scholarly journals Low-Complexity Aggregation Techniques for DOA Estimation over Wide-RF Bandwidths

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1707
Author(s):  
Ronald Mulinde ◽  
Mayank Kaushik ◽  
Manik Attygalle ◽  
Syed Mahfuzul Aziz
Keyword(s):  
Real Time ◽  
Mean Squared Error ◽  
Doa Estimation ◽  
Low Complexity ◽  
Spatial Covariance ◽  
Electronic Warfare ◽  
Estimation Performance ◽  
Narrowband Signal ◽  
Aggregation Techniques ◽  
Aggregation Technique

Accurate direction of arrival (DOA) estimation of wideband, low-power nonstationary signals is important in many radio frequency (RF) applications. This article analyses the performance of two incoherent aggregation techniques for the DOA estimation of high chirp-rate linear frequency modulated (LFM) signals used in modern radar and electronic warfare (EW) applications. The aim is to determine suitable aggregation techniques for blind DOA estimation for real-time implementation with a frequency channelised signal. The first technique calculates a single pseudospectrum by directly combining the spatial covariance matrices from each of the frequency bins. The second technique first calculates the spatial pseudospectra from the spatial covariance matrix (SCM) from each frequency bin and then combines the spatial pseudospectra into one single estimate. Firstly, for single and multiple signal emitters, we compare the DOA estimation performance of incoherent SCM-based aggregation with that of the incoherent spatial pseudospectra-based aggregation using the root mean-squared error (RMSE). Secondly, we determine the types of signals and conditions for which these incoherent aggregation techniques are more suited. We demonstrate that the low-complexity SCM-based aggregation technique can achieve relatively good estimation performance compared to the pseudospectra-based aggregation technique for multiple narrowband signal detection. However, pseudospectra aggregation is better suited for single wideband emitter detection. Both the incoherent aggregation techniques presented in this article offer a computational advantage over the coherent processing techniques and hence are better suited for real-time implementation.

scholarly journals A High-Resolution and Low-Complexity DOA Estimation Method with Unfolded Coprime Linear Arrays

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 218 ◽  
Author(s):  
Wei He ◽  
Xiao Yang ◽  
Yide Wang
Keyword(s):  
Computational Complexity ◽  
Degrees Of Freedom ◽  
Estimation Method ◽  
Doa Estimation ◽  
Low Complexity ◽  
Covariance Matrices ◽  
Linear Arrays ◽  
Estimation Performance ◽  
Uniform Property ◽  
Finding Method

The direction-of-arrivals (DOA) estimation with an unfolded coprime linear array (UCLA) has been investigated because of its large aperture and full degrees of freedom (DOFs). The existing method suffers from low resolution and high computational complexity due to the loss of the uniform property and the step of exhaustive peak searching. In this paper, an improved DOA estimation method for a UCLA is proposed. To exploit the uniform property of the subarrays, the diagonal elements of the two self-covariance matrices are averaged to enhance the accuracy of the estimated covariance matrices and therefore the estimation performance. Besides, instead of the exhaustive peak searching, the polynomial roots finding method is used to reduce the complexity. Compared with the existing method, the proposed method can achieve higher resolution and better estimation performance with lower computational complexity.

scholarly journals Optimization of array geometry for direction-of-arrival estimation using a priori information

2010 ◽  
Vol 8 ◽  
pp. 87-94 ◽  
Author(s):  
O. Lange ◽  
B. Yang
Keyword(s):  
Mean Squared Error ◽  
A Priori ◽  
Geometry Optimization ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Beam Pattern ◽  
Side Lobe Level ◽  
Estimation Errors ◽  
Estimation Performance ◽  
Array Geometry

Abstract. This paper focuses on the estimation of the direction-of-arrival (DOA) of signals impinging on a sensor array. A novel method of array geometry optimization is presented that improves the DOA estimation performance compared to the standard uniform linear array (ULA) with half wavelength element spacing. Typically, array optimization only affects the beam pattern of a specific steering direction. In this work, the proposed objective function incorporates, on the one hand, a priori knowledge about the signal's DOA in terms of a probability density function. By this means, the array can be adjusted to external conditions. On the other hand, a modified beam pattern expression that is valid for all possible signal directions is taken into account. By controlling the side lobe level and the beam width of this new function, DOA ambiguities, which lead to large DOA estimation errors, can be avoided. In addition, the DOA fine error variance is minimized. Using a globally convergent evolution strategy, the geometry optimization provides array geometries that significantly outperform the standard ULA with respect to DOA estimation performance. To show the quality of the algorithm, four optimum geometries are presented. Their DOA mean squared error is evaluated using the well known deterministic Maximum Likelihood estimator and compared to the standard ULA and theoretical lower bounds.

