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.

scholarly journals DOA Estimation of Wideband Cyclic MUSIC Algorithm under Rayleigh Fading Environment in MIMO Systems

2020 ◽  
Vol 9 (1) ◽  
pp. 1834-1837
Keyword(s):  
High Resolution ◽  
Rayleigh Fading ◽  
Array Signal Processing ◽  
Mimo Systems ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Direction Of Arrival Estimation ◽  
Music Algorithm

Direction of arrival (DOA) estimation has, for quite some time been a challenging situation in most of the wireless communication applications, radar and sonar. The resolution of the direction of arrival estimation can be increased using the help of array signal processing. The performance of the direction of arrival estimation for multiple input multiple output(MIMO) radar systems has been reviewed for cyclic Multiple Signal Classification(MUSIC), extended cyclic MUSIC and Wideband cyclic MUSIC under Rayleigh fading environment. MUSIC and its variants have been taken into consideration for the analysis as these have been a very good parameter estimation technique due its low cost, high resolution and stability. Direction of Arrival estimation clubbed with cyclostationarity has been included into the new algorithm because of its immunity to noise and interference. The new algorithm along with cyclic correlations when applied to these signals, improves the performance of the entire system substantially. The performance of this wideband cyclic MUSIC high resolution direction of arrival estimation algorithm over the Rayleigh fading is analyzed in this paper. The simulation results citing the three methods show the performance of these methods in presence of the fading environments.

Direction of Arrival Estimation Under Near-Field Interference Using Matrix Filter

2015 ◽  
Vol 23 (04) ◽  
pp. 1540007 ◽  
Author(s):  
Guolong Liang ◽  
Wenbin Zhao ◽  
Zhan Fan
Keyword(s):  
Near Field ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Direction Of Arrival Estimation ◽  
Far Field ◽  
Data Simulation ◽  
Estimation Algorithms ◽  
Simulation Results

Direction of arrival (DOA) estimation is of great interest due to its wide applications in sonar, radar and many other areas. However, the near-field interference is always presented in the received data, which may result in degradation of DOA estimation. An approach which can suppress the near-field interference and preserve the far-field signal desired by using a spatial matrix filter is proposed in this paper and some typical DOA estimation algorithms are adjusted to match the filtered data. Simulation results show that the approach can improve capability of DOA estimation under near-field inference efficiently.

scholarly journals An Enhanced Smoothed L0-Norm Direction of Arrival Estimation Method Using Covariance Matrix

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4403
Author(s):  
Ji Woong Paik ◽  
Joon-Ho Lee ◽  
Wooyoung Hong
Keyword(s):  
Covariance Matrix ◽  
Phased Array ◽  
Null Space ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Direction Of Arrival Estimation ◽  
Improve Performance ◽  
Space Projection

An enhanced smoothed l0-norm algorithm for the passive phased array system, which uses the covariance matrix of the received signal, is proposed in this paper. The SL0 (smoothed l0-norm) algorithm is a fast compressive-sensing-based DOA (direction-of-arrival) estimation algorithm that uses a single snapshot from the received signal. In the conventional SL0 algorithm, there are limitations in the resolution and the DOA estimation performance, since a single sample is used. If multiple snapshots are used, the conventional SL0 algorithm can improve performance in terms of the DOA estimation. In this paper, a covariance-fitting-based SL0 algorithm is proposed to further reduce the number of optimization variables when using multiple snapshots of the received signal. A cost function and a new null-space projection term of the sparse recovery for the proposed scheme are presented. In order to verify the performance of the proposed algorithm, we present the simulation results and the experimental results based on the measured data.

scholarly journals Spectral Domain Sparse Representation for DOA Estimation of Signals with Large Dynamic Range

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5164
Author(s):  
Jacob Compaleo ◽  
Inder J. Gupta
Keyword(s):  
Sparse Representation ◽  
Antenna Array ◽  
Dynamic Range ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Single Step ◽  
Window Function ◽  
Spectral Domain ◽  
Low Sidelobe ◽  
Weighting Window

Recently, we proposed a Spectral Domain Sparse Representation (SDSR) approach for the direction-of-arrival estimation of signals incident to an antenna array. In the approach, sparse representation is applied to the conventional Bartlett spectra obtained from snapshots of the signals received by the antenna array to increase the direction-of-arrival (DOA) estimation resolution and accuracy. The conventional Bartlett spectra has limited dynamic range, meaning that one may not be able to identify the presence of weak signals in the presence of strong signals. This is because, in the conventional Bartlett spectra, uniform weighting (window) is applied to signals received by various antenna elements. Apodization can be used in the generation of Bartlett spectra to increase the dynamic range of the spectra. In Apodization, more than one window function is used to generate different portions of the spectra. In this paper, we extend the SDSR approach to include Bartlett spectra obtained with Apodization and to evaluate the performance of the extended SDSR approach. We compare its performance with a two-step SDSR approach and with an approach where Bartlett spectra is obtained using a low sidelobe window function. We show that an Apodization Bartlett-based SDSR approach leads to better performance with just single-step processing.

