min Han ◽  
Wenbin Dou
Keyword(s):  
Doa Estimation ◽  
Spatial Domain ◽  
Estimation Problem ◽  
Semidefinite Program ◽  
Sparse Reconstruction ◽  
Reconstruction Problem ◽  
Estimation Performance ◽  
Atomic Norm ◽  
Dual Polarized ◽  
Definition Of

In the dual-polarized radar system, the horizontally and vertically polarized signals can be exploited to improve the direction of arrival (DOA) estimation performance. In this paper, the DOA estimation problem is considered in the dual-polarized radar. By exploiting the target sparsity in the spatial domain, the sparse-based method is proposed after formulating the DOA estimation problem as a sparse reconstruction problem. In the traditionally sparse methods using the compressed sensing (CS) theory, the spatial domain is discretized into grids to establish a dictionary matrix and solve the sparse reconstruction problem, but the off-grid error is introduced in the discretized grids. Therefore, we formulate a novel definition of atomic norm for the dual-polarized signals and give an atomic norm-based method to denoise the received signals. Then, an efficient semidefinite program (SDP) is derived, and the DOA is estimated by searching the peak values of the denoised signals. Simulation results show that the proposed method can significantly improve the DOA estimation performance in the dual-polarized radar. Additionally, compared with the state-of-art methods, the proposed method has better estimation performance with relatively low computational complexity.

scholarly journals Error Self-Calibration Algorithm for Acoustic Vector Sensor Array

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Peng Wang ◽  
Yujun Kong ◽  
Mingxing Zhang
Keyword(s):  
Sensor Array ◽  
Doa Estimation ◽  
Acoustic Vector Sensor ◽  
Estimation Performance ◽  
Self Calibration ◽  
Phase Errors ◽  
Vector Sensor ◽  
Calibration Algorithm ◽  
The Impact ◽  
Vector Sensor Array

In this paper, the errors of acoustic vector sensor array are classified, the impact factor of each error for the array signal model is derived, and the influence of each type of error on the direction-of-arrival (DOA) estimation performance of the array is compared by Monte Carlo experiments. Converting the directional error and location error to amplitude and phase errors, the optimization model and error self-calibration algorithm for acoustic vector sensor array are proposed. The simulation experiments and field experiment data processing of MEMS vector sensor array show that the proposed self-calibration algorithm has good parameter estimation performance and certain engineering practicability.

An enhanced spatial smoothing algorithm for coherent signals DOA estimation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bingbing Qi ◽  
Dunge Liu
Keyword(s):  
Noise Suppression ◽  
Doa Estimation ◽  
Spatial Smoothing ◽  
Weak Correlation ◽  
Correlation Matrices ◽  
Space Correlation ◽  
Content Type ◽  
Estimation Performance ◽  
Low Snr ◽  
Time Space

PurposeThe existing dimensionality reduction algorithms suffer serious performance degradation under low signal-to-noise ratio (SNR) owing to the presence of noise. To address these problems, an enhanced spatial smoothing scheme is proposed that exploits the subarray time-space correlation matrices to reconstruct the data matrix to overcome this weakness. This method uses the strong correlation of signal and the weak correlation of noise in time and space domains, which improves the noise suppression ability.Design/methodology/approachIn this paper, an enhanced spatial smoothing method is proposed. By using the strong correlation of signal and the weak correlation of noise, the time-space smoothed array covariance matrix based on the subarray time-space correlation matrices is constructed to improve the noise suppression ability. Compared with the existing Toeplitz matrix reconstruction and spatial smoothing methods, the proposed method improves the DOA estimation performance at low SNR.FindingsTheoretical analysis and simulation results show that compared with the existing dimensionality reduction processing algorithms, the proposed method improves the DOA estimation performance in cases with a low SNR. Furthermore, in cases where the DOAs between the coherent sources are closely spaced and the snapshot number is low, our proposed method significantly improves the performance of the DOA estimation performance.Originality/valueThe proposed method improves the DOA estimation performance at low SNR. In particular, for the cases with a low SNR, the proposed method provides a better RMSE. The convergence of the proposed method is also faster than other methods for the low number of snapshots. Our analysis also confirms that in cases where the DOAs between the coherent sources are closely spaced, the proposed method achieves a much higher angular resolution than that of the other methods.

scholarly journals Experiment on Direction of Arrival Estimation of Atmospheric Spark Discharge Plasma Using Combined Time-Domain Antenna

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jiangzehui Yan ◽  
Luping Xu ◽  
Weihao Tie ◽  
Dan Jiang
Keyword(s):  
Time Domain ◽  
Discharge Plasma ◽  
Elevation Angle ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Spark Discharge ◽  
Passive Detection ◽  
Estimation Performance ◽  
Plasma Target ◽  
Spark Discharge Plasma

Apart from interfering in the communication system of an aircraft, electromagnetic pulses (EMPs) radiated from spark discharge plasma, which is generated during high-speed flight, can also be utilized in passive detection. In order to validate this idea, an experiment on direction of arrival (DOA) estimation of a spark discharge plasma target using its radiated EMPs is carried out in this paper. A combined time-domain antenna is designed based on the model of spark discharge process and is used to receive the radiated EMPs during the experiment. According to the experimental results, the DOA estimation system with combined antenna is able to obtain the direction information of a spark discharge plasma. Results also show that the estimation performance of elevation angle is better when the actual elevation angle of the discharge plasma target is higher, while the estimation performance of azimuth is opposite. The azimuth angle of a target has very little influence on the DOA estimation. Moreover, the estimation error can be reduced effectively by increasing the aperture size of receiving array. The previously mentioned results provide an approach to locate the discharge plasma source using radiated EMPs with passive detection techniques.

scholarly journals Logarithmic-Domain Array Interpolation for Improved Direction of Arrival Estimation in Automotive Radars

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2410 ◽  
Author(s):  
Seongwook Lee ◽  
Seong-Cheol Kim
Keyword(s):  
Measurement Data ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Transformation Matrix ◽  
High Angular Resolution ◽  
Automotive Radar ◽  
Actual Measurement ◽  
Estimation Performance ◽  
Radar Systems ◽  
Original Array

In automotive radar systems, a limited number of antenna elements are used to estimate the angle of the target. Therefore, array interpolation techniques can be used for direction of arrival (DOA) estimation to achieve high angular resolution. In general, to generate interpolated array elements from original array elements, the method of linear least squares (LLS) is used. When the LLS method is used, the amplitudes of the interpolated array elements may not be equivalent to those of the original array elements. In addition, through the transformation matrix obtained from the LLS method, the phases of the interpolated array elements are not precisely generated. Therefore, we propose an array transformation matrix that generates accurate phases for interpolated array elements to improve DOA estimation performance, while maintaining constant amplitudes of the array elements. Moreover, to enhance the effect of our interpolation method, a power calibration method for interpolated received signals is also proposed. Through the simulation, we confirm that the array interpolation accuracy and DOA estimation performance of the proposed method are improved compared to those of the conventional method. Moreover, the performance and effectiveness of our proposed method are also verified using data obtained from the commercial radar system. Because the proposed method exhibits better performance when applied to actual measurement data, it can be utilized in commercial automotive radar systems.

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