A Unitary-UCA-Root-MUSIC Algorithm Based on MSWF

2014 ◽  
Vol 610 ◽  
pp. 339-344
Author(s):  
Qiang Guo ◽  
Yun Fei An
Keyword(s):  
Computer Simulation ◽  
Computational Complexity ◽  
Real Time ◽  
Unitary Transformation ◽  
Wiener Filter ◽  
Doa Estimation ◽  
Signal Subspace ◽  
Music Algorithm ◽  
Multi Stage ◽  
Simulation Results

A UCA-Root-MUSIC algorithm for direction-of-arrival (DOA) estimation is proposed in this paper which is based on UCA-RB-MUSIC [1]. The method utilizes not only a unitary transformation matrix different from UCA-RB-MUSIC but also the multi-stage Wiener filter (MSWF) to estimate the signal subspace and the number of sources, so that the new method has lower computational complexity and is more conducive to the real-time implementation. The computer simulation results demonstrate the improvement with the proposed method.

Slip-Clutch Torque Estimation via Real-Time Adaptive Lookup Table

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Wenpeng Wei ◽  
Hussein Dourra ◽  
Guoming Zhu
Keyword(s):  
Friction Coefficient ◽  
Real Time ◽  
Mean Squared Error ◽  
Surface Friction ◽  
Lookup Table ◽  
Recursive Least Squares ◽  
Time Varying ◽  
Squared Error ◽  
Output Torque ◽  
Clutch Torque

Abstract Transfer case clutch is crucial in determining traction torque distribution between front and rear tires for four-wheel-drive (4WD) vehicles. Estimating time-varying clutch surface friction coefficient is critical for traction torque control since it is proportional to the clutch output torque. As a result, this paper proposes a real-time adaptive lookup table strategy to provide the time-varying clutch surface friction coefficient. Specifically, the clutch-parameter-dependent (such as clutch output torque and clutch touchpoint distance) friction coefficient is first estimated with available low-cost vehicle sensors (such as wheel speed and vehicle acceleration); and then a clutch-parameter-independent approach is developed for clutch friction coefficient through a one-dimensional lookup table. The table nodes are adaptively updated based on a fast recursive least-squares (RLS) algorithm. Furthermore, the effectiveness of adaptive lookup table is demonstrated by comparing the estimated clutch torque from adaptive lookup table with that estimated from vehicle dynamics, which achieves 14.8 Nm absolute mean squared error (AMSE) and 2.66% relative mean squared error (RMSE).

scholarly journals Two-Step Root-MUSIC for Direction of Arrival Estimation without EVD/SVD Computation

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Feng-Gang Yan ◽  
Shuai Liu ◽  
Jun Wang ◽  
Ming Jin
Keyword(s):  
Super Resolution ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Low Complexity ◽  
Music Algorithm ◽  
Correlation Matrices ◽  
Noise Subspace ◽  
Using Data ◽  
Eigen Decomposition ◽  
Value Decomposition

Most popular techniques for super-resolution direction of arrival (DOA) estimation rely on an eigen-decomposition (EVD) or a singular value decomposition (SVD) computation to determine the signal/noise subspace, which is computationally expensive for real-time applications. A two-step root multiple signal classification (TS-root-MUSIC) algorithm is proposed to avoid the complex EVD/SVD computation using a uniform linear array (ULA) based on a mild assumption that the number of signals is less than half that of sensors. The ULA is divided into two subarrays, and three noise-free cross-correlation matrices are constructed using data collected by the two subarrays. A low-complexity linear operation is derived to obtain a rough noise subspace for a first-step DOA estimate. The performance is further enhanced in the second step by using the first-step result to renew the previous estimated noise subspace with a slightly increased complexity. The new technique can provide close root mean square error (RMSE) performance to root-MUSIC with reduced computational burden, which are verified by numerical simulations.

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.

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