scholarly journals A Direction-of-Arrival Estimation Algorithm Based on Compressed Sensing and Density-Based Spatial Clustering and Its Application in Signal Processing of MEMS Vector Hydrophone

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2191
Author(s):  
Huichao Yan ◽  
Ting Chen ◽  
Peng Wang ◽  
Linmei Zhang ◽  
Rong Cheng ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Spatial Clustering ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Cluster Center ◽  
Direction Of Arrival Estimation ◽  
Vector Hydrophone ◽  
Regularization Parameters ◽  
Selection Of

Direction of arrival (DOA) estimation has always been a hot topic for researchers. The complex and changeable environment makes it very challenging to estimate the DOA in a small snapshot and strong noise environment. The direction-of-arrival estimation method based on compressed sensing (CS) is a new method proposed in recent years. It has received widespread attention because it can realize the direction-of-arrival estimation under small snapshots. However, this method will cause serious distortion in a strong noise environment. To solve this problem, this paper proposes a DOA estimation algorithm based on the principle of CS and density-based spatial clustering (DBSCAN). First of all, in order to make the estimation accuracy higher, this paper selects a signal reconstruction strategy based on the basis pursuit de-noising (BPDN). In response to the challenge of the selection of regularization parameters in this strategy, the power spectrum entropy is proposed to characterize the noise intensity of the signal, so as to provide reasonable suggestions for the selection of regularization parameters; Then, this paper finds out that the DOA estimation based on the principle of CS will get a denser estimation near the real angle under the condition of small snapshots through analysis, so it is proposed to use a DBSCAN method to process the above data to obtain the final DOA estimate; Finally, calculate the cluster center value of each cluster, the number of clusters is the number of signal sources, and the cluster center value is the final DOA estimate. The proposed method is applied to the simulation experiment and the micro electro mechanical system (MEMS) vector hydrophone lake test experiment, and they are proved that the proposed method can obtain good results of DOA estimation under the conditions of small snapshots and low signal-to-noise ratio (SNR).

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.

Direction of Arrival Estimation with Compact Array Antennas

2009 ◽  
pp. 201-216
Author(s):  
Eddy Taillefer ◽  
Jun Cheng ◽  
Takashi Ohira
Keyword(s):  
Single Port ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Signal Classification ◽  
Direction Of Arrival Estimation ◽  
Multiple Signal Classification ◽  
Array Antennas ◽  
Power Pattern ◽  
Parasitic Array ◽  
Compact Array

This chapter presents direction of arrival (DoA) estimation with a compact array antenna using methods based on reactance switching. The compact array is the single-port electronically steerable parasitic array radiator (Espar) antenna. The antenna beam pattern is controlled though parasitic elements loaded with reactances. DoA estimation using an Espar antenna is proposed with the power pattern cross correlation (PPCC), reactance-domain (RD) multiple signal classification (MUSIC), and, RD estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithms. The three methods exploit the reactance diversity provided by an Espar antenna to correlate different antenna output signals measured at different times and for different reactance values. The authors hope that this chapter allows the researchers to appreciate the issues that may be encountered in the implementation of direction-finding application with a single-port compact array like the Espar antenna.

scholarly journals A New Computing Paradigm for Off-Grid Direction of Arrival Estimation Using Compressive Sensing

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Hamid Ali Mirza ◽  
Laeeq Aslam ◽  
Muhammad Asif Zahoor Raja ◽  
Naveed Ishtiaq Chaudhary ◽  
Ijaz Mansoor Qureshi ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Fitness Function ◽  
Super Resolution ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Estimation Problem ◽  
Direction Of Arrival Estimation ◽  
Grid Refinement ◽  
Computing Paradigm ◽  
The Difference

In this paper, a method for solving grid mismatch or off-grid target is presented for direction of arrival (DOA) estimation problem using compressive sensing (CS) technique. Location of the sources are at few angles as compare to the entire angle domain, i.e., spatially sparse sources, and their location can be estimated using CS methods with ability of achieving super resolution and estimation with a smaller number of samples. Due to grid mismatch in CS techniques, the source energy is distributed among the adjacent grids. Therefore, a fitness function is introduced which is based on the difference of the source energy among the adjacent grids. This function provides the best discretization value for the grid through iterative grid refinement. The effectiveness of the proposed scheme is verified through extensive simulations for different number of sources.

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.

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